vehicle's camera needs to make real-time obstacle detection to avoid an accident. this fast decision making would not be possible through transferring data from the vehicle to cloud instances and return the predictions back to the vehicle. instead, all the operation should be performed locally in the vehicle. integrating advanced machine learning algorithms including deep learning into iot devices is an active research area to make smart objects closer to reality. moreover, it is possible to get the most value out of iot deployments through analyzing iot data, extracting hidden information, and predicting control decisions. a wide variety of machine learning techniques have been used in iot domain ranging from traditional methods such as regression, support vector machine, and random forest to advanced ones such as convolutional neural networks, lstm, and variational autoencoder. in the future, the internet of things may be a non-deterministic and open network in which auto-organized or intelligent entities (web services, soa components) and virtual objects (avatars) will be interoperable and able to act independently (pursuing their own objectives or shared ones) depending on the context, circumstances or environments. autonomous behavior through the collection and reasoning of context information as well as the object's ability to detect changes in the environment (faults affecting sensors) and introduce suitable mitigation measures constitutes a major research trend, clearly needed to provide credibility to the iot technology. modern iot products and solutions in the marketplace use a variety of different technologies to support such context-aware automation, but more sophisticated forms of intelligence are requested to permit sensor units and intelligent cyber-physical systems to be deployed in real environments. architecture iot system architecture, in its simplistic view, consists of three tiers: tier 1: devices, tier 2: the edge gateway, and tier 3: the cloud. devices include networked things, such as the sensors and actuators found in iot equipment, particularly those that use protocols such as modbus, bluetooth, zigbee, or proprietary protocols, to connect to an edge gateway. the edge gateway layer consists of sensor data aggregation systems called edge gateways that provide functionality, such as pre-processing of the data, securing connectivity to cloud, using systems such as websockets, the event hub, and, even in some cases, edge analytics or fog computing. edge gateway layer is also required to give a common view of the devices to the upper layers to facilitate in easier management. the final tier includes the cloud application built for iot using the microservices architecture, which are usually polyglot and inherently secure in nature using https/oauth. it includes various database systems that store sensor data, such as time series databases or asset stores using backend data storage systems (e.g. cassandra, postgresql). the cloud tier in most cloud-based iot system features event queuing and messaging system that handles communication that transpires in all tiers. some experts classified the three-tiers in the iot system as edge, platform, and enterprise and these are connected by proximity network, access network, and service network, respectively. building on the internet of things, the web of things is an architecture for the application layer of the internet of things looking at the convergence of data from iot devices into web applications to create innovative use-cases. in order to program and control the flow of information in the internet of things, a predicted architectural direction is being called bpm everywhere which is a blending of traditional process management with process mining and special capabilities to automate the control of large numbers of coordinated devices. network architecture the internet of things requires huge scalability in the network space to handle the surge of devices. ietf 6lowpan can be used to connect devices to ip networks. with billions of devices being added to the internet space, ipv6 will play a major role in handling the network layer scalability. ietf's constrained application protocol, zeromq, and mqtt can provide lightweight data transport. in practice many groups of iot devices are hidden behind gateway nodes and may not have unique addresses. also the vision of everything-interconnected is not needed for most applications as it is mainly the data which need interconnecting at a higher layer. fog computing is a viable alternative to prevent such a large burst of data flow through the internet. the edge devices' computation power to analyze and process data is extremely limited. limited processing power is a key attribute of iot devices as their purpose is to supply data about physical objects while remaining autonomous. heavy processing requirements use more battery power harming iot's ability to operate. scalability is easy because iot devices simply supply data through the internet to a server with sufficient processing power. decentralized iot decentralized internet of things, or decentralized iot, is a modified iot. it utilizes fog computing to handle and balance requests of connected iot devices in order to reduce loading on the cloud servers and improve responsiveness for latency-sensitive iot applications like vital signs monitoring of patients, vehicle-to-vehicle communication of autonomous driving, and critical failure detection of industrial devices. conventional iot is connected via a mesh network and led by a major head node (centralized controller). the head node decides how a data is created, stored, and transmitted. in contrast, decentralized iot attempts to divide iot systems into smaller divisions. the head node authorizes partial decision-making power to lower level sub-nodes under mutual agreed policy. performance is improved, especially for huge iot systems with millions of nodes. decentralized iot attempts to address the limited bandwidth and hashing capacity of battery powered or wireless iot devices via lightweight blockchain. cyberattack identification can be done through early detection and mitigation at the edge nodes with traffic monitoring and evaluation. complexity in semi-open or closed loops (i.e., value chains, whenever a global finality can be settled) the iot will often be considered and studied as a complex system due to the huge number of different links, interactions between autonomous actors, and its capacity to integrate new actors. at the overall stage (full open loop) it will likely be seen as a chaotic environment (since systems always have finality). as a practical approach, not all elements on the internet of things run in a global, public space. subsystems are often implemented to mitigate the risks of privacy, control and reliability. for example, domestic robotics (domotics) running inside a smart home might only share data within and be available via a local network. managing and controlling a high dynamic ad hoc iot things/devices network is a tough task with the traditional networks architecture, software defined networking (sdn) provides the agile dynamic solution that can cope with the special requirements of the diversity of innovative iot applications. size considerations the exact scale of the internet of things is unknown, with quotes of billions or trillions often quoted at the beginning of iot articles. in 2015 there were 83 million smart devices in people's homes. this number is expected to grow to 193 million devices by 2020. the figure of online capable devices grew 31% from 2016 to 2017 to reach 8.4 billion. space considerations in the internet of things, the precise geographic location of a thing—and also the precise geographic dimensions of a thing—can be critical. therefore, facts about a thing, such as its location in time and space, have been less critical to track because the person processing the information can decide whether or not that information was important to the action being taken, and if so, add the missing information (or decide to not take the action). (note that some things on the internet of things will be sensors, and sensor location is usually important.) the geoweb and digital earth are applications that become possible when things can become organized and connected by location. however, the challenges that remain include the constraints of variable spatial scales, the need to handle massive amounts of data, and an indexing for fast search and neighbour operations. on the internet of things, if things are able to take actions on their own initiative, this human-centric mediation role is eliminated. thus, the time-space context that we as humans take for granted must be given a central role in this information ecosystem. just as standards play a key role on the internet and the web, geo-spatial standards will play a key role on the internet of things. a solution to "basket of remotes" many iot devices have the potential to take a piece of this market. jean-louis gassée (apple initial alumni team, and beos co-founder) has addressed this topic in an article on monday note, where he predicts that the most likely problem will be what he calls the "basket of remotes" problem, where we'll have hundreds of applications to interface with hundreds of devices that don't share protocols for speaking with one another. for improved user interaction, some technology leaders are joining forces to create standards for communication between devices to solve this problem. others are turning to the concept of predictive interaction of devices, "where collected data is used to predict and trigger actions on the specific devices" while making them work together. social internet of things social internet of things (siot) is a new kind of iot that focuses the importance of social interaction and relationship between iot devices. siot is a pattern of how cross-domain iot devices enabling application to application communication and collaboration without human intervention in order to serve their owners with autonomous services, and this only can be realized when gained low-level architecture support from both iot software and hardware engineering. social network for iot devices (not human) iot defines a device with an identity like a citizen in a community and connect them to the internet to provide services to its users. siot defines a social network for iot devices only to interact with each other for different goals that to serve human. how is siot different from iot? siot is different from the original iot in terms of the collaboration characteristics. iot is passive, it was set to serve for dedicated purposes with existing iot devices in predetermined system. siot is active, it was programmed and managed by ai to serve for unplanned purposes with mix and match of potential iot devices from different systems that benefit its users. how does siot work? iot devices built-in with sociability will broadcast their abilities or functionalities, and at the same time discovers, navigates and groups with other iot devices in the same or nearby network for useful service compositions in order to help its users proactively in every day's life especially during emergency. social iot examples iot-based smart home technology monitors health data of patients or aging adults by analyzing their physiological parameters and prompt the nearby health facilities when emergency medical services needed. in case emergency, automatically, ambulance of a nearest available hospital will be called with pickup location provided, ward assigned, patient's health data will be transmitted to the emergency department, and display on the doctor's computer immediately for further action. iot sensors on the vehicles, road and traffic lights monitor the conditions of the vehicles and drivers and alert when attention needed and also coordinate themselves automatically to ensure autonomous driving is working normally. unfortunately if an accident happens, iot camera will inform the nearest hospital and police station for help. social iot challenges internet of things is multifaceted and complicated. one of the main factors that hindering people from adopting and use internet of things (iot) based products and services is its complexity. installation and setup is a challenge to people, therefore, there is a need for iot devices to mix match and configure themselves automatically to provide different services at different situation. system security always a concern for any technology, and it is more crucial for siot as not only security of oneself need to be considered but also the mutual trust mechanism between collaborative iot devices from time to time, from place to place. another critical challenge for siot is the accuracy and reliability of the sensors. at most of the circumstances, iot sensors would need to respond in nanoseconds to avoid accidents, injury, and loss of life. enabling technologies there are many technologies that enable the iot. crucial to the field is the network used to communicate between devices of an iot installation, a role that several wireless or wired technologies may fulfill: addressability