The paper is structured as follows. First, the theoretical basis is set by introducing the concept of public value. Next, the concept of IoT and its role in public sector will be introduced. In the following parts, the research design and findings are presented. This will be followed by a discussion of findings and a conclusion.

#### 2 Theoretical Background

This section introduces the concept of public value, which is regarded as one of the main reasons for public sector innovation. The following section introduces the concept of IoT and discuss its importance in generating public value.

#### 2.1 Public Value Perspective

Public entities such as governments, municipalities and county councils are constantly seeking to address contemporary challenges and opportunities through utilizing emerging technologies [29]. While there are many motivators for adopting IT solutions, a primary goal of IT initiatives is to create and deliver public value [18]. Public value focuses on governmentally produced benefits that serve the interests of stakeholders both inside and outside the public organization [14]. The creation of public value entails balancing competing public interests through emphasizing collective preferences and expectations of government, citizens and other stakeholders who consume the services [5, 27]. Consequently, public sector bases its decisions to implement IT on the expected benefits and conflicting demands of various stakeholders [30].

It is widely acknowledged that IT creates public value. A successful IT implementation is believed to drastically change how governmental organizations operate, bringing forth substantial organizational, technical, business and societal benefits [10, 20]. IT-based value manifests itself both in terms of economic values e.g. operational efficiency, and non-economic values e.g. trust in government and sustainability [29]. For the associated stakeholders, e.g. citizens, users, public administrators and politicians, the public value can manifest differently, e.g. administrative personnel and managers from public sector might place high value on accountability, c.f. [10], while citizens might value accessibility to governmental information [38]. Harrison et al. [14] divided public value into seven categories, namely:

The incorporation of public value in the discussion of public sector investment in IT is especially relevant as it allows to monitor the outcome of government investment and understand the relationship between IT and public value delivery [36]. Understanding the relationship between IT and public value creation can assist public organizations in the pursuit of suitable use of technology to benefit society and secure the cost efficiency of public value creation [3].

#### 2.2 Role of the Internet of Things

The IoT has rapidly gained presence and is sometimes regarded as the most disruptive phase of the Internet revolution [2, 34]. IoT is ubiquitous by nature and is present in different almost identical concepts, such as "Internet of Everything", "ubiquitous computing", "pervasive computing" and "ambient intelligence", whereas the differences between the terms are of academic nature [9]. IoT is a general term used for objects interconnected through networks, that encompass processing and sensor capabilities, allowing the devices to transmit recorded information from the outside environment. IoT allows information, resources, "things", e.g. sensors, beacons, actuators, mobile phones etc., to interact with each other and cooperate with their smart components to reach common goals [15]. Realizing the value of IoT requires integration of IT infrastructures and information services - such as RFID tags, wireless broadband and geographic information systems [33]. Sensory data must be gathered from distributed smart objects and be transmitted using a communication infrastructure, which can encompass both wired and wireless communication technologies [32]. IoT allows to link real-life objects with the virtual world - providing anytime, anyplace connectivity for anything [34].

These capabilities can be applied to everyday objects, thus affecting all areas of life [9]. IoT will change the way we collect, analyse, and respond to data, creating opportunities for individuals, governments, and businesses to develop new business models and forms of interaction that take advantage of ubiquitous computing power embedded in objects [13]. While the majority of IoT initiatives have been implemented in the private sector, studies have indicated that the importance of IoT in public sector is increasing [4, 12]. This is driven by the rapid development of IT, and expanding efforts by national and local governments to change how they operate. Local governments are increasingly turning towards new information systems, often utilizing IoT, to develop livable, economically sustainable and efficient living environments. IoT is implemented in various settings from cities to rural areas. Utilizing IoT devices allows local governments to monitor and take immediate action on almost every aspect of urban and rural space, and provide citizens with relevant information and services [33, 35]. Among others, IoT can be used to create new and enhance existing services, improve efficiency and effectiveness of internal management and service delivery, and foster collaboration with different stakeholders [9, 22]. Due to the dynamic nature of public sector, the requirements for IoT devices can vary significantly. The IoT devices that can swiftly and accurately transmit information can be crucial for winter road maintenance, while speed would not be a requirement for water consumption monitoring.

In the context of public sector, IoT has the potential to improve several areas, including: healthcare, education, utilities, infrastructure, buildings, environment and culture. With regards to this, IoT becomes pivotal in streamlining governmental processes and engaging citizens in all areas of local governance. However, to date, little is known on how IoT generates public value. Consequently, there also lacks knowledge on how local governments could benefit from implementing IoT.

#### 3 Public Value Framework

For the study in hand, it was considered suitable to use the six public value generating mechanisms proposed by Harrison et al. [14] to understand what public value IoT creates in public sector (See Table 1). Harrison et al. [14] created the set of value generators to specify how public value is created, i.e. which government action lead to the creation of public value. For analysis, the six public value mechanisms proposed by Harrison et al. [14] were used for guidance. To investigate the relationship between IoT and public value generating mechanisms, a public value framework was created to assess the IoT initiatives and to distinguish between the outcomes (See Table 1). Based on Nam and Pardo [28] the evaluation dimensions were presented from the external view (interactions with citizens, private companies and other relevant non-governmental actors) and from the internal view (interactions within the local government). Identifying and measuring the public value of IoT is complicated as different stakeholders may have different attitudes on what is regarded a successful outcome of an IT initiative [37]. As a result, public value creation was studied from the perspective of public organizations. Similarly to this study, most research on IT value studies the outcome of past IT investments through a post hoc analysis [21]. As the study is concerned with how IoT solutions add value to local governments and public, broader economic factors are not considered unless they directly relate to governmental or public impact.

Table 1. Public value framework.

#### 4 Research Design

To understand how IoT creates public value, a qualitative study was carried out on the use of IoT in Estonian municipalities. The following section provides information on the research domain, data collection and analysis.

#### 4.1 Data Collection

To paint a thorough picture of the use of IoT in public sector, the research concentrated on the use of IoT in Estonian municipalities. The data was collected between March and December 2017 through semi-structured interviews and supporting documentation reviews.

The research participants were found by first contacting all 202 municipalities in Estonia via e-mail regarding the use of IoT in public sector, the municipalities that had implemented IoT solutions were included to the study and the municipalities who were in the planning phase of IoT implementation or who had not implemented IoT solutions were excluded. Thus, ex ante descriptions of the predicted outcome of IoT were not included in the study. In total 81 municipalities replied, of whom 46 were included in the study. From those 46 municipalities, 67 participants were contacted and interviewed regarding the implemented IoT solutions. In some municipalities, more than one public official was interviewed due to a lack of involvement of the interviewee with other IoT solutions.

The interviews were conducted in person where possible, and over the phone in other circumstances. An interview guide was used to determine the course of the interviews, which included open-ended questions that allowed to include topics that were not predetermined by the interview guide. The type of questions in the interview guide included the type of solutions implemented, the outcomes of the solutions, collaboration with other stakeholders, the role of IoT, measurement techniques for the outcomes etc. In Table 2 an overview of the interview questions is presented. The interview guides were iteratively changed, according to the research conducted prior to the interviews - publicly available information on each municipality was gathered to determine the type of solutions introduced.

The interviews lasted between 20 and 120 min, with an average of 57 min. All interviews were audio recorded and later transcribed. The participants had been using IoT solutions from five months to five years - providing enough time to realize the value of IoT initiatives [24]. A summary of all interviews will be presented in Table 3.

Table 3. Summary of interviews.

Supporting documentation such as procurement documents, public documents, and technical files were collected from governmental databases and online sources, e.g. municipal websites and from technology providers. Technical documentation was attained on both the system and the device where possible. Procurement documents which allowed to better understand the reasons and granularities of the implementation of any device were studied where possible. Public documents used included governmental reports, public statements etc. Secondary data sources were used to prepare for interviews, map government priorities etc. A summary of all additional procedures for data collection are presented in Table 4.

#### 4.2 Data Analysis

To analyse the data, the recommended procedures for qualitative research were followed to guide through the three steps of coding [6, 26]. During the first round of coding an initial coding scheme was developed. As the research was concerned with identifying what public value IoT generates, the coding scheme was based on public value framework (See Table 1). During the second round of coding, examples of IoT generated public value were identified. As the study focused on how IoT has generated public value for the municipalities, the perspective of citizens and other stakeholders were considered through municipal perspective. The different dimensions were recorded altogether 2644 times. From the mentioned segments, 1481 concentrated on the external view. The remaining segments were divided between the internal dimensions. A summary of coded segments can be seen in Table 5.

Table 5. Summary of coded segments.

The third round of coding compared previously coded segments to summarize shared features and variance within and across research sites. Multiple data sources allowed to compare, contrast and triangulate data [26]. ATLAS.ti was used throughout the study for data analysis. Findings are presented in the following section.

## 5 Empirical Analysis

In recent years, IoT has gained traction in public discourse as IoT solutions are increasingly implemented. This research concentrates on the use of IoT in Estonian municipalities - studying the public value created by these solutions.

#### 5.1 Mapping the Estonian IoT Solutions

IoT solutions can be implemented for virtually any purpose, however the study identified some core areas in which the implementation of IoT was more common, namely buildings and transportation. In total, 158 IoT solutions were implemented, from those, the study identified 30 different IoT solution types. Table 6 will present an overview of the implemented solutions and the number of municipalities that implemented those.

Table 6. Summary of IoT solutions.

(continued)

Table 7. Number of solutions with identified public value.

Table 7 presents a summary of solutions where public value was identified. A solution could simultaneously provide public value in many dimensions, for example there were 75 solutions which generated efficiency, from those solutions, 68 produced public value for management and 36 solutions produced public value in service delivery.

The following part presents how IoT impacted the six public value mechanisms, drawing on specific IoT implementation examples for illustration, see Table 6.

#### 5.2 Efficiency

The study identified that 64.5% of solutions (102 solutions) had efficiency as their primary desired outcome. From all solutions, 47.5% of solutions (75 solutions) identified efficiency as an achieved outcome. While reducing costs was the main consideration during the procurement, it was difficult for municipalities to isolate the effects and measure the outcome. Consequently, improvements in efficiency were visible in terms of productivity, most commonly through reduced time and improved communication. The devices allowed municipal employees to work everywhere at any time without physically being present. This allowed to get a better and timelier overview of work processes, coordinate and reduce the workload. The devices allowed to verify whether service contractors and systems worked as needed. Through automatic analyzation it was possible to identify deviations that would have otherwise remained unnoticed. Even though IoT in some instances increased the hourly rate of work, the number of hours spent decreased. For instance, the fleet telematics enabled maintenance managers to re-route optimal vehicles, better maintain roads, and reduce the time vehicles were standing idle. Commonly implemented solutions included fleet telematics which helped to resolve disputes among stakeholders and reduced strain on municipalities to provide timely information to citizens. At the forefront of cost cutting were solutions related to energy consumption reduction, however the use of motion and CO2 sensors were difficult to utilize to their fullest potential due to the human element which interfered with the automatization, i.e. people opened windows instead of allowing the ventilation system to operate independently. To counter the human element, various approaches were taken, from employee training to changes in the physical environment, i.e. making it impossible to open windows. Still, cost efficiency remained difficult to achieve and often even harder to measure due to the changing environmental elements, e.g. changing weather conditions made it hard to compare changes on yearly basis.

#### 5.3 Effectiveness

Improved effectiveness was the most commonly achieved outcome from the use of IoT, identified as an achieved outcome in 59.5% of solutions (94 solutions). Although the implementation of IoT systems commonly had a goal to gain efficiency, especially in financial terms, most highly valued outcomes identified by the municipalities were derived from improved effectiveness. The devices allowed public officials to reduce unnecessary work (avoid personal interaction, provide easier access to information, allow for increased control, and offer convenience), and improve decision making and work outcomes (services could be personalized and provided in timely manner, investments were more targeted). Data provided by the sensors allowed to evaluate the work of devices, e.g. heating systems and private contractors, e.g. garbage disposal providers. In case of deviations, municipalities were notified of the changes. This allowed to improve the quality of service delivery and through that reduce costs as it took less time to achieve the intended outcome. Controversially, the devices often proved to be more efficient in identifying the shortcomings in effectiveness rather than aiding in improving them. For example, the pothole identifying system allowed to map the problematic roads, however the information did not necessarily lead to improvements as the processes surrounding the road management were not improved to reflect the capabilities of the data generated by IoT. As an example, a municipality used the system to identify potholes, but made no changes to organizational operation and road management strategy.

#### 5.4 Intrinsic Enhancements

Intrinsic enhancements were identified as the least important outcome from the use of IoT, only identified in 12% of solutions (19 solutions) as an achieved outcome. In majority of cases intrinsic enhancements were not considered during the implementation, but rather emerged as unexpected outcomes during the use of the devices. Intrinsic enhancements considered here, i.e. less redundant work practices regarding communication, were by nature silent outcomes, as they were experienced by all, but the highest improvements would be expected at service level and for consumers, but not necessarily by service providers - in this case municipalities. This in turn contributed to the low priority of intrinsic enhancements.

#### 5.5 Transparency

Improved transparency was identified as an achieved goal of IoT in 42.4% of solutions (67 solutions). IoT allowed municipalities and their employees to defend their decision making, overcome false accusations (reduce the impact of political loyalty), and helped to avoid unethical and unlawful actions, i.e. provide equal treatment for all stakeholders and protect individual rights. Through making information available for the public, municipalities could reduce their workload, make more calculated decisions, foster trust and accountability, and receive valuable feedback on their services. When citizens demanded information, the public official could rely on IoT generated data to provide up-to-date information. Municipalities could further control whether service contractors worked in an agreed upon amount and according to contracts - helping to contribute to reliability and stability of service provision. This allowed to avoid conflicts between the municipality and the service contractors, paved the way towards a trusting relationship and enhanced cooperation. However, transparency remained the most controversial outcome of IoT implementation, as improvements in transparency generated countless unforeseen difficulties that resulted in reversing the improved work practices to the previous mode of operation. To illustrate, citizens started to ambush winter road maintenance vehicles when the information of the vehicle movement was made available online. This resulted in an overall stigma for improvements in transparency, as failures in municipal management could have given a political disadvantage in upcoming elections, or provided a competitive advantage for neighboring municipalities.

#### 5.6 Participation

Improved participation was identified as an outcome of IoT implementation in 20.3% of solutions (32 solutions). Improved participation was never the primary intent of IoT implementation, however municipalities experienced improved participation both from local government and from external stakeholders. From the improvements in the dimension of participation, 24 solutions identified improvements on an internal level, improvements on an external level were visible in 17 solutions. However, silos existed whereby internal actors from different departments were not invested in the improvement of government operation even if capabilities to support it were created. Circumstances where internal stakeholders were involved consisted of instances where the system directly affected them in terms of their private or working life. While external stakeholders were more highly interested in decision making and in the operation of government, it was generally not welcomed by the government officials. Instead the generated data was used to support the sole decisions of managers involved with the IoT implementation, leaving out the external actors - mistrust and uncooperative behaviors remained wide-spread.

#### 5.7 Collaboration

Improved collaboration was identified as an achieved outcome in 28.5% of solutions (45 solutions). Collaboration was wide-spread both internally and externally. However, in majority of cases, collaboration was required for IoT implementation, but not necessarily fostered it. For instance, vehicle surveillance systems required police involvement due to the data protection acts. Collaboration was often present before the implementation of IoT, however IoT greatly enhanced the level of involvement and supported influx of more relevant information. For instance, when municipalities noticed that the quality of work was consistently worse for some drivers, e.g. garbage removal, they notified the private contractors, which allowed to improve the service delivery. Due to the availability of information, collaboration emerged between public officials and private contractors. For instance, when a technician previously had to be present to fix a biomass heating system, then following IoT implementation public officials took increasingly care of the maintenance. Similarly, when there were problems with private contractors' work, public officials directly contacted the people responsible. IoT provided capabilities that made municipalities adopt a more active role in service delivery. Indeed, municipalities had started to consider providing services without public-private collaboration. For instance, in winter road maintenance IoT created a situation where the public official became more actively invested in the maintenance than the private maintenance manager. Citizens on the other hand were involved in government operation through actively evaluating the available information, notifying of shortcomings. When services did not meet citizen expectations or citizens needed a custom service, they contacted for improvements. While citizens were involved, their participation in the governance was not wide-spread. With regards to this, citizens had limited impact, i.e. did not contribute, on the functioning of government.

#### 6 Discussion

Previous research indicates that efficiency is typically the primary goal of IT initiatives [11], which also holds proof here. However, the findings suggest that public organizations are increasingly focusing their attention on other dimensions besides efficiency, most commonly on effectiveness and transparency. This goes against the typical notion of IoT which is mainly articulated in terms of economic value [31]. Municipalities included in the study often considered other dimensions as the most sought after, neglecting efficiency as an important outcome even if an element of efficiency, e.g. time or cost, was stated as the primary implementation goal. By contrast other elements, most notably effectiveness and transparency, were increasingly derived as outcomes of these government initiatives. With regards to this, IoT allows to create public value beyond efficiency.

The findings suggest that while collaboration, transparency and participation generated public value, they should not be viewed as an administrative goal [14]. The opposing needs of stakeholders could create disturbances that could negatively affect the effectiveness and efficiency of public organizations. Harrison et al. [14] argued that transparency, participation and collaboration should be viewed as means toward desirable ends. The findings of this study suggest that without clear goal, public organizations should refrain from making information publically available. Controlled secrecy in some instances could prove more beneficial for deriving public value. For instance, available information was used to interfere with service provision and how the system operated, e.g. stopping winter road maintenance vehicles or opening windows to regulate temperature. Hence, each case should be closely evaluated to determine whether external and/or internal actors should be engaged in the operation of government. While it is easier to continue in secrecy, transparency and collaboration had a strong effect on achieving public values from other dimensions, e.g. effectiveness and efficiency, which otherwise were not achieved. Thus, government initiatives should be directed to stakeholders that allow to achieve the optimum public value.

The study illustrates that while some solutions are more beneficial than others, deriving public value from IoT initiatives requires public organizations to use the generated data to change how they operate. While the most substantial changes require IoT technology, management and policy changes make the biggest difference in the derived outcome of IoT. Using smart garbage bins or fleet telematics without changing how public organizations operate brought forward positive changes, but substantial benefits, required a change in management and policy. Public organizations had to change how they operate through using the devices and generated data in a unique way. For instance, they could continue to use fleet telematics for surveillance of vehicles, however when incorporating the generated data in investments, decision making or fostering closer collaboration with private organizations, the most substantial benefits occurred. As a result, the findings suggest that most public organizations are handling post-implementation phase of IoT inadequately, most notably regarding what to do with the produced data.

The research indicates that public organizations are not evaluating the outcome of IoT implementation. Throughout the study, rather few organizations had performed a formal evaluation of an IoT initiative. Similarly, the study by Jones and Hughes [17] identified that public organizations tended to rely on an assessment whether the technology works, rather than considering the social impact or value of the technology. If the outcome was evaluated, it was done through the prism of efficiency in an abstract manner. Without the formal assessment, it becomes difficult for public organizations to demonstrate the public value of IoT. Relying on abstract measurement systems, such as the number of complaints by citizens, could be a viable predictor of the success of an initiative, but it neglects the more substantial outcomes the technology could produce. There thus remains a need to explore how IoT created public value can be measured and is measured by public organizations. As the previous findings suggest, other aspects besides efficiency are gaining traction in IoT generated public value, the study concludes that the analysis of IoT should also consider other value generators besides efficiency when evaluating the solution. Evaluation and presentation of the different forms of public value generated by IoT could allow public organizations to avoid public scrutiny and improve the utilization of IoT. Especially, as many IoT projects were sealed off from external stakeholders due to the fear of criticism and political harm.

#### 7 Conclusion

To conclude, IoT has the capability to create public value and the intended public value dimensions have widened from the goal of improved efficiency. There has been a shift in value creation with other goals and outcomes such as effectiveness, transparency and collaboration gaining increased presence. Furthermore, the data suggest that the evaluation of IoT remains largely insufficient, and is mostly done abstractly or through financial metrics alone, which inhibits capturing the full potential of IoT. Public value derivation cannot rest on the implementation of IoT solutions alone, but must include improvements in management and policy according to the ample data generated by the devices.

Finally, there are limitations to be acknowledged. First, the research studied public value through the perspective of public organizations, however public values are rarely identical for stakeholders. Thus, additional research is required to study public value from the perspective of stakeholders like citizens and private organizations. Secondly, evaluating public value is never an easy task and more longitudinal studies could offer further in-depth understanding of public value created by IoT.

#### References

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## Strategies for Reducing Power Consumption and Increasing Reliability in IoT

Ricardo Reis(&)

Instituto de Informática – Universidade Federal do Rio Grande do Sul (UFRGS), Caixa Postal 15.064, 91501-970 Porto Alegre, RS, Brazil<EMAIL_ADDRESS>
Abstract. The Internet of Things (IoT) demands new challenges in the design of computing and electronics components. One of the challenges is the power reduction of this expanding network of connected devices, where the majority is permanently connected. In a large set of applications, another significant issue is reliability, especially on critical areas as health and transport. This paper shows an overview of design strategies that we have developed to reduce power consumption and to increase reliability in circuits that are components of the IoT, as the reduction of the number of transistors in IoT devices, using optimisation techniques and the physical design of circuits tolerant to radiation effects.

Keywords: Internet-of-Things Optimization Physical design Fault tolerance Radiation effects Nanoelectronics

#### 1 Introduction

The growing number of connected devices in the Internet of Things (IoT) is one of the reasons for the ever increasing increase in the number of transistors produced annually in the world. Figure 1, based on (SIA 2005), shows the number of transistors manufactured annually in the world, year by year. This impressive growth is due to 3 main factors: the increasing number of transistors integrated into a chip, the growing number of products that include embedded chips and the increasing number of manufactured copies of each product. The manufacturing cost of a transistor is relatively cheap. In (The Economist 2010) a comparison is presented between the cost of a grain of rice and the cost of a transistor. The price of a rice grain can be equivalent to the manufacturing cost of more than 125,000 transistors. This would indicate that there is no need to optimise the number of transistors in a design, since the cost of them is relatively small. But the cost of energy required for the operation of a transistor is increasing a lot. We also have to consider that a high-power consumption can reduce the lifetime of a system, as well as increase the effects of variability that can cause an integrated system to malfunction and/or also reduce its useful life. With the increasing connection of electronic and computational devices on the Internet, that is, in the Internet of Things, power consumption problems tend to get worse, and a lot. How much Power Plants we will need to cope with the IoT/IoE (Internet of Everything) world? This is a major issue.

So, an essential keyword on the Internet of Things is optimisation, especially the optimisation of power consumption, which must be addressed at all levels of abstraction in the design flow of a computer or electronic system. The total power optimisation is a summation of the optimisation done at each level of design abstraction. So, sustainable computing requires optimisation at all design levels of a computer or electronic system design.

Fig. 1. Number of transistors produced annually in the world (adapted from SIA 2005)

#### 2 Internet of Things

The term Internet of Things has already given rise to several other associated terms, such as the Internet of Health (IoH), Internet of People (IoP) and the Internet of Everything (IoE). In fact, the latter term becomes the most comprehensive, but each one of the others has some specific characteristics. When talking about the Internet of Health, which includes real-time monitoring of a person's clinical conditions, as well as chips injected in a person, the issue of reliability is a key one. And reliability is also related to power consumption in most cases. High power consumption can reduce the lifetime of a system. When it comes to the Internet of People, the issue of people's security and privacy is of great relevance. But in all cases, the importance of optimising energy consumption is growing more and more.

When considering optimisation, it means that integrated systems must increasingly be dedicated to the intended application to optimise the number of components, that means the number of transistors. Another important strategy for optimisation is the hardware and software codesign, where one can manage the compromise between performance, consumption, and reliability.

Devices connected to the Internet of Things (or the Internet of Everything), can have very different complexities. If it is analysed the complexity considering the number of components, we can find small devices with few transistors and large devices with billions of transistors. Of course, large devices will consume much more power, but we have to consider that most devices on the Internet of Things are devices with a low number of transistors. But, because they are found in large quantities, they can represent a total consumption more important than the consumption of the so-called large devices that are present in a lower number. Therefore, consumption optimisation must be performed on both large and small devices that are present in large quantities. Another aspect to consider is that some devices require the application of reliability techniques (such as those related to transport or health systems), which can increase the number of components, while other devices are not critical, such as a camera or video, where an error in viewing a pixel of an image does not cause significant problems.

Also, we can expect that many systems connected to the Internet of Everything will be Cyber Physical Systems (CPS), that are systems composed by different classes of components like electronic elements, mechanical elements, optical elements, physical sensors, chemical sensors, organic components, and many others. So, it is needed to obtain EDA tools to cope with the design of CPS composed of all these classes of devices.

Figure 2 (The Connectivist 2014) shows an estimate of the number of devices connected to the Internet since 1992 when they were about 1 million devices. By 2020 when it is estimated that there will be more than 50 billion devices connected in the network, and there are currently around 35 billion connected devices. In (IHS Markit 2018) the number of devices connected to the network in 2018 is shown by industrial and commercial sectors, where almost half is in the area of communication. The significant growth in the number of connected devices to the Internet has naturally led to a considerable increase in the energy consumed in the Internet of Things. For how long will we have the energy to meet this growing demand? Therefore, it is necessary to use techniques to minimise the energy consumption of each connected device in the Internet of Things.

The Internet of Health (IoH) is a significant way to increase the life of human beings but also to improve life quality. Some of the examples of devices to be connected to the IoH are: Glasses that can advise eye correction; Toothbrush that can find cavities and breath issues; Razor that identify acne; Pacemakers that broadcast data to cardiologist; Underwearables that can provide early detection of cancer and other anomalies; Combs that can scan for fungus and hair loss; Earphones that does measurement of hearing, analysis of emotional level; Watches able to measure parameters like blood pressure, heart rate and others.

In critical areas such as the design of implanted devices (chips) in humans (Fig. 3), the reliability of the implanted systems is obviously critical. Some of the techniques used are based on the triplication of circuits and the temporal analysis of the propagation of a signal. Previously, the design of fault-tolerant circuits, to cope with radiation effects, was mainly in circuits that were sent to space. With the reduction of the value of the supply voltage of integrated circuits, nowadays the integrated circuits for use at ground level are also sensitive to errors caused by the radiation incident on the earth. Therefore, in critical areas such as implanted chips in humans, it is necessary to implement radiation effects tolerance techniques (Velazco et al. 2007). Also, critical

Fig. 2. Number of devices connected on the Internet (adapted from The Connectivist 2014)

systems used on the Internet of Health should be tolerant to any kind of noise (internal or external to the human body). They also must have a larger lifetime as possible, for obvious reasons and also should cope with environmental variability.

Fig. 3. The implantation of Chip Systems in humans demands reliability and ultra-low consumption

Also, there is the effect of "ageing", that is, the ageing of the circuit, which is more eminent in nanometric technologies (Vasquez et al. 2012). One of the most important effects is known as NBTI (Negative Bias Temperature Instability) that alters the threshold voltage of the PMOS transistors, degrading the operation of the transistor. Another effect that causes failures in circuits throughout their life is the effect of electromigration, which can cause short circuits or rupture of connections (Fig. 4). In order to increase the lifetime of the chips, it is necessary to use physical design techniques that reduce the probability of electromigration (Posser et al. 2016, 2017).

Fig. 4. Exemplo of a Void (open circuit) and hillock (short circuit) (Geden 2011).

#### 3 Electronic Design Automation (EDA) Tools

The use of EDA tools is essential for optimising energy consumption and increasing reliability, as the design flow has a large set of steps as well the number of components of a cheap can reach billions off transistors. In Fig. 5 we can see the floorplan of an integrated circuit, where the hotter colours show regions (hot spots) with higher energy consumption, indicating that in some points there is a significant concentration of power consumption. One way to deal with the problem is to modify the placement of the logic cells in the circuit to distribute the cells with the highest energy consumption over the entire circuit area. But this must be done without compromising the area, wirelengh and operating frequency specifications (much depends on the routing). Another way is to decrease the number of transistors, since the static consumption is related to the number of transistors (Reis 2011A).

Fig. 5. View of the consumption density on a chip

#### 4 Power Consumption Reduction by Reducing the Number of Transistors

The reduction of the power consumption of a System on a Chip (SoC) is a function of a sum of techniques and strategies of design applied in different levels of abstraction in the design flow of an integrated system (Reis 2010). The summation of the gains is that it will set the total gain in power reduction. When we deal with the physical synthesis of a system on a chip, one technique is the optimisation of the number of components, that is, the number of transistors. In Fig. 6 (Reis 2011A) we can observe two solutions for the implementation of the same equation. The first solution makes use of 4 basic logic gates (3 NOR 2-input ports and one CMOS inverter), using a total of 14 transistors. The second solution makes use of only one logic gate, which performs the same function but with only 8 transistors. That is, the second solution, having a reduction in the number of transistors, will also have a proportionally smaller static power consumption. Furthermore, in the example of Fig. 6, we can see that the first solution also has 3 connections between the basic gates (and therefore even vias and contacts) that are eliminated in the second option with only one logic gate.

This elimination of connections is increasingly important because it decreases the number of connections to be implemented using the different metal layers. The decrease in the number of connections decreases the density of connections and, therefore, increases the routability of the circuit and also contributes to reduce the average length of the connections, which implies in a reduction of the delay. In modern technologies, the delay in connections is so or more significant than the delay in the switching of logic gates. A greater spacing between the connections also contributes to an increase of reliability, due, for example, to the reduction of the possibility of electromigration, as already mentioned above.

Fig. 6. Two options for the implementation of the same function (Reis 2011A)

The reduction of the number of transistors depends on the use of efficient Electronic Design Automation (EDA) tools that transform the logical equations of a system so that in addition to mapping equations in CMOS gates, make optimum use of complex logic gates. In (Conceição et al. 2017) we present a tool to reduce the number of transistors in a circuit through the fusion of networks of transistors that present fanout equal to 1. Also, it is fundamental the use of an automatic synthesis tool that can perform the automatic layout of any logical function. There is no use to achieve a logical optimisation if it is necessary to map (transform) the equations according to the logic gates available in a traditional cell library [which have few functions, in general, no more than 100 functions], as is still done when using traditional EDA systems. This mapping step is called technology mapping, and it represents a step of deoptimization. With this aim, we have developed automatic layout synthesis tools such as ASTRAN (Ziesemer and Reis 2015) (Fig. 7), which allows automatic generation of the layout of any network of transistors (Reis 2011A, B).

Another technique to reduce consumption is through the sizing of the transistors. Modern integrated circuit manufacturing technologies show a significant increase in static power consumption that is often greater than dynamic power consumption. One way to mitigate power consumption, especially the static one, is to carry out a sizing of transistors to optimise power consumption (Posser 2011). In (Reimann et al. 2016) significant decreases in consumption are obtained through the use of automatic transistor sizing tools. This is also called cell selection, where the cells are selected from a cell library. In this case, cell selection means the selection of cells with a specific size and Vth (threshold voltage). In traditional cell libraries, one function has in general 3 sizings (one for less area, one for less power, and one for less delay) and 3 Vth (threshold voltage).

Fig. 7. Transistor network layout generated automatically (Ziesemer and Reis 2015)

#### 5 Reliability

As in the reduction of power consumption, in the design of critical systems, it is needed to use techniques to increase reliability at different levels of design abstraction. At the architectural level, a very applied method is the redundancy of modules, especially triple module redundancy (TMR) (Kastensmidt et al. 2006). Another is the temporal redundancy (Nicolaidis 1999) where a signal traverses two paths, one with higher delay and another one with less delay. The difference of delay must be longer than the duration of a transient. Comparing the signal after traversing the two paths indicates whether there has been a transient propagation or not. At the physical level, we can apply different techniques to reduce or avoid problems such as electromigration (Posser et al. 2016, 2017). In the example of Fig. 8, the position of the output pin in the centre (point 4) increases the lifetime of the circuit because it allows reducing the maximum density of current in the segments of the metal layer.

In (Velazco et al. 2007) it is presented a series of works aimed at mitigating the effects of radiation on integrated circuits. In (Kastensmidt et al. 2006; Neuberger et al. 2014; Gennaro et al. 2017; Aguiar et al. 2016; Lazzari et al. 2011) we present some of the results that our research group has obtained in the development of techniques aiming the design tolerant to faults due to transients, as the effects due to radiation.

Fig. 8. Changing current density with the change of position of the output pin (Posser et al. 2016, 2017)

#### 6 Hardware Accelerators

The evolution of computer architectures, that today means, the evolution of microprocessor architectures has been very significant. In the 1970s, one marketing argument from microprocessor producers was the number of instructions that the microprocessor could execute as well as the clock frequency of the microprocessor. In the last decades, there has been a change of paradigm, discontinuing the race for the increase of the clock frequency, because the increment of the clock means an increase of the dynamic consumption. Instead, there was an increase in the number of cores (CPUs) aiming at increasing performance. Initially with homogeneous cores and later with heterogeneous cores.

Currently, we can find chips with multiple CPUs and several GPUs (as can be seen in Fig. 9 (Shao 2016) showing the floorplan of the A8 microprocessor (from Apple). In this same figure, it can be observed that about half of the area is occupied with hardware accelerators, which are modules dedicated to the execution of a specific function. For example, an encryption module placed next to the output/input pins and which will encode the output data and decode the received data. So, the execution of this function will be faster, because it is done by a dedicated module (that means smaller) and with only the needed number of components to perform that function. It also will consume less power.

A more important fact is that the use of hardware accelerators leads to greater energy efficiency (allowing more sustainable computing), mainly due to the reduction in the number of components used to perform a function. At any given time, only the hardware accelerators in use at that time are being powered. So, the hardware accelerators that are not in use are disconnected from the power supply. This strategy is also known as "Dark Silicon". We can even predict architectures consisting essentially of hardware accelerators, with only one or two small CPUs to manage these hardware accelerators.

Fig. 9. Apple 8 floorplan with 29 hardware accelerators (AnandTech 2014; Shao 2016)

The introduction of an NPU in A11 is another element characterising the heterogeneity of the SoC (chip system). And we can expect increasingly heterogeneous architectures, with dedicated modules for different operations to be performed by a SoC. In Fig. 10 (Techinsights 2017) the floorplan of the Apple A11 microprocessor is presented, where one of the modules is an NPU (Neural Processing Unit). NPU is mostly dedicated to facial recognition (Techinsights 2017), processing machine learning tasks more efficiently, consuming less energy than CPUs do. The CPUs occupy about 15% of the area of the chip and 6 GPUs occupy about 20% of the area. Most of the area is filled with the hardware accelerators. That is, it is growing in the architecture of Apple microprocessors the use of hardware accelerators.

Fig. 10. Apple 11 floorplan with an NPU (Techinsights 2017)

## 7 Conclusions

To have sustainable computing, when the number of connected devices in the Internet of Things is fast increasing, it is fundamental the design of devices optimised regarding energy consumption. Most of the chips produced today use much more transistors than necessary to perform a function. So, there is a significant space for the optimisation of the number of components. In many devices related to critical applications, the application of techniques for fault tolerance is also fundamental, as nowadays circuits at ground level can have faults due to radiation effects. The reduction of power consumption must be treated at all design abstraction levels in a synthesis flow of integrated systems, from the specification of them in high-level languages to the physical synthesis. It was presented several works that were developed to reduce the power consumption and increase the reliability of integrated systems on a chip, and more details are shown in the mentioned references. The keyword in the age of the Internet of Things is optimisation.

Acknowledgements. We thank CNPq, FINEP, Fapergs, and CAPES for financial support for the development of our team's work, as well as the master's and doctoral students of PGMICRO and PPGC and students of Scientific Initiation who have contributed to the research works that served as the basis for this paper.

#### References

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## An Internet of Things (IoT) Model for Optimising Downtime Management: A Smart Lighting Case Study

Brenda Scholtz1(&) , Mando Kapeso1 , and Jean-Paul Van Belle<sup>2</sup>

<sup>1</sup> Nelson Mandela University, Port Elizabeth, South Africa<EMAIL_ADDRESS><sup>2</sup> University of Cape Town, Cape Town, South Africa<EMAIL_ADDRESS>
Abstract. In today's global, competitive economy, downtime has been identified as a key performance indicator for field service organisations. The emergence of an Internet of Things (IoT) has brought new enhancement possibilities to various industries such as the manufacturing and field service industry. This paper provides a vision and motivation for using IoT in Field Service Management (FSM) in order to address data quality and service delivery issues. The theory of information quality was used to undergird the research and a model for the optimisation of downtime management in the field service industry using the IoT is proposed. The model was used to drive the design of a "proof of concept" prototype, the KapCha prototype. The paper also includes a report on an empirical study of the application of the proposed IoT model in FSM. The experiment findings showed that the prototype reduced the round trip delay time for sending and receiving data and was scalable. As a result, access to quality information supporting advanced data analytics and artificial intelligence was provided. Therefore, service technicians can be alerted more quickly as soon as any potential technical problems occur. In turn improved diagnostics and more efficient decision making can be achieved. The model and the lessons learned provide valuable guidance to other researchers and fill the gap in research of empirical studies conducted on IoT implementations.

Keywords: IoT Smart lightning Design science research

## 1 Introduction

The lack of accurate, available and real-time information is a common challenge faced by field service organisations [1]. As a result downtime, which is an important performance measure in this domain, is negatively impacted. Downtime is defined as the time between a customer's request for service and the completion of the service by the field service team to rectify the problem [2]. Field service management (FSM) refers to the support provided by hardware and software in the management of field service operations and involves the management of the activities and processes that are associated with field services. There is a need for solutions that efficiently address the challenges of FSM and downtime management and that support the provision of quality information and ultimately improved decision making and service delivery levels [3].

One application of downtime management in the field service domain is that of the maintenance of smart lights. Smart lighting projects have been undertaken by municipalities as a result of a drive for improved energy management within cities [4]. In the context of cities, streetlights are one of the most important assets to maintain as they provide safe roads and enhanced security for homes, businesses and city centres. However, they are costly to operate and account for an estimated 40% of the amount of electricity spent in an urban city [5]. To address this issue, city managers are implementing smart lighting solutions. Smart lighting consists of heterogeneous and multidisciplinary areas of lighting management, with the possibilities of integrating a wide range of sensory and control technologies with ICTs. This integration can improve efficiencies in lighting products and lower the negative impact derived from the use of energy for illumination. Smart lighting provides intelligent features and interfaces for lighting solutions in the ambient, commercial and public domains [4, 6]. Smart lighting is linked to the concept of a smart city, which is an urban development that envisions the efficient management of a cities resources and services with the use of integrated ICT solutions [4]. Smart cities play an important role in the sustainable economic development of countries or states seeking to attain environmental sustainability. Smart cities are made possible through the abundance of smart devices, smart objects and the emergence and rapid growth of technologies such as the Internet of Things (IoT). The IoT is described as a decentralised system of "smart" objects with sensing, processing, and network capabilities [7].

Extensive research has been conducted related to the IoT [16, 18, 26]. In particular several studies have proposed various architectures for IoT, such as a general reference architecture [27] and others in certain domains such as smart metering [28]. However, limited studies can be found that report on empirical studies of IoT applications in practice and findings and lessons learnt from these applications. There is a need for research into how technologies in the IoT can be applied to various business domains [20].

This paper addresses this gap by investigating an IoT application in the domain of smart lighting. The purpose of this paper is to propose an IoT model that addresses the challenges of information quality leading to poor downtime management. The paper reports on the application of this model in the smart lighting domain. The model includes IoT compatible technologies and techniques (protocols and formats) to support successful downtime management. To address this purpose, a critical analysis of the literature related to FSM, downtime management and IoT was conducted (Sect. 2). The context was a smart lighting organisation in South Africa (Sect. 3). From the literature and consideration of the context, a theoretical model was derived (Sect. 4). The model was used to design the architecture of and to implement a prototype for the case study (Sect. 5). The experiments conducted revealed that the new architecture and protocols implemented resulted in a lower Round Trip Delay time and was scalable (Sect. 6). The quality of information was improved and provided a foundation for advanced data analytics and artificial intelligence (AI), since the system provided intelligent information to technicians and managers; thereby improving diagnostic decision making, downtime management and service delivery.

There are several contributions and implications for future research that are identified from this study (Sect. 7). The practical contribution is the model, which can provide guidance to practitioners working in the field service domain and for system designers. On a theoretical level the model and the implementation issues identified contribute to the body of knowledge regarding the application of IoT models, architectures and network protocols.

#### 2 Literature Review

#### 2.1 Challenges in Field Service Management (FSM) and Downtime Management

In a competitive global economy where every organisation is looking at ways to cut costs, increase efficiency and gain a competitive advantage, organisations have become more customer-centric. The effectiveness of field services provided by technicians affects everything from the retention of customers and the profitability of the organisation [1, 8]. With field-based services, customers receive either an on-site or a remote service [2]. FSM operations include tracking vehicles, scheduling and dispatching employees, and integration of these operations with a back-office system for inventory, logistics and marketing. FSM includes elements such as Enterprise Asset Management (EAM), maintenance support, sensor networks, Radio Frequency Identification (RFID) tags, technical support, contract management and product life-cycle management. The FSM market has seen a steady growth and evolution in the last 10 years [9], which can be attributed to new technology developments, as technology is a driver in improved after-sales service innovations.

Downtime management is an important measure of performance for field services for both the organisation providing the service and the customer [10]. From the customer's perspective, that is the organisation undergoing downtime, the downtime period has operational implications such as reduced productivity levels and delayed delivery of services to the organisation's clientele. It is therefore imperative that downtime is kept to a minimal period. Service providers have to adequately manage downtime in order to satisfy its customers and by doing so efficiently they may gain a competitive advantage. Agnihothri [2] classified downtime into two subcategories, response time and on-site time. Response time is the time between the customer's request and the service team's arrival on-site. On-site time is the duration of time taken between the service team's arrival at the customer's site and the rectification of the problem. Corrective maintenance occurs when the machinery breaks down and includes activities undertaken to diagnose and rectify a fault so that the failed machine, equipment or system can be restored to its normal operational state, thus reducing the extent of downtime.

A lack of information related to a technical breakdown can result in longer cycle times and possibly a second service visit, thus resulting in longer periods of downtime for customers [8]. A malfunctioning piece of industrial machinery on a manufacturing floor can translate into tens of thousands of dollars per minute. It is important to make critical information immediately available to field technicians and management with high levels of accuracy. Critical data related to the problem must be accurate, available anywhere, and dynamically changing along with the day-to-day operations of field service teams. Access to this information can assist with optimising the problem detection step in FSM and field service providers can determine strategies to ensure that downtime is minimised and managed with optimal efficiency. Within IS literature, information quality (IQ) can be used as a dimension of IS success [12]. Knowledge is functionally related to data and information, thus it follows a hierarchy (data ! information ! knowledge), termed as the knowledge hierarchy [11]. Our study classified the problems in FSM related to information that impact downtime management according to six of the attributes of IQ proposed by [12]. These are:

This analysis also confirmed the findings of [13] showing a significant relationship between IQ and individual impact. Individual impact is measured in terms of decisionmaking performance, job effectiveness, and quality of work. Challenges faced by FSM organisations with regards to IQ resulted in negative impact on decision making and service delivery. Inaccurate or missing information and a lack of real-time availability of information to employees onsite in the field (for example dispatchers and service technicians) resulted in operational challenges [1, 3, 8]. Information related to the customer or the equipment under maintenance or repair is not always readily available to field service employees, resulting in the poor scheduling of field employees, the ineffective management of field service resources such as service parts [3] and ultimately in poor service delivery. In a study by Lehtonen [1], it was reported that service teams could not provide a service due to missing spare parts. The main reason for this was the inaccurate information on the spare parts that was taken to the client at the time of repair. Challenges in FSM within Enterprise Systems may also arise due to the lack of accessibility and integration of various systems [14]. For example, geographical data is found in Geographical Information Systems (GIS), whilst maintenance-related data and reports are often stored in an Enterprise Resource Planning (ERP) system, thus resulting in integration and consistency issues. Schneider [14] reported issues related to the use of aggregated data within an ERP system. For example, in an ERP system electricity usage data for a manufacturing plant is usually stored as an aggregated figure for all work centres within the plant. Aggregated data makes the operational performance monitoring of a single work centre or equipment within the plant difficult.

Access to real-time information aids organisations in optimising FSM since it can minimise the time for the service team to locate a client location by using GPS services and can reduce the on-site time spent servicing a clients' request [1, 2]. Real-time access to the clients' location eliminates the need for the service team to return to the service provider's facilities in order to get information about a new client's request, thereby optimising the scheduling element [3].

The IoT has brought new functionality possibilities for many industries such as manufacturing and field services [16, 17]. It is expected that soon more than fifty billion devices ranging from smart phones, laptops, sensors and game consoles will be connected to the Internet through heterogeneous access network technologies [18]. However, the successful implementation of an IoT system introduces several other challenges. The abundance of data provided by sensors can introduce inefficiencies in data transfer and a need for aggregated data since sensor nodes are constrained by limited resources, for example computational power, memory, storage, communication, and battery energy [15]. These constraints provide an important challenge to design and develop approaches to information processing and aggregation that are efficient and make effective use of the data. For a given query, it may not be necessary or efficient to return all the raw data collected from every sensor – alternatively information should be processed and aggregated within the network and only processed and aggregated information returned. From a system level perspective, the IoT can be viewed as a dynamic, radically distributed, networked system, consisting of many smart objects that produce and consume information [19]. It can optimise business processes by leveraging on advanced analytics techniques applied to IoT data streams [19]. Thus, it provides good potential for addressing the downtime problem, if successfully implemented.

Although technology advances enable the possibility of the IoT, it is the application of the IoT which is driving its evolution [18]. The potential social, environmental and economic impact that the IoT has on the decisions we make and the actions we take is its main driving force. For example, having accurate information about the status, location and identity of things which are part of our environment opens the way for making smarter decisions. The application domains that the IoT includes can provide a competitive advantage beyond current solutions. In its inception the IoT was used in the context of supply chain management with RFID tags as the enabling technology [7]. However, in the past decade its applications have covered a wide range of industries, including transportation and utilities, to name just a few. Hwang et al. [20] classified the potential business contexts of IoT into three different factors: industry applications (for example government, education and finance); service domains (for example transportation, asset management) and value chain activities (for example sales and marketing, service or procurement). On the other hand, Borgia et al. [18], classified the IoT into three application areas: industrial (for example agriculture, logistics or other industrial applications), health/well-being and smart city. The smart city factor includes safety, mobility, buildings, road conditions, waste collection and public lighting.

#### 3 Context of Research: Smart Lighting

The case study used in this research is a smart lighting system that is maintained at an engineering consulting and research organisation in South Africa. For purposes of anonymity, the organisation will be called LightCo. The smart lights that are used as outdoor luminous equipment for parking bays and security lights for building facilities and are grid independent; meaning they are not connected to a local or municipal electricity provider for the energy needed to light them. An interview was conducted with one of the senior engineers at LightCo in order to establish an overview of the environment as well as the challenges faced by the organisation in delivering maintenance services for the smart lighting environment.

Smart lighting consists of the integration of intelligent functionalities and interfaces at four complementary levels [4], namely: the embedded level; system level; grid level and communication and sensing level. The embedded level is the lighting engine or the light itself, whilst the system level is the luminaries and lighting systems. The grid level consists of the management and monitoring of the power sources, energy generation and plants and the distribution of utilities and appliances. The final level is the communication and sensing level, which provides complete lighting solutions with monitoring, control and management of the applications.

The smart light unit at LightCo consists of an on board 48-voltage battery pack that is used as an energy storage unit. The solar panel is used to harness solar energy and the wind turbine generates electricity by the turning of a generator. The architecture of the smart lighting system allows for remote monitoring. The smart light also contains sensors and actuators that enable it to measure environmental variables and to respond to specific conditions by means of the actuators. The sensors include ambient sensors on the solar panel and voltage and current sensors on the circuit board of the smart light. Furthermore, the smart light is uniquely identifiable and contains on-board microcontrollers that provide computational and communication capabilities. The microcontrollers receive voltage and current data readings from the solar panel and wind turbine and also record the voltage and current that is outputted to the LED light. The battery management system manages the flow of current to the battery. Once these readings have been recorded they are then sent to a remote server for processing.

Prior to starting this study, the smart lighting system at LightCo did not provide for efficient or effective downtime management. Technical problems with the lights were not being reported timeously and were not correctly diagnosed due to IQ issues reported in literature [1, 14]. These problems could be for example, an LED light or circuit board is damaged. The system that was in place for detecting technical problems with their lights used a Global System for Mobile Communications (GSM) SMS-based messaging/polling protocol to transfer data from a smart light to a server at a remote location. This protocol was reported as inefficient due to its high latency times and high data costs affiliated with the sending and receiving of SMS messages. Increasing the latency was not an option, since it would increase the data costs. Data transmission was not bi-directional and data was merely recorded in a CSV file, with no processing performed on it. An Arduino microcontroller was situated in each smart light with a GSM Shield, which allowed the Arduino board to send and receive an SMS as well as connect to the Internet using the GSM library. However, the system did not use the GPRS wireless component that would enable the Arduino to connect to the Internet. Technicians had to manually peruse the data to diagnose any issues or potential issues.

#### 4 IoT Model for Downtime Management

The Three Phase Data Flow Process model proposed by Borgia et al. [18] (Fig. 1), the four layers of IoT [25], and IQ theory were used as the main guiding theories for the proposed IoT Model for Downtime Management (Fig. 2). The model describes the flow of data in the IoT over three phases [18], namely the Collection Phase; the Transmission Phase; and the Process Management and Utilisation phase and four layers [25] (the Sensing Layer; the Networking Layer; the Service layer; and the Interface layer). The Sensing Layer consists of hardware that senses and controls the physical world and acquires data. Examples are RFID, sensors and actuators. The Network Layer provides networking support and transfers data over either a wireless or a wired network. The Service Layer is responsible for the provision of services to satisfy the user needs and creates and manages services. The Interface Layer (or Application Layer) interacts with other applications and users.

Fig. 1. The three phase data flow process [18]

The Collection Phase reports on the event driven processes during the collection and acquisition of the data from the environment [18]. Data acquisition technologies attached to sensors and cameras collect information about the physical environment (temperature, humidity and brightness), or about the objects (identify and state) in realtime; while data collection is accomplished by short range communications, which could be open source standard solutions or proprietary solutions. In the FSM context these would be integrated into the equipment or assets in the field, for example the smart light. The Transmission Phase involves mechanisms that deliver collected data to various applications and external servers [18]. Once data has been collected it must be transmitted across the network so that it can be consumed by applications. For wired technologies the standard is Ethernet IEEE802.3. The primary advantage that wired networks have for data transmission is that they are robust and less vulnerable to errors and interference. However, they are costly. Therefore Wireless LAN (WLANs) are often used to access the network. Due to the flexibility of WLANs, it is believed that they will be the main communication paradigm of the IoT. However, the restricted wireless spectrum available for cellular networks is a major limitation to their widespread use.

Fig. 2. IoT model for downtime management

The Processing, Management and Utilisation Phase incorporates the processing and analysing of information flows, data forwarding to services and applications and the provision of feedback to control applications [18]. It also involves device discovery and management, data filtering, aggregation and information utilisation. The Service Platform & Enabler sub-phase covers an important role for managing these functions and is necessary in order to hide the heterogeneity of hardware, software, data formats, technologies and communication protocols that are a key feature of the IoT. Its responsibility is to abstract all the features of objects, networks and services, and to provide a loose coupling of components.

### 5 Methodology and Development of the Prototypes

#### 5.1 Methodology

The Design Science Research (DSR) methodology [21] was adopted in this study to create and evaluate the artefacts (model and prototype). The model was derived from a systematic literature review as well as from the case study of smart lighting maintenance, which was used for implementing and evaluating the model. The Technical and Risk efficacy evaluation strategy from the Framework for Evaluation of Design Science (FEDS) is used in the DSR methodology for evaluations conducted in the design cycle of DSR and was used in this study to evaluate both the model and the KapCha prototype [22]. An artificial-summative evaluation was used to evaluate the design of the model, but due to space limitations these results are not reported on in this paper but are available on request. Iterative formative evaluations were conducted during the development of the prototype; after which a summative-naturalistic evaluation was conducted in order to determine the performance of the prototype under realworld conditions.

#### 5.2 The Prototypes and Their Mapping to Requirements

The KapCha prototype was developed using an incremental prototyping process comprising of three prototype components (Table 1). ProWebSoc is the web socket protocol; ProObjWeb is the web socket client; and ProDT is the interface layer and web socket server. The IoT Model for Downtime Management (Fig. 2) was used to design the architecture of the prototypes.

Table 1. Prototype components

#### Collection and Transmission Phases (ProWebSoc and ProObjWeb)

As an alternative to the SOAP/XML data transmission protocols used by LightCo prior to this intervention, a protocol based on JavaScript Object Notation (JSON) was implemented. JSON, is a text-based open standard format that is designed for humanreadable data interchange and used for the serialisation of structured data making it easy for machines to parse and generate it. JSON is ideal for low processing computational capabilities (such as the smart light) and can result in less data that needs to be generated as compared to SOAP/XML.

ProObjWeb, through the web-socket client, enabled the smart light in the case study, as an OEM, to interface with a remote web-server using the KapCha web-socket protocol and to transmit data over a web socket protocol (ProWebSoc). Web sockets enable bi-directional communication (upstream and downstream) through the introduction of an interface and the definition of a full-duplex single communication channel that operates through a single socket [23]. They provide a reduction in network traffic and latency as compared to polling and long-polling solutions that are used to simulate a full-duplex connection by maintaining two connections. They also reduce the amount of port openings on the server side, as compared to the traditional means of retrieving resources such as polling. This reduction also reduces maintenance of connection channels from the server side, therefore decreasing the overhead network traffic. The web socket protocol also has the ability to traverse firewalls and proxies, which is a problem for other protocols. The protocols provide real-time communication (RTC) between a smart object and a central system or other smart objects and supports ad hoc and continuous data transfer as well as operational status communication and Remote Procedure Calls (RPCs).

A GPRS wireless component was used to enable the Arduino to make use of web socket technology. The web socket client was developed on the Arduino board using the Arduino open source software and several web socket methods. During connection, the web socket detects the presence of a proxy server and automatically establishes a tunnel to pass through the proxy. The tunnel is established through the opening of a TCP/IP connection. The connection is established by the client issuing an HTTP connect statement to the proxy server for a specific host and port. Upon the tunnel being set up communication flows uninterrupted through the proxy.

The web socket protocol (ProWebSoc) was designed to work with existing web infrastructure, therefore the protocol specification defines that the web socket connection starts as an HTTP connection [24]. This guarantees full backwards compatibility with HTTP based communication protocols. The upgrade from an HTTP to web socket is referred to as a handshake. In this process the client sends a request to the server indicating that it wants to switch protocols from HTTP to Web sockets, by means of an upgrade header. During the handshake process the server accepts the request and responds with an upgrade switch header. The server acknowledges receipt of the client's request by taking the |Sec-Web socket-Key| value and concatenating it with a Globally Unique Identifier (GUID) in string form. An SHA-1 hash (160 bits) base64-encoded of this concatenation is then returned in the server's response. This prevents an attacker from tricking a web socket server by sending it carefully crafted packets using XMLHttpRequest or a form submission.

Web sockets are ideal due to the ability to use customised protocol calling depending on the service being offered [23]. In ProObjWeb, when the client receives a response with no errors the connection is upgraded to a web socket over the same TCP/IP connection. Once the connection is established data frames between clients and servers can be transferred. Once the web socket client application connects to the webserver, the webserver initiates an upgrade sequence to upgrade the connection from an HTTP connection to a web socket server. ProObjWeb was functionally tested using the web socket.org echo server, which allows developers of web socket applications to test the ability of their applications to successfully upgrade the connection from HTTP to a web socket protocol. The test results showed that ProObjWeb successfully managed to connect to the web.org server and upgrade from HTTP to web sockets.

#### Management and Utilisation Phase

The third prototype (ProDT) focused on the development of the web-socket server application, a decision tree algorithm implementation and a REST (Representational State Transfer) API web interface. REST APIs with web socket Requests/Responses were used to form an intermediate layer between a client and the database, translate the raw data from the database to a format that the client requests and transmit the data. Most databases provide real-time notifications for added or updated data. However, real-time notification passage between the database and the client was required, therefore an Interface Layer was created in ProDT using a web socket server. The websocket server also handled communication with the database, which is the core of the application architecture. These techniques (including the protocols) provided real-time notification between the database and the client, eliminating the need for the Ajax techniques of polling. The database contained information about each smart light such as the date of manufacture and installation, its GPS location, object data sent by the Smart Light, the fault issue result after data analysis and job card data.

In order to generate an issue, data analysis using a decision tree learning algorithm was implemented. A decision tree is a tree structure consisting of nodes that each represent a test of an attribute with each branch representing a result of the test [29]. The tree splits observations into mutually exclusive subgroups until observations can no longer be split. The ID4.5 is a popular splitting algorithm that builds a decision tree by employing a top-down, greedy search through the given sets of training data to test each attribute at every node. Decision trees require little effort for data preparation unlike some statistical techniques and they are easy to interpret. The data collected was categorised data and therefore is ideal for decision tree. Furthermore, as there was no historical information on the diagnosis of faults or issues the decision tree was the ideal AI technique to be used as the classifier would be developed from the expert's opinion of plausible issues. The diagnosis of a set of problems based on these opinions was determined as a classification problem.

The improvements in the quality of information provided by these techniques thus allowed for advanced data analytics and intelligent algorithms (such as decision trees) to be conducted on the IoT data streams. The ability to interface with mobile technologies was also provided.

#### 6 Experiment Procedure and Findings

The aim of the experiments was to evaluate the Round-Trip Delay time (RTD) of messages, latency, accuracy of the decision tree analysis and scalability. Due to space constraints this paper only provides details of the RTD and accuracy experiments.

Round-Trip Delay time (RTD). The RTD experiment measures the time taken for a client to send a signal to a server and the time it takes for the server to acknowledge the signal and send a response [22]. In this context the client was the smart light and the server was the remote web socket server application. For the RTD experiment a connection had to be established between the smart light and the web socket server application. The experiment procedure involved running the applications in three cycles; the first cycle involved sending a data packet 10 times, then the second cycle 100 times and the third cycle 1000 times. The data packet was an array data object that was instantiated and sent to the server.

Two phases of experiments were performed: the first phase (local testing) consisted of running the server applications on a local host machine; and the second phase (remote testing) of the experiment involved running the server applications on a remote server. The performance metrics for the RTD evaluation were delay time and messages per second.

From the results of all three cycles it is evident that the KapCha web socket had a lower RTD time as compared to the Ajax protocol (Table 2). The results can be attributed to the fact that there are fewer HTTP overheads when using web sockets as compared to Ajax requests. Upon the connection being established all messages are sent over the single socket connection rather than the creation of new connections for new HTTP request and response calls created every time a message is sent over the Ajax protocol.

Furthermore, the web socket protocol had more messages sent per second as compared to that of the Ajax protocol. The messages per second for web sockets are higher because the web sockets establish the connection once over a single socket, unlike Ajax techniques that require multiple connections to be opened and closed during request/response calls. Therefore, web sockets do not have messages delayed during the connection process and can send more messages per second. The messages sent per second over the web socket protocol increased exponentially with the number of iterations completed.

Table 2. Experiment results – RDT and messages per second

The results from the remote testing set of experiments revealed that the web socket protocol had a lower RTD time as compared to the Ajax protocol when the number of packets was lower than a certain level. This result could be due to the upgrade sequence overhead during the web socket handshake process. The additional overhead connection, however, is not significant as the number of iterations increase due to the maintenance of the single socket connection. The RDT results highlighted the advantages of applications that use web sockets have over HTTP polling mechanisms. The advantages are lower latency and the provision of a single socket connection that enables the web server to push data to the client at will, creating a fully duplex bidirectional data exchange web-protocol.

Accuracy of decision tree: Prior to the development of the prototype there wasn't any data stored regarding the cause of a fault or the documentation of the diagnosis of a fault at LightCo. Therefore, the accuracy of the training dataset created was based on the expert's verification. The C4.5 decision tree algorithm was used to analyse the data and deduce the cause of the faults that occurred. For the experiment, the algorithm was deployed/executed to analyse three sample data set sizes of 50, 100 and 175. The number of correct predictions after each execution was recorded and verified by an expert at LightCo. This process was undertaken to establish the accuracy of the algorithm in diagnosing faults. The execution time of the algorithm was also recorded to determine the turnaround time of the fault diagnosis. The formula used for determining the accuracy percentage was:

$$\text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}} = \frac{f\_{11} + f\_{100}}{f\_{11} + f\_{10} + f\_{01} + f\_{00}}.\tag{1}$$

The accuracy results are summarised in Table 3. The sample size of fifty (n = 50) resulted in a percentage accuracy of 82.2% meaning that 41 out of the 50 predictions were correct. The sample size of 100 had 79 correctly predicted faults with a percentage accuracy of 79%. The final sample size had an accuracy percentage of 77% that is 96 faults were correctly predicted. Whilst the accuracy results were all above 70% additional testing is required to determine accuracy of larger datasets. However, this could not be done since previous records were non-existent and the training set was small. This is a limitation of this study. Future studies should perform the accuracy tests on the larger data set, which will increase rapidly with time. However, in spite of this limitation useful results and lessons learned were obtained regarding the IoT techniques used in the model.

#### 7 Conclusions

In this paper a theoretical prescriptive model for optimising downtime management is proposed that was derived from a systematic literature review of FSM, IoT and IQ theory. The use of intelligent algorithms and data accessibility are features of the model that can aid in the reduction of downtime. The model also supports geographically dispersed devices and clients. From a practical viewpoint, an organisation in the smart lighting industry was used to test the model as a proof of concept. In the case of the smart lighting scenario, prior to the intervention of our study, an SMS/Ajax polling system was used that was slow and expensive due to the data costs. As a results insufficient data was provided to assist with detecting and diagnosing problems. The solution lacked real-time information and field service technicians had to rely on human 'diagnostics' and sometimes travelling to the smart lights in order to physically detect problems. The proposed IoT model for downtime management was used to design an architecture and to develop and implement a system prototype for optimising downtime management in the smart lighting environment.

The evaluations of the prototype revealed that web-sockets are more efficient and cost-effective than other web-based data transfer protocols such as Ajax. The implementation of a web-socket based protocol provided a low-cost data communication protocol with real-time bi-directional capabilities and fully duplex communication between a smart light and a remote server. The use of IoT-enabled communication protocols reduced the latency time and data exchange costs. Furthermore, the websocket server implements an expert system mechanism using intelligent algorithms for data analysis. The intelligent algorithm, a C4.5 decision tree, automates fault detection and provides an issue report. The intelligent algorithms can assist service technicians to identify and diagnose problems. The practical contributions of this research are therefore the model, which can be used by FSM organisations in the implementation of IoT. The results of the evaluations revealed that the implementation of the various techniques and features of the model optimised downtime within the smart lighting environment. A problem encountered during the study related to restrictions on GSM protocols by the mobile service providers, some of which do not support the use of web-socket connections. Another challenge was inventor patents on the smart lights in the case study that restricted testing of the prototype in its natural environment. As a result only historical data was used for testing. A further limitation was that not all elements of the model could be tested due to time and resource constraints. However, the findings of this study can still be used by other researchers as a valuable source of reference when conducting similar research. The lessons learnt can be useful to other researchers and practitioners working in FSM and similar industries that can benefit from IoT.

The combination of advanced big data analytics, cloud-computing and IoT enables users to not only gather vast amounts of data but also enable them to process it without having to acquire high infrastructural costs. This leads to several opportunities for researchers in these fields. Future research directions could extend the study to include functionality such as predictive maintenance. AI mechanisms can be implemented in the model to support the prediction of faults before they occur. Additional intelligence can be achieved by interacting with other systems in the same environment that have a direct impact on the equipment's performance. The addition of predictive mechanisms as well as enabling object interaction with other systems will transform regular equipment into a self-aware and self-learning machines, and consequently improves overall performance and maintenance management. The model serves as a reference model for standards and protocols in an IoT-based implementation in the field of downtime management within the after-sales industry. Although the study was limited to evaluating the prototype in only one environment it provided valuable lessons that could be used by other practitioners and researchers to guide the implementation of IoT in FSM.

Acknowledgements. The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the NRF.

#### References

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## IoT Enabled Process Innovation: Exploring Sensor-Based Digital Service Design Through an Information Requirements Framework

Niclas Carlén, August Forsman , Jesper Svensson(&) , and Johan Sandberg

Department of Informatics, Umeå University, 901 87 Umeå, Sweden {niclas.carlen,august.forsman,jesper.svensson<EMAIL_ADDRESS>
Abstract. Through digitisation of physical artefacts and environments, the Internet of Things carries vast potential for process innovation. However, navigation of the quickly evolving technological landscape and identification of emerging opportunities for value creation remains challenging. To this end, we combine existing frameworks on information requirements, IT capability, and business value of IT. We evaluate the usability of these frameworks for IoT enabled innovation in our analysis of two sensor-based process innovation projects. We investigate the fit between process characteristics and technological functionality, and the implications of this alignment. Our analysis demonstrates that the framework provides a practically useful and theoretically coherent conceptual device for analyzing process characteristics and digital options to innovate processes. Furthermore, we find that IoT sensors are well suited to address connectivity and uncertainty requirements. However, in order to leverage them to address high equivocality requirements designers need deep contextual understanding to align IoT capability with information requirements.

Keywords: Process innovation Internet of Things Information requirements

## 1 Introduction

The ongoing pervasive digitisation of physical artefacts and environments, often collectively referred to as the Internet of Things (IoT), signifies a new paradigm in data processing and communication. It is not a new technology per se, Wortmann and Flüchter (2015) describes IoT as "a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies". IoT has evolved into an ecosystem of possible objects or things to connect to the internet where basically anything that has an on and off switch can be connected e.g. sensors. Combined with an expected battery life of several decades for many sensors allows for data collection around the clock. Sensors like this are making headway around the world with the emergence of smart cities, i.e. cities that are connected and efficient collecting data to manage its resources and serve its citizens in the best way possible (Sundmaeker et al. 2010). The global market for IoT is growing tremendously, resulting in significant market opportunities and an expected turnover at around 8.9 trillion dollars combined with an estimated 26–100 billion connected devices by the year 2020 (Statista 2017). Currently, there are significant uncertainties when companies engage in IoT; lack of standards, low understanding of technology and security issues are all sizeable problems hindering development (Forbes Insight 2017). Most companies feel uncertain which IoT-solution will collect the data they need, this results in a discrepancy in what companies need and what they get with regards to data and information (Forbes Insight 2017). In this paper, we argue that this uncertainty can be mitigated by synthesising two theoretical frameworks combining both analysis of information requirements and corresponding digital options with effects on processes by different IoT implementations.

We have been involved extensively in two IoT-projects and have seen issues regarding information requirements and process analysis arise throughout both. Both cases illustrate the need to understand the working environment and the technology at hand. While most studies thus far have focused either on the technical or business side of IoT (Forbes Insight 2017), we argue that there is a need to understand both aspects to generate successful outcomes. Since in most cases there is going to be humans interacting with the technology in their daily work life, we also argue that it is of great importance to understand the duality of technology and social activity. We have, therefore, focused on a micro-level perspective by investigating how IoT-systems affect work practices. This paper seeks to examine the effects of implementing sensor-based systems in organisations, i.e. how the implementation effects the process and value chain. This study will be limited to two separate case studies where sensor-based systems have been both designed and implemented in the organisations. Specifically, we explore the following research question:

How can IoT sensors be used for digital service design to innovate processes?

#### 2 Internet of Things in Business Processes

With the emergence of ICT in the 20th century a lot of IT projects have had lacklustre results, one reason is the assumption that technological functionality solves organisational issues (Alter 2006). The argument against this is that an IT system should be viewed as a resource for the solution and not the solution itself (Alter 2006; Meyer et al. 2013). IoT is also argued to have a decentralising effect on the value chain in business as parts of it become connected. As the value chain becomes decentralised the decision-making rights are moved to the individual components in the chain, and the different parts become more independent, arguably creating a more efficient and streamlined process chain throughout (Haller et al. 2008). We would add to the current research by enacting these principles in real-world implementations. Doing so with the intention of showing real-world scenarios with the technology in place, we argue that the current body of research will benefit by the holistic perspective applied in this study.

#### 2.1 A Framework for Analysing Process Effects with IoT and IoT Capability

In this paper, we synthesise two different theoretical frameworks. The first is a framework developed to identify what digital options are available to organisations and what information requirements different processes have (Sandberg et al. 2014). The second framework is intended to measure the effects of the chosen solution on the process (Mooney 1995). By synthesising we refer to using the frameworks in tandem, iteratively, throughout the process analysis, implementation and evaluation of the systems. This in turn contributes to the body of research with a practical framework encompassing the entire process of implementing IoT in an organisation with intent to innovate business processes.

#### Information Requirements

Information requirements are used to identify what digital options there are for a specific business process. Digital options should here be understood as opportunities for leveraging IT in process innovation. The conceptualisation is grounded in earlier research, but Sandberg et al. (2014) further develop the concept of a specific tasks requirements about uncertainty and equivocality by adding connectivity to modernise the theory. We extend existing applications of the framework by using the information requirements in an IoT context. Establishing information requirements for each case is the first step of the innovative work process to map if and how IoT can support or transform a business process. This analysis, enables generation of the digital options available to organisations. To identify information requirements there are three aspects to consider and analyse: connectivity, uncertainty and equivocality.

Connectivity relates to the informational dependencies between processes and systems within an organisation, i.e. the need for information sharing across boundaries in an organisation (Malhotra et al. 2005). If the connectivity need is regarded as high, the focus for managers should be to counteract technical or social barriers by increasing information reach, characterised as "the number information sources that can be accessed during task execution" (Sandberg et al. 2014, p. 428). If information can be accessed across organisational and geographical boundaries, process information requirements have low connectivity requirements. When the connectivity need is low, it can be relevant to increase richness which refers to "the number of data points available regarding a given object during task execution" (Sandberg et al. 2014, p. 428).

Uncertainty refers to the availability and accuracy of information needed for actors to execute their task within an organisation. Uncertainty requirements can be addressed by continually balancing information production and information consumption (Ramaprasad and Rai 1996). Information production occurs when actors generate new information based on stimuli in the process and its environment. Information consumption turns the existing and available information into business process actions. If the information requirements are high in uncertainty, i.e. the current information is inaccurate, unreliable or insufficient, organisations should aim to increase production of information as they should not want to consume unreliable information. When uncertainty requirements are low, organisations can instead focus on consumption of information. If the information is reliable, available options for consumption emerge which can drive an organisation to make better data-driven decisions.

Equivocality refer to confusion and lack of mutual understanding when executing a task, or the level of complexity and ambiguity in the tasks information processing (Sandberg et al. 2014) (Mathiassen and Sørensen 2008). When a task is to be executed, there may be a need for mutual understanding between actors or processes, as they may have to rely on contextual knowledge. Conversely, there can be situations where actors or processes can rely on their codified knowledge and routines. If information requirements are high in equivocality, then the situation or task context is unknown for the involved parties. A relationship enables success in these situations; there needs to be a high level of understanding and trust within the system and in the systems supporting situational knowledge and contexts. If the requirements are low, however, then the characteristics are based on encounters where standardised protocols and workflows are utilised.

Table 1. Information requirements and digital options characteristic (Adapted from Sandberg et al. 2014)

#### IoT Capabilities in Organisations

Sandberg et al. (2014) refer to the organisation's IT capabilities as a firm's previous investments in IT resources, such as technology or IT competence. We use the concept and expand upon it to fit the field of IoT and thereby refer to them as IoT capabilities. Table 1 shows both information requirements, their corresponding digital options and examples of IoT capabilities that reflect the requirement characteristic.

#### 2.2 Measuring Effects of IoT on Processes

We draw on Mooney's (1995) framework for assessing the business value of IT. Business performance is best measured from the performance of its processes (Ray et al. 2004; Mooney 1995). Sensors collect a limited range of data in an environment which in turn supports business processes. Therefore, we conclude that examining sensor implementation impact is most efficiently done in the context of the processes which they intend to support. In this study, we analyse the automational, transformational and informational effects of the IT-systems designed for each case, which are classifications derived from Mooney's process-oriented framework and have been used in similar studies (Stenmark and Jadaan 2010; Visich et al. 2009).

The automational dimension relates to how sensors data collection can substitute manual labour. Different kind of sensors can continuously collect data which are to support or initiate processes. Automational effects on business value are gained through aspects such as improved customer service, increased productivity and a more efficient labour distribution (Stenmark and Jadaan 2010; Visich et al. 2009).

Informational effects are those that are caused by IT-enabled collection, storage, processing and spread of information acquired from the sensors. Case studies of RFID implementations have shown that business value can arise from improved resource management and reduced manual labour (Stenmark and Jadaan 2010; Visich et al. 2009).

The transformational dimension affects and supports process innovation and transformation. Sensor data may support and improve existing processes but may also be utilised for business innovation. Data acquired to support a specific process chain can be used in combination with other aspects of the organisations' knowledge base to innovate the business (Stenmark and Jadaan 2010).

#### 3 Methodology

For this paper we conducted a multiple case study consisting of two cases where sensor-based IoT-systems, through collection and visualisation of data, were designed and implemented to support different process chains. Case studies are a preferred strategy when research questions related to "how" and "why" are posed (Yin 2003), and multiple case-studies when the logic of the study is to "produce contrasting results but for predictable reasons" (Yin 2003, p. 47). We argue that this makes a case-study approach viable with regards to the framing and research question stated earlier. By evaluating this innovation process through multiple cases, we intend to generate general findings and propose practices which could be built upon in further research. We chose the cases based on an analysis of two different research sites. The classification for inclusion was that the case should present one or several concrete problems in a process chain where sensor-based technology could be a solution. These problems could either be a lack of ability in performing activities which could be enabled by the technology or addressing problems currently present in an organisation.

A process is "a structured, measured set of activities designed to produce a specified output for a particular customer or market" (Davenport 1993, p. 5) and can be classified into two different categories; operational processes and management processes (Mooney 1995). Operational processes are the set of activities an organisation performs to produce something that generates value and is referred to as an organisation's primary activities. Management processes are related to streamlining and improving the efficiency of an organisation's primary set of activities such as coordinating and handling different information. Process innovation in this context refers to the practice of analysing an organisation's processes and redesigning them using innovative technology to improve performance and support the processes (Davenport 1993). In this case, that innovative technology is LoRa-sensors, which enables remote monitoring and control of different aspects of a process, and the IT-artefacts of the software designed to visualise or manipulate the data generated by the sensors. Each case process chain was broken down into sub-processes depending on what type of activities and the complexity of the tasks performed. We then mounted sensors at each research site to collect data and designed IT-artefacts with the purpose of solving specific problems related to the sub-process. The effectiveness of these systems was analysed in the context of what type of value and effects the data generated when innovating operational and management processes.

#### 3.1 Research Sites and Sensor Technology

The sensors used in the project are based on LoRa technology, and a specific type of gateway delivers connectivity to the sensors. Because of lacking infrastructure in the northern municipality, we were tasked to mount two base-stations; this implementation took place in September 2017. One of these base-stations was installed on the highest point in the town, which is a water tower, the other one on the roof of the local secondary school. We also installed a base-station in the city where the cleaning company project took place to further the existing coverage. The base-stations have guaranteed coverage of a 3 km radius, however, depending on disruption and quality of air it may be greater than that (LoRa-alliance 2015).

LoRa stands for Long Range and is the physical layer utilised to create long-range communication links. LoRa is based on chirp spread spectrum modulation, using its entire spectrum of bandwidth and is therefore very resistant to channel noise. LoRaWAN is based on Low Power, Wide-Area Networks i.e. LPWAN, and defines the communication protocol and system architecture for the network. LoRaWAN has a great influence on battery times for nodes, network capacity and security. LoRaWAN is explicitly designed for sensors and applications that need to send small amounts of data over long distances at different time intervals, making it ideal for IoT sensors applications. As IoT is still a new phenomenon and standards are currently lacking, other technologies are competing to become the business standard. In likeness with LoRa, several LPWAN networks are emerging as competitors; examples are Sigfox and Narrowband IoT that operate similarly (LoRa-alliance 2015).

In this study, we have used two different types of LoRa-sensors that measure different values while having some similar readings; temperature inside the casing, humidity at the sensor and battery-level (Table 2).

Table 2. LoRa Sensors utilised in projects with corresponding properties

#### 3.2 Data Collection and Analysis

The data collection process consisted of an analytical phase where we in conjunction with each organisation collected data regarding the problem background of their process chains. An implementation phase studying the physical environment of the research site, collected data relevant to the practical implementation of the system, and an evaluation phase where the data related to the results of the system was collected. All interviews conducted are semi-structured and were conducted throughout all three phases to capture viewpoints from the participants on both current problematic aspects, initial impressions of the implemented system, and impressions regarding its effects. Observations refer to activities where we have studied staff members performing the process chains, as well as documenting areas of interest. This was performed mainly during the analytical phase in order discover details relevant to mapping the process chains and capture insights possibly missed using the other methods. Workshops are meetings where we collected data related to our subsequent design choices and was mainly conducted in the analytical phase with participants in each innovation project. Informal encounters are the interactions with the organisation where we have performed different tasks or exchanged minor pieces of information related to the projects, a method which was applied throughout all three phases.

A total of 9 interviews, 11 observations, 7 workshops and 46 informal encounters were conducted, spanning all three cases in the study during a time frame spanning from 20th September 2017 to the 29th of Mars 2018 (Table 3).

Table 3. Data collection overview

The data analysis was performed in iterations together with the organisation throughout the time-frame of the data collection, where the input in the analytical phase formed the basis for our description of the information requirements and IT-capabilities in each case. The results of the evaluation period (testing the systems in practice) formed the input for the process effects each system had on the corresponding process chain it supported.

## 4 Results

This section consists of the results of our research, each case will be presented with a description of the research site, problem background and information requirements of the process chains, with a subsequent description of practical implementation and effects from each case.

#### 4.1 Swimming Pool

#### Research Site

The research site of the public swimming pool-case is in a municipality in the north of Sweden and is run by a small organisation of six people who maintain the pool, a gym and a gymnasium in the same building. The public pool is a facility open during weekdays and Saturdays on regular weeks.

#### Process Chains and Problem Backgrounds

The organisation lacks data on the number of visitors and which hours and days during the week generate most activity. The main areas of activity which the organisation found interesting were the entrance to estimate the overall number of customers, the cafeteria to investigate the air quality during peak hours, and the locker rooms to investigate differences in attendance between the genders. This data is interesting for the organisation when optimising staffing and air quality, and to create an overview of when and how much the facilities are used during the week. To achieve these informational effects, the sensor implementation sought to address the high uncertainty and low connectivity information requirements through continuous collection of visitor data. Further, the information regarding the number of visitors is non-equivocal as the collected data is readily interpreted in the context.

The second process chain in the case is optimising heating of the swimming pool. According to the person responsible for this routine, the pools are heated to 32 °C every week during Tuesday nights, and then the temperature falls successively to around 27 °C during the weekly cycle. This practice leads to uncertainty amongst the customers on the current temperature and generates phone calls to the organisation increasing the workload. Further, the facilities have shown signs of increased wear in forms of mould and moisture damage due to the increased evaporation generated by higher temperatures. The organisation seeks informational effects on the managerial level through an increased amount of temperature data points and implementing sensors to address the connectivity and uncertainty requirements. The equivocal requirements were low as temperature data is readily understood in the context.

The third process chosen for this case is the documentation of pool water quality, which is a process chain performed by the staff daily to discover anomalies and potential health risks related to the pool water. Water samples are collected, analysed and documented as the first task of every day: water temperature, pH-value and chlorine-levels. To collect this data, the staff places a thermometer in the pool water where it is submerged for 15 min. During this time, they gather two water samples which are analysed using a pool water quality kit establishing its pH-value and amount of chlorine. This data is then documented manually in a binder and stored in the staff office of the facilities. Due to the repetitive manner of the data collection and documentation, the organisation seeks to explore to what extent it could be automated using IoT sensors. To achieve these effects, automatic production of information regarding temperature, pH-value and chlorine levels is required, which characterises an information requirement high in uncertainty. We found the connectivity and equivocality requirements to be low as the information is to be utilised within task entity boundaries and well understood in the context.

#### Implementation

To support the first process chain, we mounted four sensors at areas for which the organisation had expressed interest. The main units of observation these sensors were to measure were motion activity and temperature in each respective area. The sensors were placed at the entrance of each respective area at the height of around 150 cm's ensuring measurements of every individual passing.

This motion activity and temperature-data is uploaded every 30 min to a database, imported into tables and transformed into graphs, both real-time and historical. The information was made accessible to the organisation through a web application where it could be studied and form part of the basis for process innovation. The implemented system has the characteristics referring to the production of information to address uncertainty requirements. As this process aims to collect information about motion and temperature in the facility we argued that the four sensors would address the uncertainty requirements.

To support the second chain, we mounted one temperature sensor in the bottom of the swimming pool, hidden behind a ladder. This sensor uploaded water temperature every 30 min to a database and was imported into tables and graphs made available to the organisation. The historical data generated by the sensor can be utilised to measure how much time it takes to heat the pool to the preferred temperature and get a more detailed overview of its heating cycle. This data could serve as a basis for innovating the heating process chain and minimise the problems of their current practices. The third process chain utilised the same temperature sensor as the second as the only relevant unit in the process chain collected and documented is the temperature data. This data was then uploaded every 30 min and presented in the form of tables and graphs. For this process, we addressed the information requirements with the intent to increase richness by collecting data with the sensor as well as produce more information to lower uncertainty.

#### Effects

The first process chain showed primarily informational effects. The data collected from the motion sensors generated an estimate of which areas has the most activity, and during which hours can be established by studying the graphs in the web application. The organisation had ideas of using the data to optimise the air conditioning. However, the functional capabilities of adjusting the air conditioner cycles to reflect usage or be automated by the data seemed to be limited which was uncovered later in the project when this issue was discussed with a janitor responsible for the air conditioning. A side-effect of studying the comprehensive dataset was the discovery that the temperature in the cafeteria rises around 2–3 °C during the nights when the facility is closed. This occurrence was unknown to the organisation when they were informed of it, and according to one of the staff members, may be related to the underfloor heating being active during the night time when the ventilation is inactive.

The effects on the second process chain had informational effects. The historical data generates a clear and consistent timeframe over how long it takes for the swimming pool to reach its intended temperature and shows some anomalies. An example is the re-warming of the pool, which usually happens around 4 h after it has reached its maximum temperature. Why this happens is unknown to us right now but will be of interest in further evaluation of the system. The third process chain has potential to be completely automated, generating both automational and transformational effects, but since the sensors automate only 1/3 of the data collection, the staff must still perform a majority of the process chain in the same manner as before. A future update of the system will be to implement sensors collecting data of the chlorine level and pH-value. With a complete system in place, the whole process will be performed continuously and automatically document the data in the same way as current practice. The permanent character enables transformational possibilities in the sense that with a system documenting the water quality continuously, anomalies in the water can be discovered faster.

#### 4.2 Cleaning Company

#### Research Site

The research site for the case of the cleaning company is located in a university building. The organisation is responsible for cleaning all facilities and have a staff of six managing and executing this task at the research site.

#### Process Chains and Problem Backgrounds

The staff have expressed a problem of prioritising which order that classrooms are cleaned after the weekend in an effort to work more condition-based. The current situation is such that the activity in the classrooms during the weekends is unknown to the staff when they begin cleaning on Mondays, and they clean each room in a set routine. The consequence of cleaning in a routine-based manner could result in rooms with less cleaning needs getting cleaned, and rooms with higher cleaning need left unattended. The cleaning process of each room constitutes four sub-processes: cleaning the floor, cleaning the tables, wiping the whiteboard and emptying the waste bin, and the cleaning need of each sub-process in combination is what constitute the cleaning need of the classroom. The organisation seeks informational effects through collection of information regarding classroom cleaning need. This information requirement has the characteristics of high equivocality, as assessing cleaning need is non-algorithmic and is based on situated knowledge about the specific context. The process chain has high connectivity and both high and low uncertainty requirements as cleaning need information must be collected and accessed remotely. Since the organisation currently lacks data on the activity in the classrooms during the weekends, it cannot innovate its processes in such a way that it aligns with the ambition of working more conditionbased.

The second process chain presented as problematic by the organisation is assessing if a room is vacant. In the current situation, the staff do not clean rooms which are occupied and wait until the rooms are vacant to clean them. This problem means in practice that they sometimes spend time visiting rooms only to discover that they cannot be cleaned, and delay that process until later, having wasted time moving to the classroom. The organisation seeks informational effects on the operational level through remote access to information with regards to classroom vacancy. To achieve these effects, vacancy information need to be produced and remotely accessible to cleaning staff, which characterises the information requirements as high in connectivity and uncertainty. Lastly, information regarding classroom vacancy is non-equivocal as the room is either vacant or not.

The third process chain is the comparison between the presumed usage based on the booking schedule and actual usage of the specific classrooms. According to the organisation, it is not uncommon for a room to be booked during the week, but its actual usage is unclear. The staff can plan the cleaning of classrooms only to discover that they have not been used and, therefore, not in need of cleaning. This could also be used as a basis when negotiating terms with its currently largest customer which is the university itself. Part of how many hours the company can bill the university is based on the number of hours booked in the electronic booking schedule. The organisation seeks informational effects on the managerial level through remote information collection of classroom usage. High connectivity, high uncertainty, and low equivocality characterises its information requirement. The multiple data collection points increase reach and production of this non-equivocal information.

#### Implementation

To support the process chains described in the case we mounted sensors in classrooms collecting motion data. The sensors were placed at the entrance around 170 cms from the floor, registering every motion near the entrance door. This data was uploaded every 10 min to a database. We then designed a web-application containing various artefacts which utilise this data to address the information requirements in the process chain. Due to the high connectivity requirements the purpose was to increase reach through multiple data collection points. For the uncertainty requirements, there was a need for both production and consumption to address the requirements relating to uncertainty. The sensors installed addressed the information production aspect, and the web application was developed to increase consumption of information. As the equivocality requirements were high, the need for a relationship characteristic was of high priority. Due to the organisation wanting to measure cleaning need in the rooms, which is a highly equivocal measurement, the sensors and web application needed to be utilised in conjunction with the cleaning staffs' knowledge and routines.

The artefacts contained in the application display three sets of data; accumulated motion in each room, a two colour-button signalling if motion has been detected the last 10 min and historical data available for export in the form of graphs. The application was made accessible for the cleaning staff in their day-to-day work by a tablet placed on their cleaning cart.

The system was tested for three weeks, during which the staff had access and utilised it when performing their tasks. Furthermore, they graded the experienced cleaning need which was defined on a three-grade scale where one was clean, two was normal and three related to a high cleaning need. Interviews with the staff were conducted before, during and after the test period. During this period, we also tested the hypothesis that an increased motion value from a classroom during the weekend represents a higher cleaning need. This hypothesis was tested by photographing every aspect related to the sub-processes of the cleaning process chain after the weekends, comparing the empirical findings with motion data captured by the sensors.

#### Effects