**Hypothesis 1 (H1).** *Innovation capabilities have a direct positive effect on the export performance of SMEs.*

According to Yi et al. (2013), the government relationship has a positive moderating effect on the relationship between innovative capabilities and export performance. Government institutional support can offset the negative effects of market imperfections by reducing transaction costs and enhancing the role of innovative skills in export performance. Government institutional support functions as an important formal regulatory mechanism that remedies the adverse effects of institutional voids and helps organize and direct effective business operations (Stephan et al. 2015). Export support programs are a clear example of how public policies can support firms in their competitive development (Mota et al. 2021; Malca et al. 2020). We thus argue that the government relationship

moderates the relationship between ICs and export performance. As such, it is possible to defend the following hypothesis:

**Hypothesis 2 (H2).** *Government institutional support positively moderates the relationship between innovation capabilities and the export performance of SMEs.*

SMEs' with proactive internationalization behavior, besides being better at managing their innovation processes, have entrepreneurial orientation capabilities that support better performance in international markets when compared to firms that react to external stimuli (Ribau et al. 2017a). On the other hand, proactive stimuli may result from SMEs' aggressive behavior and a deliberate search for market opportunities abroad (Westhead et al. 2004). Therefore, we argue that SMEs' active internationalization behavior positively moderates the relationship between innovation capabilities and export performance. Thus, it is possible to defend the following hypothesis:

**Hypothesis 3a (H3a).** *Proactive internationalization behavior positively moderates the relationship between SMEs' innovation capabilities and export performance.*

Reactive internationalization behaviors result from endogenous or exogenous factors that affect the firm's internationalization processes and export performance (Bruyat and Julien 2001; Westhead et al. 2004). Reactive internationalization behaviors can arise from within SMEs, but reflect involvement in international business as a reaction to certain external conditions or internal events (Westhead et al. 2004). Thus, we propose the following hypothesis:

**Hypothesis 3b (H3b).** *Reactive internationalization behavior positively moderates the relationship between ICs and export performance of SMEs.*

The proposed conceptual model is shown in Figure 1.

**Figure 1.** Proposed conceptual model.

#### **3. Methodology**

This study follows a quantitative methodology underpinned by the application of a questionnaire that was created as a result of a thorough literature review of different factors involving innovation capabilities, export performance, government support, and passive/active internationalization.

Data were collected using a questionnaire composed of scales adapted, validated, and published in previous research. We implemented a seven-point Likert-type scale in which respondents have the opportunity to agree or disagree and express the intensity of agreement (1 = Strongly disagree; 7 = Strongly agree). ICs were measured with a multidimensional scale developed by Guan and Ma (2003), the export performance scale

was adapted from Jantunen et al. (2005); Kuivalainen et al. (2007); Aulakh et al. (2000); Zou and Stan (1998). Relationship with government was adapted from Shu et al. (2019), and Li and Atuahene-Gima (2001), and active/reactive internationalization was adapted from Westhead et al. (2004).

The questionnaire was applied in Portuguese. As such, Brislin's (1971) recommendations were followed to avoid misunderstandings when translating the questionnaire from English into Portuguese and back to English. At the beginning of the questionnaire, respondents were shown a small introduction of what the questionnaire was about. For operational reasons, the questionnaire was divided into five different sections—one for each topic addressed: innovation capabilities, government institutional support, active/passive internationalization, and export performance—and a final one containing respondents' data.

The questionnaire was subjected to a pre-test conducted with a convenience sample of eight individuals (three university professors and five managers) in order to check the organization and formatting of the questionnaire, the correct wording, how the respondents understood the questions and the response time required, and to eliminate typos. As a result of the pre-test, some changes were made in the terminology to facilitate the respondents' understanding. In addition, the number of items per variable was reduced to a minimum to keep the questionnaire to an adequate size. The final version of the questionnaire was made available online to companies via Google Drive LimeSurvey for 4 months, finishing in March 2020 before the outbreak of COVID-19 pandemic in Mozambique. Several rounds of emails were sent to the firms, complemented with telephone calls, to increase the response rate.

This research is grounded on the information obtained with the help of the National Institute of Statistics of Mozambique and with the support of the Investment and Export Promotion Agency (APIEX) of Mozambique, which provided a database of 400 exporting SMEs. Over the course of the survey, 305 responses were obtained. However, 55 questionnaires with incomplete answers were excluded, and a set of 250 questionnaires with complete answers was obtained, constituting 62.5% of the total sample. The characteristics of the sample are presented in Table 2. The sample size is considered suitable for data analysis using partial least squares structural equation modeling (PLS-SEM) (Hair et al. 2011).

**Table 2.** Characterization of respondents.

Source: Own preparation.

As this research is based on quantitative approaches, it needs to be supported with reliability and validity analysis to ensure replicability and generalizability. While reliability is concerned with the consistency of measurements, validity is related to the extent to which the study reflects the social phenomena being studied. With reliable and valid measurement, it is possible to replicate the study (Wahyuni 2012).

The statistical analysis of the data was carried out using partial least squares structural equation models (PLS-SEM), using SmartPLS 3.2. This methodology was justified because its results are robust and because PLS-SEM supports linear regression equations that explain both linear and moderation effects when researchers seek to test and validate exploratory models (Henseler and Chin 2010).

#### **4. Results**

The evaluation of the models was based on reliability, convergent and discriminant validity. Tables 3–6 present the factor loadings of the items, which were obtained through bootstrapping with 5000 interactions, the average variance explained (AVE), and composite reliability (CR) for the different constructs under analysis. All items have loadings equal to or greater than the recommended minimum threshold of 0.7 (Götz et al. 2010) and items below this were removed.

**Table 3.** Loadings, AVE, CR and Cronbach's alpha of export performance.

Source: Own preparation. Scale adapted from Jantunen et al. (2005); Kuivalainen et al. (2007); Aulakh et al. (2000); Zou and Stan (1998).

Source: Own preparation. Scale adapted from Shu et al. (2019); Li and Atuahene-Gima (2001).

Discriminant validity is shown in Table 7 using the Fornell–Lacker criterion. It is clear that the square root of AVE is larger than the correlation values of the two variables under analysis (Hair et al. 2011).

In order to test the four hypotheses put forward, seven different modes were tested, as shown in Table 8, considering export performance as the dependent variable. Model 1 tests the direct effect of innovation capabilities on export performance. Models 2, 4, and 6 test government institutional support, reactive internationalization behavior, and active internationalization behavior as antecedents of export performance, respectively. Models 3, 5, and 7 test the moderating effects of government institutional support, reactive internationalization behavior and active internationalization behavior on the relationship between innovation capabilities and export performance.

**Table 5.** Loadings, AVE, CR, and Cronbach's alpha of first order innovation capability construct.

Source: Own preparation. Scale adapted from Guan and Ma (2003); Ribau et al. (2017b).

**Table 6.** Loadings, AVE, CR, and Cronbach's alpha of first order innovation capability construct.

Source: Own preparation. Scale adapted from Westhead et al. (2004).

#### **Table 7.** Discriminant validity.

Note: The values of the diagonal (in bold) are the square root of AVE.

**Table 8.** Summary of the regression analyses.

Source: Own preparation. Dependent variable: Export performance.

The results presented in Table 8 for model 1 indicate that the correlation coefficient is high (β = 0.501) and explains 25.1% of the export performance in model 1, i.e., (R2 = 0.251), thus confirming previous literature (Guan and Ma 2003; Vicente et al. 2015; Oura et al. 2016; Ribau et al. 2017a) and hypothesis 1.

Model 2 in Table 8 measures the effect of government institutional support as an antecedent of export performance. The results indicate that the explanation of model 2 rises from 25.1% (R2 = 0.251) to 29.6% (R2 = 0.296), i.e., government institutional support plays an important role (β = 0.225) as an antecedent of export performance (Peng and Heath 1996; Szeto and Kim 2018).

Model 3 presents the mediating relationship of government institutional support in the relationship between innovative capabilities and export performance. A stagnation of the indicators can be noted with regard to the explanation of the models as R2 presents a marginal increase from 29.6% to 29.8%, as presented in Table 8. Although Yi et al. (2013) and Stephan et al. (2015) advocate the importance of the role of government institutional support, the result of this research confirms that the moderating effect is almost null (β = −0.042) and is not statistically significant. Therefore, contrary to that expected, the moderating effect of SMEs' relationship with the government is non-existent. According to Yi et al. (2013), this effect is only positive and significant in contexts where the level of marketing activities among firms is high. In Mozambique, the marketing dimension is considered to be incipient, despite several firms already defining their target customers and adjusting products/services in response to the market (Ministério da Indústria e Comércio 2016). Meanwhile, it is confirmed that Mozambican SMEs are supported by the government. This is done either through the Institute for the Promotion of Small and Medium Enterprises (IPEME) through Decree no. 47/2008, of 3 December, as the public entity that has the responsibility not only to ensure the implementation of the Strategy for the Development, promotion and dynamization of Micro, Small and Enterprises (MSMEs), or by the Agency for the Promotion of Investment and Exports (APIEX), created through Decree no. 60/2016, of 12 December, the objective of which is to promote and facilitate private, public investment and exports, in accordance with the objectives and goals of the government's economic policy. However, this effort is not significant as SMEs still face problems such as regulatory barriers, lack of financing, high tax burdens and costs of procedures, and poor access to international markets.

On the other hand, this relationship ceases to make sense in regions where governments are corrupt (Qian 1996; Yi et al. 2013). Corruption in developing countries is a hindrance to the growth of SMEs. In Mozambique, in particular, SMEs are the most confronted with bribes and other corrupt practices because they are less equipped to defend themselves and or turn to politicians (Ministério da Indústria e Comércio 2016). Clearly, if the relationship with the government is far from ideal, which can be justified by the lack of resources of Mozambican SMEs, as well as the inadequacy of support for SMEs that have difficulty competing in international markets, then government institutional support is lost.

The result of the moderating effect of the mediation of government institutional support tested in model 3 is presented in Figure 2 where it can be seen that as the innovation capability increases the marginal increase in export performance of firms with lower and higher relationship with the government is practically null.

Models 4 and 5 present the relationship between reactive internationalization behavior, export performance, and the mediating effect of reactive internationalization behavior on the relationship between ICs and export performance, respectively.

The results of model 4 indicate that innovation capabilities and reactive internationalization behavior explain 48.2% (R2 = 0.482) of export performance, and there is a positive and statistically significant relationship between reactive behavior and export performance (β = 0.421), confirming what is postulated in the literature (Bruyat and Julien 2001; Westhead et al. 2004; Ribau et al. 2017a).

Model 5 shows the moderating effect of reactive behavior between innovation skills and export performance, where it is found that the R2 did not increase relative to model 4 (R2 = 0.482) and that the moderating effect is negative and statistically significant (β = −0.228). Thus, as shown in Figure 3, although firms with weak innovation capacity increase their export performance as they react to requests from international markets, firms that are truly innovative end up not benefiting from their export potential, confirming the results presented by Ribau et al. (2017a), as reactive SMEs do not rely on innovation to compete in international markets.

**Figure 2.** Graphic representation of the moderating effect of government institutional support on the relationship between innovation capabilities and export performance.

**Figure 3.** Graphic representation of the moderating effect of reactive internationalization strategy behavior on the relationship between innovation capabilities and export performance.

The results of model 6 indicate that the relationship between active internationalization behavior and export performance is positive and statistically significant (β = 0.547), so the higher the proactive internationalization behavior, the higher the export performance. Therefore, the previous literature is confirmed (Bruyat and Julien 2001; Westhead et al. 2004; Ribau et al. 2017a).

The moderating effect of proactive behavior does not confirm the hypothesis 3b raised, since, although it is statistically significant, it has a negative effect on moderating the relationship between ICs and export performance (β = −0.129). Thus, as the innovation skills of Mozambican SMEs increase, as well as their proactive behavior, their export performance also increases. However, as shown in Figure 4, while firms with weak innovation skills increase their export performance by using active internationalization strategies, firms with higher innovation skills do not benefit from this active internationalization strategy.

**Figure 4.** Graphic representation of the moderating effect of active internationalization strategy behavior on the relationship between innovation capabilities and export performance.

Table 8 presents the results of this research. The first hypothesis (H1) confirms that the direct effect between ICs and export performance is statistically significant (β = 0.501; *p* < 0.000). Thus, H1 is validated. Although the relationship between government institutional support and export performance is statistically significant, the moderating effect of government institutional support on the relationship between ICs and export performance was not shown to be statistically significant (β = −0.042; *p* = 0.464). Thus, the second hypothesis H2 cannot be validated. The third hypothesis (H3a) confirms that the moderating effect of reactive internationalization behavior between ICs and export performance is statistically significant, however, the sign is negative (β = −0.228; *p* = 0.000), thus rejecting hypothesis H3a. Finally, the fourth hypothesis (H3b) confirms that the moderating effect of proactive behavior between ICs and export performance is statistically significant, however, the sign is also negative (β = −0.129; *p* = 0.001), thus rejecting hypothesis H3b.

#### **5. Discussion**

The results of model 1 confirm the importance of ICs to Mozambican SMEs confirming previous studies (Guan and Ma 2003; Oura et al. 2016; Ribau et al. 2017a; Vicente et al. 2015). Thus, it is possible to state that exporting firms from emerging countries depend on their ICs to leverage their export performance. Another important aspect is that the moderating effect of government relationship, which is highly relevant in several contexts (Comi and Resmini 2020; Malca et al. 2020; Mota et al. 2021; Stephan et al. 2015; Yi et al. 2013), is not statistically significant, being close to zero (β = −0.042) in the Mozambican case. Thus, as presented in Figure 2, although export performance improves as innovation skills improve, the marginal difference in export performance is slightly lower

as innovation skills improve for higher levels of government relationship. As such, the effect of the government relationship on the relationship between innovation skills and export performance remains virtually unchanged for increasing levels of government relationship.

As seen in Figures 3 and 4 and models 5 and 7 in Table 8, the moderating effect of active and reactive internationalization behaviors of Mozambican firms on the relationship between ICs and export performance is negative and statistically significant. Thus, it can be stated that as innovation skills increase, export performance increases marginally, though much more for firms with relatively modest active strategies than for firms with high levels of active internationalization. This may indicate that Mozambican exporting firms that are more active in international markets face problems penetrating international markets and do not improve their performance as ICs increase. Similarly, with passive internationalization, it is found that with increasing innovation skills, the performance of Mozambican firms decreases with higher levels of reactive exporting strategies, although export performance increases with increasing innovation skills for low levels of reactive exporting behavior. This clearly indicates that Mozambican SMEs may not be benefiting from their innovation skills when trying to push their products into international markets with reactive strategies. Likewise, it can be seen that Mozambican SMEs have difficulty in implementing active internationalization strategies, which may indicate that international competitiveness is not properly assured, despite investment and effort put into improving their innovation capabilities.

One issue is clear: Mozambican SMEs face clear resource constraints to competing in larger international markets, especially because investment in innovation is much more risky and is clearly hampered by the lack of government aid.

#### **6. Conclusions**

This research analyzes the moderating effect of government support and active/reactive internationalization of Mozambican SMEs in the relation between ICs and export performance. The target population of the study, 250 Mozambican SMEs, contributed to the literature on SME internationalization in the context of an emerging country. This study was undertaken on the premise that in the context where SMEs carry out their activities, the support that the government provides and active/reactive behavior can influence the relationship between ICs and export performance. Its originality stems from the fact that it deals with how ICs impact export performance in emerging countries, and the effect of government support and active/reactive internationalization strategies on the relationship between ICs and export performance. Both aspects are novel among emerging countries, namely in Mozambique.

Although most Mozambican SMEs, as in other emerging economies, particularly in sub-Saharan Africa, have structural problems, low productivity levels, little modern technology and, as a consequence, low product/service quality, Mozambican exporting SMEs have shown that the use of ICs leads to good export performance.

Paradoxically, government support to SMEs is nonexistent, as is the moderating effect of the relationship of SMEs with the government, i.e., as this effort is not significant, SMEs continue to face problems such as regulatory barriers, lack of funding, high tax burden and cost of procedures, and poor access to international markets. As such, Mozambican SMEs, despite improving their ICs, are not able to leverage their innovation improvements with the government programs available to them. This ends up hindering the normal growth of SMEs, especially in emerging countries whose domestic resources are very limited.

The lack of resources is a general characteristic of SMEs. In the case of emerging economies, this lack of resources is even more prevalent. Although Mozambican SMEs invest in their innovation skills, these are not having the desired effect on export performance because, on the one hand, the lack of resources often makes active internationalization strategies impossible and, on the other hand, government support is non-existent, which does not benefit the internationalization effort undertaken by many companies. It is not enough just to develop internal ICs, it is also necessary to operationalize proactive strategies

so that SMEs can face international competitiveness, which requires clear and sustainable public policies and state support, able to make up for the companies' lack of pressing resources. However, the priority of governments in most emerging countries is oriented towards solving immediate socio-economic problems such as the percentage of the population with low survival rates and high corruption rates. In this context, SMEs aspiring to international business projects find themselves alone, relying on their own resources and capabilities, strangled by an unenthusiastic domestic market.

The proactive or reactive international strategy of Mozambican SMEs only benefits less innovative SMEs. In this context, it can be stated that Mozambican SMEs with high levels of innovation have some difficulty in competing internationally, not because of their lack of ICs, but because of the lack of resources, and perhaps because riskier investments are needed to compete within broader international horizons. This is possible only with effective public governmental business-support policies from the governments of emerging countries.

Clearly, at the government level, the challenge is clear: without governmental support, international business performance is compromised, especially because SMEs in emerging countries need to overcome the liability of newness that they face in international markets by increasing business risk, which ends up hindering active innovation development to implement internationalization strategies. On the other hand, the need for financial resources to adapt the firm's product portfolio may compromise the success in international competitive markets and relegate Mozambican and emerging countries' SMEs to the much less demanding domestic market, without solving the lack of competitiveness at the international level. Thus, the great challenge that many emerging economies have to overcome is to create clear government support for companies in order to support them to get to know and compete in international markets through support that increases their innovative capabilities.

At the business level, it is recommended that Mozambican SMEs, in particular, and those in emerging countries, in general, continue to invest in their ICs so that they can improve their internal processes and the development of new products and manufacturing skills, which will support them in marketing and implementing competitive strategies in wider markets. The development of ICs appears to be core to improving international competitiveness. They should also be more demanding of governmental institutions, demanding clear policies to support business competitiveness.

The main limitation of this study is that its content is based on the responses of 250 companies from a single emerging country, Mozambique, which could be complemented by samples from other emerging countries that could give a broader perspective. As it is a cross-sectional study, the intrinsic characteristics of a longitudinal research were not considered. The fact that only one informant per company was considered, and that it was not possible to compare several industrial sectors, may also be considered limitations. However, in the Mozambican context, this would be very difficult to implement. In addition to the limitations presented, future research should take into account the analysis of the context of the internationalization of Mozambican companies, especially their modes of entry and their main international competitors, as well as the qualifications of Mozambican managers and their degree of knowledge of the international context.

**Author Contributions:** Conceptualization, A.M., E.N. and C.R.; methodology, A.M., E.N. and C.R.; validation, A.M., E.N. and C.R.; formal analysis, A.M., E.N. and C.R.; investigation, E.N.; data curation, A.M. and E.N.; writing—original draft preparation, E.N.; writing—review and editing, A.M., E.N. and C.R.; supervision, A.M. and C.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

Olsson, Annika, Carl Wadell, Per Odenrick, and Margareta Bergendahl. 2010. An action learning method for increased innovation capability in organisations. *Action Learning: Research and Practice* 7: 167–79. [CrossRef]

Oura, Mauricio, Silvia Zilber, and Evandro Lopes. 2016. Innovation capacity, international experience and export performance of SMEs in Brazil. *International Business Review* 25: 921–32. [CrossRef]


Section 2 Digital Twins

## **Chapter 3**

## 3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

*Yunus Egi and Engin Eyceyurt*

## **Abstract**

Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS's efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system's main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.

**Keywords:** point cloud, image processing, direct geo-referencing, machine learning, radio networks, signal strength path loss

### **1. Introduction**

In recent years, the number of the mobile subscriber has reached 5.11 billion worldwide with a 2% increase [1]. This is due to the fact that available technologies such as LTE and 5G are in use in smart cities to communicate and share the data

through video conferencing, online shopping, working from home, smart transportation and etc. In addition, Covid-19 lockdowns cause 70% increase in internet usage and 12% increase in streaming [2]. Accessibility of higher throughput transfers in uplink and downlink led to accelerating the digitization process of new technological advancements including Internet of Things (IoT), cloud computing, big data analysis, and machine learning [3]. The digitization process is crucial in terms of creating smart cities. As the usage of LTE and 5G technologies increases in smart cities, the low-cost broadcast systems designed for different terrains become more critical. Lands containing mountains, hills, vegetation, and human-made structures are considered irregular terrains in telecommunication due to their complex structure and surface pattern. However, vegetation has more impact on wireless communication since leaves and branches cause faster absorption of signal strength through multi-path reflection [4, 5]. Empirical propagation models such as Free space, Log-Normal (LN), and Cost-231 Hata models are limited by the environment selection which may lead to inaccurate results in SSPL predictions [6, 7]. In order to avoid faulty results, it is essential to take into account both the elevation and the terrain shapes in SSPL calculations. For instance, in **Figure 1**, ignoring the tree canopies falling into the Fresnel zone between the transmitter and the receiver affects the performance of the transmitted signal and causes false path loss estimation as the main signal has interference from the tree canopy. It is not possible to physically distinguish between vegetation and the rest of the surrounding environment in practical applications. When equipped with a geo-referenced satellite image and a corresponding geo-referenced 3D point cloud, it becomes possible to create virtual twin environment and extract the features of the environment to predict SSPL as seen in **Figure 2**. In airborne LiDAR, Geographical Mobile Mapping System (Geo-MMS), the GPS and IMU provide the exact location and orientation of airborne LiDAR [8]. The LIDAR sends laser pulses to objects on the earth and collects the reflected pulses from the environment. The distances are found by calculating the time delay (*μ*) between the transmitted and received laser pulses [9]. The obtained geo-referenced data points form a 3D point cloud that will give the surface's property, such as the height and width of the obstacles. However, since these features will not be sufficient to classify trees from the environment, 2D satellite imagery will be used. The 2D satellite image has a colorful and classifiable character. We can use this feature to extract trees from the 3D image by classifying them. This process is illustrated in **Figure 3**.

**Figure 1.** *Illustration of non line-of-sight (NLOS) propagation.*

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

**Figure 2.** *Obtaining 3D point cloud using airborne LiDAR.*

**Figure 3.** *3D Point cloud vegetation classification process.*

Machine learning determines the learning patterns and builds a general decision algorithm by training some portion of data and is one of the most important parameters of smart cities. In this research, Artificial Neural Network (ANN) which is one of the most widely used machine learning algorithms will be used to build an adaptive SSPL model by using features obtained from 3D virtual environment. ANN consists of an input layer, hidden layer, and output layer [10]. A simplified illustration of an ANN is represented in **Figure 4**. A simple ANN with only one hidden unit is shown in **Figure 4**. In the input layer, the data (*x*1, *x*2, *::*, *xn*) and randomly initiated weights (*θ*1, *θ*2, *::*, *θn*) are multiplied and transmitted to the hidden layer, also known as the activation layer. In the hidden layer, a bias unit is added to the sum of the processed data, and the result goes through an activation function. A non-linear sigmoid function is usually used as an activation function and assigns 1 or 0 based on a threshold. In the next step, a gradient descent algorithm is used to adjust the connection weights between neurons. Gradient descent calculates errors between predicted and real values and finds the neuron

**Figure 4.** *Simple Presentation of a Perceptron.*

weights that minimize the error by iteratively taking the gradients. This section will include an example using ANN with multiple hidden layers to estimate the SSPL of tree canopies. In this study, a powerful SSPL model has been created by using LiDAR and machine learning to solve MCS's aforementioned problems. The proposed model, having a combination of ANN and 3D point cloud, applies to largeand small-scale applications.

## **2. Related work**

Many comprehensive studies have been conducted on Wireless Sensor Networks due to the continuous growth of mobile communication in smart cities. The studies mostly focus on power consumption and system accuracy [11]. In each study, different environments were tested over various aspects with image classification. However, image classification usage is not limited to a specific area but can be seen in almost all areas of studies. Juheon et al. performed an extensive study on the classification of individual tree species with LiDAR and deep learning [12]. Similarly, Torabzadeh et al. also studied tree species classification in forests with a combination of spectroscopy and airborne LiDAR [13] while Hartling et al. focused on urban tree species recognition and classification [14]. Image classification is a complex technique that finds space in many research areas. Image processing plays a crucial role in path loss analysis. In his study, Thrane aims to find the impact of buildings and multi-path propagation path loss of predefined signals [15]. He collects the signal attenuation measurements between the transmitter and receiver located in many different positions. After a 2D satellite image of the measurement area is obtained via Google Maps and rotated versions of images are prepared, the path loss effects of buildings are estimated with image classification and deep learning techniques. He states that 1 dB to 4.7 dB improvement factor in path loss prediction with his path loss model compared to empirical models. Likewise, the research conducted by Ahmadien et al. demonstrates a path loss model with Kmean clustering, deep learning techniques, and 3D images that converted from 2D satellite images via various simulation software [16].  Gracchi et al. uses a 3D point cloud taken by LiDAR to optimize WSN installation. High-resolution 3D point cloud data is analyzed to find a clear line of sight. She has validated her simulated 3D version of the model with the LiDAR-based 3D point cloud visualization [17]. The next section will provide 3D image segmentation using geo-referenced satellite images.

## **3. Visualization of Trees Using Image Classification and 3D point Cloud**

## **3.1 Direct geo-referencing and multi-sensor fusion**

Direct geo-referencing is a highly efficient and accurate technique used to determine the location and direction of a Geo-MMS. Measurements of external orientation parameters such as altitude, orientation angles, and distances are used in geo-referencing. A cameraless illustration of the Geo-MMS system is shown in **Figure 5**.

Downward directed LiDAR under the plane takes rotational scans while IMU and GPS take separate measurements. The direct geo-referencing of a ground vector on the surface is computed by Eq. (1) [18].

$$\stackrel{\rightarrow^a}{X}\_k^a = \stackrel{\rightarrow^a}{X\_b} + R\_b^a \left[ R\_L^b R\_k^L \overline{\hat{\mathbf{x}}^L\_k} + \Delta \overline{\hat{\mathbf{x}}^L\_b} \right];\tag{1}$$

Where: *X* !*a k*: The ground vector (*ath* frame), *Ra L* and *Rb L*: Rotation matrix and boresight rotation, *RL k* ð Þ *θ*, *η* : The LiDAR mirror rotation, *x* !*L k* and Δ*x* !*L b* : Slant range of the LiDAR and boresight-shifts. The *RL k* ð Þ *θ*, *η* is the function of the angle (*θ*) between ground and target. The angle (*η*) between ground and laser's X direction is calculated as follows.

$$R\_k^L(\theta,\eta) = \begin{bmatrix} \cos\theta & -\sin\theta\sin\eta & -\sin\theta\cos\eta \\ 0 & \cos\eta & -\sin\eta \\ \sin\theta & -\cos\theta\sin\eta & \cos\theta\sin\eta \end{bmatrix} \tag{2}$$

Kalman filter supported Inertial Navigation System (INS) geo-locates the data received by LiDAR and IMU by GPS data. A sequential adjustment is required since

**Figure 5.** *Obtaining 3D point cloud using airborne LiDAR.*

**Figure 6.** *2D satellite image of Florida Tech neighborhood.*

each sensor operates in different frequencies. After synchronization is ensured and direct geo-referencing is complete, geo-referenced IMU and LiDAR data points are combined to visualize a 3D point cloud. An example of a 3D point cloud that belongs to the Lane County Mental Health service neighborhood located in Oregon is illustrated in **Figure 6** [19]. The LiDAR used in this example is operating at 70 kHz scanning speed and 1 cm resolution. The data is interpolated to fill the unmeasured spots and minimize the measurement error via Natural Neighbor Interpolation (NNI) [20]. It is also seen that the LiDAR data is geo-referenced in the x and y direction as longitude and latitude, respectively.

The corresponding 2D satellite image is also required for 2D/3D image fusion. The Google Map is utilized to extract the required geo-referenced 2D satellite image as represented in **Figure 7**.

### **3.2 Color classification for visualization of trees**

Image classification is implemented to visualize necessary and informative properties using various methods through image processing. In this section, we apply a color-based classification using density and LAB color space. The images in RGB format are converted into LAB images since they are not suitable for digital manipulation. The three channels of LAB color space L, a, and b must be evaluated separately. After this process, with the help of Eq. (3), the intended color on the image will be picked to create a binary mask that will provide the average color of each channel falling on the selected mask on the image [21].

$$\mu = \left(\frac{1}{m \times n} \sum (m, n) P\_{\text{mask}}(m, n)\right) \times Ones\_{m \times n} \tag{3}$$

Find :*μmaskL*, *μmaska*, *μmaskb*

Relevant masks such as *mumask*, *mumaska* and *mumaskb* will be computed for each channel. The next step is to find the difference (Δ) between the masks and the channel as indicated in Eq.(4).

 $\Delta L\_{M \times N} = L - \mu\_{mask}$
$$\text{, } \Delta L\_{M \times N} = a - \mu\_{mask} \tag{4}$$

$$\text{, } \Delta L\_{M \times N} = b - \mu\_{mask}$$

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

**Figure 7.** *2D satellite image of Lane County Mental Health service.*

Since the masks alone represent only the drawn area, calculating the Euclidean distance for all three channels with the formula Δ*EM*�*N* will reveal the color values closest to the masked part of the image as seen in Eq. (5).

$$
\Delta E\_{M \times N} = \sqrt{\left(\Delta L\right)^2 + \left(\Delta a\right)^2 + \left(\Delta b\right)^2} \tag{5}
$$

To obtain an efficient classification, the color estimation (Δ*EM*) should fall in the 95% Confidence Interval (CI). This is essential since sharp color gradations within the area without tolerance can cause some areas to disappear. Thus, 95% CI should be applied to results by adding 3*σ* as seen in Eq.(6).

$$CI = \mu(\Delta E\_{masked}) + \mathbf{\mathcal{G}}\sigma(\Delta E\_{Masked}) \tag{6}$$

Next, CI is applied to the Δ*E* to test Δ*E*< ¼ *CI* values. If the condition is correct, logic one is assigned to this value. If not, logic 0 is assigned. The logical image containing zeros and ones is represented in **Figure 8a**. After implementing the logical image to the original image, the classified trees are obtained on a 2D satellite image. The classified image is demonstrated in **Figure 8b**. Since we aim to classify trees on the 3D point cloud, we can use that classified image to filter out undesired parts of the 3D point cloud other than trees, see **Figure 8c**. The results are shown in **Figure 9**.

**Figure 8.**

*3D Filtering process of tree canopies.*

**Figure 9.**

*Obtained extracted vegetation and trees from the environment.*

## **4. Empirical models and proposed smart deployment technique**

The signal's strength weakens when the signal encounters obstacles and loses energy due to multi-path reflection and absorption. The sum of the power loss and the signal path is called the signal strength path loss, which is decisive when deploying MCS. Therefore, many researchers have created path loss models such as Free-Space, Log-Normal, and Cost231-Hata models. Since each model emerges from experiments at a specific location, it has unique approaches specific to that location. In this research, we want to create a model supported by artificial intelligence and can fit in any location by getting out of location-oriented models that can be considered a disadvantage. All models, including the model to be obtained, were compared with each other. This model has been validated using the Mean Absolute Percent Error (MAPE).

## **4.1 Free-Space Path Loss Model**

The free space path loss (FSPL) model determines the attenuation between the transmitter and the receiver in an unobstructed path. This phenomenon is indicated by the Friis transmission formula, as indicated in Eq.(7) [22].

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

$$FSPL = 10\log\left(\frac{P\_t}{P\_r}\right) = 10\log\left(\frac{\left(4\pi d\right)^2}{\lambda^2 G\_t G\_r}\right) \tag{7}$$

Where: *λ*: Wavelength, *Gt*: Gain of the transmitting antenna, *Gr*: Gain of the receiving antenna, *d*: Separation between transmitter and receiver.

#### **4.2 Log-Normal Shadowing Path Loss Model**

Log-normal shadowing (LNS) is the extended version of the Friis formula, which includes obstacles to the free space. It is a frequently used model for longrange propagation [23]. Because of the shadowing effect, the LNS model comprises Additive white Gaussian Noise (AWGN) represented as *Xσ* [24]. The LNS model is demonstrated in Eq. (8).

$$PL\_{L\text{NIS}}[dB] = PL(d\_0) + \mathbf{10}\eta \log\left(\frac{d}{d\_0}\right) + X\_\sigma \tag{8}$$

Where: *PL d*ð Þ0 : The path loss at *d*0, *η*: Path loss exponent, *Xσ*: *N*ð Þ 0, *σ* Normal distribution with zero mean. LNS model has the following environments and Path loss exponents, as shown in **Table 1**.

#### **4.3 Cost231-Hata model**

Cost 231-Hata model is an SSPL model that takes the Okumura Hata model to a more diverse frequency range (1500–2000 MHz). This model can be used mainly for urban, suburbans, and open areas. The Cost 231-Hata model is indicated in Eq. (9) [25].

$$\begin{aligned} \text{PL}\_{C-23}[dB] &= 46.3 + 33.9 \log \left( f \right) - \text{13.82} \log \left( h\_{\text{B}} \right) \\ &- a(h\_{\text{R}} f) + (44.9 - 6.55 \log \left( h\_{\text{B}} \right)) \log \left( d \right) + C \end{aligned} \quad \text{(9)}$$

$$\text{Where } a(h\_{\text{R}} f) = (\mathbf{1.1} \log \left( f \right) - \mathbf{0.7}) h\_{\text{R}} - (\mathbf{1.56} \log \left( f \right) - \text{0.8})$$

$$\text{C} = \begin{cases} \text{0 dB for Subturban areas} \\ \text{3 dB for Unban areas} \end{cases}$$

#### **4.4 Proposed smart MCS deployment Technique**

To design an intelligent broadcasting model for the deployment of MCS, the impact of vegetation must carefully be defined in the LOS direction. Towards LOS, the signal will be attenuated by transmission across vegetation due to reflections and absorption. Therefore, trees are measured as a highly complex obstruction in the environment from a telecommunication perspective. This phenomenon is expressed in **Figure 10**.

**Table 1.**

*Predefined Path loss exponent (η) for different environments.*

**Figure 10.** *Illustration of tree canopy path loss system.*

This model is based on signal loss occurring on tree canopies (*PLtree*) through scattering and absorption. In addition, considering *Pa* and *Ps* as the tree loss factor *PLtree*, the corresponding signal strength loss is calculated as follows [26].

$$\text{SSPL}\_{\text{tree}} = \text{FSPL} + \text{PL}\_{\text{tree}} \tag{10}$$

In the literature, analysis of tree SSPL is a challenging ongoing research question. Even though the research numbers are going up, there are still a few consistent results due to the complex structure of the surveying area. This section extracts the features by means of image color classification and uses those features to reveal the required tree canopy path loss and add its effect to the FSPL through the ML algorithm. Since absorption and scattering are positively associated with the tree canopy's height and width, our algorithm will use these properties and estimate the required signal strength path losses.

#### *4.4.1 Experiment and Model Presentation of Tree Canopy Path loss: case study*

In this section, a experiment conducted by Egi et al. [18]. will be evaluated. From his study, the necessary training data were collected using a 40 m Mini Handheld Digital Laser Range Finder, LG G5 mobile phone. The application used for this experiment is a network activity application called Network Cell Info (by Wilysis). The phone is fixed in the LOS direction of the transmitter. To calculate *PLtree*, the data is taken from the front and back sides of the tree canopy and subtracted from each other. This difference corresponds to the *PLtree* caused by the scattering and absorption. This procedure is replicated in various places for different sizes of tree canopies. Since the data obtained are raw, feature normalization is applied to cause the gradient descent to converge faster. Mathematically explained, normalization is to subtract each feature's mean value from each item of the corresponding feature and scale the feature according to its standard deviation. Normalized data are used as input in our proposed model, as seen in **Figure 11**.

In this part, *x*1ð Þ *height* and *x*2ð Þ *width* are normalized and fed to the ANN. As the data pass through the perceptrons, they are multiplied by randomly initialized coefficients, called weights, *θl* . At each layer, bias units (+1) are added to the data, which contribute to ANN's outcome by modifying the activation functions [27]. The parameters *x*1, *x*2 and, +1 with 200 elements are the input layers. The second layer is *3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

**Figure 11.** *Proposed ANN presentation.*

the hidden layer consisting of 30 hidden units. The third layer is the output layer, which makes decisions based on the height and width taken from trees.

## *4.4.2 Calculating Cost Function Using Forward-propagation*

In ANN, the data travels from the input layer to the output layer to make predictions. Since the propagation is only one direction towards forward, it is called forward-propagation. The data are exposed to weights and non-linear sigmoid functions throughout propagation to add non-linearity into the estimation model. A regularization parameter can also be used to increase the prediction accuracy [27]. After the prediction through forward-propagation, the cost is computed to measure the performance of the ANN model. The cost is calculated by cost the function which determines the error between real and predicted values. The cost function may differ based on the purpose of ANN. In this chapter, we use the logistic regression cost function, as seen in Eq. (11).

$$f(\boldsymbol{\theta}) = \frac{1}{m} \sum\_{i=1}^{m} \sum\_{k=1}^{K=3} \left[ -\boldsymbol{\mathcal{y}}\_{k}^{(i)} \log \left( \left( \boldsymbol{\mathcal{P}} \boldsymbol{L}\_{\text{tree}} \left( \mathbf{x}^{(i)} \right) \right)\_{k} \right) - \left( \mathbf{1} - \boldsymbol{\mathcal{y}}\_{k}^{(i)} \right) \log \left( \mathbf{1} - \left( \boldsymbol{\mathcal{P}} \boldsymbol{L}\_{\text{tree}} \left( \mathbf{x}^{(i)} \right)\_{k} \right) \right) + \boldsymbol{\mathcal{Y}}\_{k}^{(i)} \right] \tag{11}$$

$$\frac{\lambda}{2m} \left[ \sum\_{j=1}^{30} \sum\_{k=1}^{2} \left( \boldsymbol{\theta}\_{j,k}^{(1)} \right)^{2} + \sum\_{j=1}^{3} \sum\_{k=1}^{30} \left( \boldsymbol{\theta}\_{j,k}^{(1)} \right)^{2} \right]$$

where: *PLtree x*ð Þ*i* � � is the last activation function, K and m are the number possible outcomes and number of labels respectively, y is the observed outcome, *θ*'s are the weights and *λ* is regularization parameter. The regulation parameter, *λ*, is

used to prevent over-fitting [26]. Random initial weights are required to break the symmetry and to utilize each hidden unit. Initial weights should be given in the �*εinit*, *εinit* ½ � range to keep the parameters small and increase the learning efficiency. The formula for the required *εinit* is given in Eq. (12).

$$
\varepsilon\_{init} = \frac{\sqrt{\mathsf{G}}}{\sqrt{L\_{in} + L\_{out}}} \tag{12}
$$

Where: *Lin and Lout* are number of units in adjacent layers. After forwardpropagation process with *λ* ¼ 0*:*01, the cost, *J*, is found as 2.052.

#### *4.4.3 Back-propagation*

Unlike forward-propagation, the back propagation propagates backward from the output layer to the input layer. While doing that, back-propagation computes gradients (*g*0 ð Þ*z l* ) in every step towards backward. Gradient reveals *δ j* � � changes in hidden layers. The subscript *J* indicates the number of iterations and changes regularly with each iteration in the back-propagation algorithm. This process is used for the optimization of the cost function. The sigmoid gradient is defined as follows:

$$sigmoid(z) = \mathbf{g}(z) = \frac{1}{1 + e^{-z}} \tag{13}$$

$$\mathbf{g}'(\mathbf{z}) = \frac{d}{dz}\mathbf{g}(\mathbf{z}) = \mathbf{g}(\mathbf{z})(\mathbf{1} - \mathbf{g}(\mathbf{z})) \tag{14}$$

In the model, ð Þ *delta* errors given in Eq. (15) cause deviation and must be calculated in every layer [26].

$$\begin{array}{rcl} \boldsymbol{\delta}\_{j}^{(3)} = \boldsymbol{a}\_{j}^{(3)} - \boldsymbol{y}\_{i} \rightarrow & \text{Output} & \text{layer} \end{array}$$

$$\boldsymbol{\delta}\_{j}^{(2)} = \left(\boldsymbol{\theta}^{2}\right)^{T} \boldsymbol{\delta}^{(3)} .\* \mathbf{g}' \left(\mathbf{z}^{2}\right) \rightarrow & \text{Hidden} \end{array} \tag{15}$$

$$\boldsymbol{\Delta}^{l} = \boldsymbol{\Delta}^{l} + \boldsymbol{\delta}^{(l+1)} \left(\boldsymbol{a}^{(l)}\right)^{T} \rightarrow & \text{Adding}$$

The outcome of Δ*l* is an unreqularized gradient of the ANN cost function, which should be divided by *m* (total number of samples). During the process, it updates the *θ* value for all j's simultaneously. After training the data with a hundred forward and back-propagation, the cost was reduced from 2.052 to 0.636, resulting in an accuracy of 94.5% in signal strength estimation. Since the predicted accuracy is reached with our ANN algorithm, we apply our algorithm in the direction of LOS to the detected trees. Tree canopies are detected through the Local Maximum Method (LMM) and median filter processes.

#### *4.4.4 Implementation of Local Maximum Method for detection of tree canopies*

In order to determine the required parameter, the local maximums in the 3D point cloud must be calculated with some hypothetical constraints such as using 3 � 3 Median Filter, setting tree heights, width and peak to peak distance greater or equal to 1.6 m, 2 m, and 2 m, respectively. This will maximize the accuracy of the model by avoiding many deceptive local maxima. Local maxima are calculated using the following Eq. (16).

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

$$\circlearrowleft \, f(\mathbf{x}, y) > f\left(\mathbf{x}\_{nn}, y\_{nn}\right) \tag{16}$$

Where: *f x*ð Þ , *y* is a pixel of an image, *f xnn*, *ynn* � � are the neighborhood pixels of the f(x,y). By completing the training of the ANN, *LOS SSPL* ð *tree* formula is obtained for LOS direction:

$$LOS(\text{SSPL}\_{tree}[dB]) = FSPL[dB] + \sum\_{j=0}^{n\_{\text{tree}}} \left( \overrightarrow{PL}\_{tree\_j}(h\_j, w\_j) \right) \tag{17}$$

Where: *P* ! *Ltree j* : Predicted tree path loss, *h j*: Height of the detected tree canopy, *w j*: Width of the detected tree canopy.

### *4.4.5 Model Validation with MAPE*

Mean Absolute Percent Error (MAPE) is a statistical method to measure the prediction accuracy of models. MAPE determines the differences between real and theoretical values. Later it divides this difference by the real values. Next, the absolute values of the results are averaged and represented as a percentage. MAPE is determined by Eq. (18) [28].

$$LOS(\text{MAPE}[\%]) = \frac{\mathbf{100\%}}{n} \sum\_{i=1}^{n} \left| \frac{\text{Models} - \text{PL}\_{real}}{\text{PL}\_{real}} \right| \tag{18}$$

### **5. Analysis and results of a case study**

This study is based on a comparison of four models, including *PLtree*. The 3D environment obtained by airborne LiDAR belongs to the Florida Institute of Technology neighborhood. Since the data has some faulty values, the natural neighbor interpolation is performed on the raw 3D point cloud. To create a colorful 3D image, the geo-referenced 2D satellite image is imported via Google API and merged with the 3D point cloud. Later, the tree canopies are located by the local maxima method on a 2D image. This 2D classified image helped us extract and locate trees on the 3D point cloud. The process of extracting tree canopies from the 3D point cloud is shown in **Figure 12**.

While the detected trees are marked with red �, the labeled transmitter and receiver belonging to MCS are marked with yellow + signs. Using the LMM technique and limitations, the height and width of five trees were plotted. The proposed ANN algorithm will be used to estimate the required *PLtree* value for each tree canopy detected in the LOS direction, where the characteristics of the environment such as height and width are obtained. The properties of five trees, such as distance, width, height, and *PLtree* are shown in **Table 2**. It is seen from **Table 2**, there is a correlation between tree size and estimated *PLtree*. This is because the complex structure of the tree canopies causes more reflection and absorption in proportion to the tree's size. The detected features from the environment are used as input data for the ANN model, and compared with the other empirical models with the same parameters. The results are listed in **Table 3**. It is seen that the energy demand of LNPL and Cost231 models increased with the distance exponentially, but they could not provide the required signal power considering the real values. This is because traditional propagation models assume that terrains have the same characteristics when it comes to terrain pattern. For this reason, they deviate significantly in terms

#### **Figure 12.**

*3D Filtering process of tree canopies.*

of SSPL and RSL estimations. The performance of *SSPLtree* is also shown in **Figure 13**. According to **Figure 13**, All models have a strong relationship in terms of distance and SSPL. However, unlike other models, the tree SPPL is discerning itself by showing a peak anytime propagation encounter a tree in the LOS direction. To see whether the model is performed well or not, RSL measurement should be compared with real values. In this case study, RSL values are taken through transmitter and receiver facing each other with an operating frequency of 2110 MHz. The devices have effective radiated power of -1 dB. All empirical models and SSPL tree predicts the RSL results operating with these parameters. The results are

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

#### **Table 2.**

*Detected features and obtained* PLtree*.*

#### **Table 3.**

*Comparison of Tree Canopy Path loss with empirical models.*

**Figure 13.** *Comparison of 4 Models with respect to distance.*

presented in **Figure 14**. The RSL values are significant in terms of maintaining the communication between transmitter and receiver. That is why it is essential to keep the predicted values as close as the real values. In **Figure 14**, it is indicated that the red line, which is tree RSL (*RSLtree*), has a similar track with the black line, which is measurement RSL(*mRSL*). This proves that the ANN aided model has overperformed among all empirical models. MAPE results also validate these results. From **Table 4**, *RSLtree* have a minimum deviation of 4.26% in terms of MAPE percentage while others result stayed in between 6.29% to 16.9%.

**Figure 14.** *RSL Comparison of Models and Measured values with respect to distance.*

#### **Table 4.**

*The MAPE performance of the models.*

AI aided RSL tree model with the a deviation of 4.26% has a significant improvement compared to other empirical models since all the micro-variations contribute to the estimates. In addition, unpredictability of tree variations [29] was overcome using artificial intelligence. The proposed model result was also outperformed compare to A. Alsayyari et al. case study with MAPE results of % 34.37 and %19.80 [30].

### **6. Conclusions**

This chapter demonstrated the fusion of state of art technologies that can potentially contribute to developing an intelligent environment for smarter cities. Sensor fusion, UAV, satellite image, and image classification have integrated for the purpose of creating a 3D virtual environment for a realistic data platform. The obtained information is crucial in terms of the evaluation of the planned projects for futuristic cities. In our case, we have assessed the effect of trees upon smart deployment of MCS using 3D point cloud, which is basically the 3D virtual presentation of the city, to maintain connectivity and efficiency. Since tree canopies are considered irregular terrains and their complex structure highly affect the efficiency of SSPL due to multi-path reflection, we extracted tree canopies by using 2D color classified satellite image as a filter. By means of extracted 3D point cloud

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

information of tree canopies, the features (height and width) are obtained for the purpose of training the ANN. The data and results that are taken from Egi et al.'s case study are a good demonstration of the model application. The analysis shows that the created smart model can significantly affect the MCS' propagation since it adds all micro-variations and utilizes tree features for adjustment of RSL. The MAPE results for all models are obtained as 4.26%, 10.16%, 6.29%, and 16.9% error for *PLtree*, FSPL model, log-normal model, and the Cost 231-Hata model, respectively. It should also be pointed out that the ANN model did not consider the effect of the buildings. This effect may be added to the model in future applications. The primary contribution of this chapter is to create a colorful 3D virtual environment and make more precise feature extraction possible. This technique may also shape the future of smart cities by using digitized information for city planning, communication planning, and infrastructure planning. It should be noted that the proposed model is only applicable to outdoor applications since the 3D virtual environment only provides outdoor information. This limitation can be also removed if the LiDAR scanning is performed indoors and combined with the outdoor data.

## **Author details**

Yunus Egi1,2\*† and Engin Eyceyurt2†

1 Sirnak University, Sirnak, Turkey

2 Florida Institute of Technology, Melbourne, FL, USA

\*Address all correspondence to<EMAIL_ADDRESS>
† These authors contributed equally.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Shirowzhan S, Tan W, Sepasgozar SME. Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities. ISPRS Int. J. Geo-Inf. 2020; **9**:240. DOI: https://doi.org/10.3390/ ijgi9040240

[2] Stoll C. Michael Arthur Mehling, COVID-19: Clinching the Climate Opportunity. One Earth. 2020;**3**(4): 400-404, ISSN 2590-3322. DOI: https:// doi.org/10.1016/j.oneear.2020.09.003

[3] Qi Q, Tao F, Hu T, Anwer N, Liu A. Yongli Wei. A.Y.C. Nee, Enabling technologies and tools for digital twin, Journal of Manufacturing Systems: Lihui Wang; 2019 ISSN 0278-6125

[4] Shanker Y, Lobiyal DK. Role of earth bulge on the prediction of radio propagation path loss over irregular terrain. International Journal of Wireless and Mobile Computing. 2020; **18**(4):352-360

[5] Carpenter, John, et al. Investigations and analyses of LTE network cell tower deployments and impact on path loss calculations. MITRE CORP BEDFORD MA BEDFORD United States, 2019.

[6] Mir Ghoraishi, Jun-ichi Takada, Tetsuro Imai,"Radio Wave Propagation Through Vegetation" 17 01 2019. [online].Available:http://cdn.intechopen. com/pdfs/42732/InTech-Radiowave propagationthroughvegetation.pdf.

[7] Ribero, MÃ3 nica, et al. "Deep learning propagation models over irregular terrain." ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.

[8] Getis A. Introduction to Geography: Earth sciences. CTI Reviews: Physical geography; 2016

[9] Cramer M et al. ULTRA-HIGH PRECISION UAV-BASED LIDAR AND DENSE IMAGE MATCHING. Remote Sensing Spatial Information Sciences: International Archives of the Photogrammetry; 2018

[10] Rodrigo Fernandes de Mello; Moacir Antonelli Ponti, Machine Learning"A practical approach on statistical Learning Theory", Sao Paulo: Springer, 2018.

[11] Sherazi, Hafiz Husnain Raza, Luigi Alfredo Grieco, and Gennaro Boggia. "A comprehensive review on energy harvesting MAC protocols in WSNs: Challenges and tradeoffs." Ad Hoc Networks 71 (2018): 117–134.

[12] Juheon, et al. Individual tree species classification from airborne multisensor imagery using robust PCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016;**9**(6):2554-2567

[13] Torabzadeh, Hossein, et al. "Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning." Agricultural and Forest Meteorology 279 (2019): 107744.

[14] Hartling, Sean, et al. "Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning." Sensors 19.6 (2019): 1284. org/citation. cfm?doid=1711113.1711127. [Accessed 6 1 2018].

[15] Thrane J, Zibar D, Christiansen HL. Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz. Ieee Access. 2020;**8**:7925-7936

[16] Ahmadien O et al. Predicting Path Loss Distribution of an Area from Satellite Images Using Deep Learning. IEEE Access. 2020;**8**:64982-64991

*3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile… DOI: http://dx.doi.org/10.5772/intechopen.96179*

[17] Gracchi, Teresa, et al. "Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds." ISPRS International Journal of Geo-Information 8.10 (2019): 460.

[18] Egi Y, Otero CE. Machine-Learning and 3D Point-Cloud Based Signal Power Path Loss Model for the Deployment of Wireless Communication Systems. IEEE Access. 2019;**7**:42507-42517

[19] F. I. University, "International Hurricane Research Center," 12 12 2018. [Online]. Available:http://dpanther2.ad. fiu.edu /Lidar/lidarNew.php.

[20] Liu P et al. Fusion of color histogram and LBP-based features for texture image retrieval and classification. Information Sciences. 2017;**390**:95-111

[21] MATLAB, "Segmentation methods in image processing and analysis," MATLAB, 18 June 2018. [Online]. Available:https://www. mathworks.com/

[22] Breinbjerg, Olav, and Kyriakos Kaslis. "On the accuracy of Friis' transmission formula at short range." 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). IEEE, 2017.

[23] MacCartney, George R., and Theodore S. Rappaport. "Study on 3GPP rural macrocell path loss models for millimeter wave wireless communications." 2017 IEEE International Conference on Communications (ICC). IEEE, 2017.

[24] Silvia Demetri ; Gian Pietro Picco ; Lorenzo Bruzzone, "Estimating Lowpower Radio Signal Attenuation in Forests: A LiDAR-based Approach," in Distributed Computing in Sensor Systems (DCOSS), 2015 International Conference on, Cambridge, 2015.

[25] Braga, Allan Dos S., et al. Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path. IEEE Access. 2020;**8**:146395-146407

[26] de Jong YLC, Herben MHAJ. A treescattering model for improved propagation prediction in urban microcells. in IEEE Transactions on Vehicular Technology. March 2004; **53**(2):503-513. DOI: 10.1109/ TVT.2004.823493

[27] A. Ng, "Machine Learning lecture 4," 14 01 2019. [Online]. Available: https://www.coursera.org/learn/ machine-learning.

[28] Olasupo TO, Otero CE, Olasupo KO, Kostanic I. Empirical Path Loss Models for Wireless Sensor Network Deployments in Short and Tall Natural Grass Environments. in IEEE Transactions on Antennas and Propagation. Sept. 2016;**64**(9): 4012-4021

[29] de Jong YLC, Herben MHAJ. A treescattering model for improved propagation prediction in urban microcells. in IEEE Transactions on Vehicular Technology. March 2004; **53**(2):503-513. DOI: 10.1109/ TVT.2004.823493

[30] A. Alsayyari, I. Kostanic, C. E. Otero and A. Aldosary, "An empirical path loss model for wireless sensor network deployment in a dense tree environment," 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, NJ, 2017, pp. 1–6, doi: 10.1109/SAS.2017.7894099.

## **Chapter 4**