sensors Article

Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections Leyre Azpilicueta 1, *, Peio López-Iturri 2 , Erik Aguirre 2 , Carlos Martínez 2 , José Javier Astrain 3 , Jesús Villadangos 3 and Francisco Falcone 2 1 2

3

*

School of Engineering and Sciences, Tecnologico de Monterrey, Tecnologico 64849, Mexico Electrical and Electronic Engineering Department, Public University of Navarre, Pamplona 31006, Spain; [email protected] (P.L.-I.); [email protected] (E.A.); [email protected] (C.M.); [email protected] (F.F.) Mathematical Engineering and Computer Science Department, Institute of Smart Cities, Public University of Navarre, Pamplona 31006, Spain; [email protected] (J.J.A.); [email protected] (J.V.) Correspondence: [email protected]; Tel.: +52-81-582-082

Academic Editor: Stefano Mariani Received: 31 March 2016; Accepted: 11 July 2016; Published: 22 July 2016

Abstract: With the growing demand of Intelligent Transportation Systems (ITS) for safer and more efficient transportation, research on and development of such vehicular communication systems have increased considerably in the last years. The use of wireless networks in vehicular environments has grown exponentially. However, it is highly important to analyze radio propagation prior to the deployment of a wireless sensor network in such complex scenarios. In this work, the radio wave characterization for ISM 2.4 GHz and 5 GHz Wireless Sensor Networks (WSNs) deployed taking advantage of the existence of traffic light infrastructure has been assessed. By means of an in-house developed 3D ray launching algorithm, the impact of topology as well as urban morphology of the environment has been analyzed, emulating the realistic operation in the framework of the scenario. The complexity of the scenario, which is an intersection city area with traffic lights, vehicles, people, buildings, vegetation and urban environment, makes necessary the channel characterization with accurate models before the deployment of wireless networks. A measurement campaign has been conducted emulating the interaction of the system, in the vicinity of pedestrians as well as nearby vehicles. A real time interactive application has been developed and tested in order to visualize and monitor traffic as well as pedestrian user location and behavior. Results show that the use of deterministic tools in WSN deployment can aid in providing optimal layouts in terms of coverage, capacity and energy efficiency of the network. Keywords: traffic lights; propagation; wireless sensor networks; ray launching

1. Introduction In the past two decades, the number of vehicles circulating in cities has increased considerably. This fact, together with the tendency of citizens to cluster has caused congestion of roads and junctions, especially in cities with large populations. The existing infrastructures have become inefficient to optimize traffic. The traditional traffic control system based on traffic lights depending on preset date and time patterns is not efficient in reducing waiting times and energy consumption. In fact, simulations show that adaptive traffic control based on information from Wireless Sensor Networks (WSNs) can be improved by 65% in normal traffic conditions compared to traffic management by preset patterns [1]. Since the nineties, there has been extensive research looking for different solutions in Intelligent Transportation Systems (ITS), with the objective of improving and performing traffic

Sensors 2016, 16, 1140; doi:10.3390/s16071140

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management. ITS consist of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) over wireless communications links, contributing to safety improvements and environmentally friendly driving. The ITS road infrastructure has the potential to enhance road safety by helping drivers avoid collisions during basic maneuvers, changing lanes, merging on highways, and driving safely in blind turns. The first implemented ITS were based on counting vehicles through magnetic sensors or artificial vision systems. However, these systems were not completely efficient because line of sight conditions must be fulfilled. Magnetometers sensors based on the detection of magnetic fields on the road lanes have been used to detect the presence of vehicles. These sensors can detect the number of vehicles and their speed, and send their data to the nearest Intersection Control Agent (ICA) which, determines the flow model of the intersection depending on sensors’ data, using ZigBee technology [2,3]. For the network architecture, there have been several proposals, ranging from a two sensors architecture [4,5] to several sensors with an ad-hoc network [6,7]. In the past few years, due to the advancement of wireless communications systems, there are new possibilities to manage traffic based on vehicle communications, which make it easier and more accurate than traditional approaches of detecting and counting vehicles. By adding short-range wireless communication capabilities to vehicles, the vehicles form mobile ad-hoc networks. These are referred to in the literature as Vehicular Ad-Hoc Networks (VANETs). Traffic safety is the main focus of current research on VANETs and the main motivation of deploying this technology making it ubiquitous. However, there are a number of other applications that could improve the way we drive today. In the future, the main goal for vehicles is to gather sensor data and share information on traffic dynamics with each other and with the road infrastructure. Traffic lights are regarded as one of the most effective ways to alleviate traffic congestion. However, traditional traffic lights cannot meet the challenges in traffic regulation posed by the fast growing number of vehicles and increasing complexity of road conditions. Because of that, there are many works in the literature that deal with this problem. The work presented in [8] designs an adaptive traffic light system based on short-range wireless communication between vehicles, showing clear benefits compared to adaptive systems based on sensors or cameras. In reference [9], an intelligent dynamic traffic light sequence using radio frequency identification (RFID), which avoids problems that usually arise with standard traffic control systems, especially those related to image processing and beam interruption techniques is described. In [10], a new real-time traffic monitoring is proposed, based on a cluster V2X traffic data collection mechanism, which is able to gather more than 99% of the available data and reduce the overhead to one quarter when compared to other approaches. An intelligent traffic control system to pass emergency vehicles smoothly based on RFID and ZigBee technologies is presented in [11]. In [12], a ZigBee-based wireless system is also presented to assist traffic flow on arterial urban roads. The work proposed in [13] describes a dynamic traffic regulation method based on virtual traffic light for VANETs. The results demonstrate the viability of their solution in reducing waiting time and improving the traffic efficiency. The operation of WSN is clearly dependent upon having adequate signal levels at the distributed nodes. When designing any wireless network, a significant issue to be considered is the maximum distance between two nodes that still ensures a reliable wireless connection. This depends on a broad range of parameters such as the transmitter power, the receiver sensitivity, the signal propagation environment, the signal frequency and the antennas parameters. In addition, the main challenge for vehicular communications is the rapidly changing radio propagation conditions. Both the transmitter and the receiver can be mobile, and the scattering environment can rapidly change. Due to this fact, the proper design of a WSN for use in these type of environments could be a very challenging process [14]. There are several papers in the literature that present WSN placement optimization mechanisms. In [15] a mechanism for deploying a minimum number of sensors to cover all targets that are randomly placed in a grid environment is presented. Reference [16] proposed a node deployment strategy for blindness avoiding in WSNs on the basis of ant colony optimization. The paper [17] investigates the problem of minimizing the total cost of deploying roadside units and sensor nodes along the

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two sides and the median island of a two-lane road to cover the whole road, and to form a connected VANET-sensor network. Reference [18] provides analytical modeling for IEEE 802.15.4-compliant WSN in vehicular communications, as well as guide design decisions and tradeoffs for WSN-based VANET applications. In [19], a numerical functional extreme model is proposed for searching the minimum exposure path in WSNs with a hybrid genetic algorithm. Reference [20] also proposes an addressing-based routing optimization scheme for IPv6 over low-power wireless personal area network in vehicular scenarios. Different communication channel modeling approaches can be roughly split into four groups. The first one includes the empirical methods, which were used traditionally for initial coverage estimation (such as COST-231, Walfish-Bertoni, Okumura-Hata, etc.). Their advantage is that they provide rapid results, but they require calibration based on measurements to give an adequate fit of the results based on initial regression methods. The second group are the stochastic channel models (narrowband or wideband) [21], which characterize the channel from a frequency selective perspective. The third group includes the geometry-based stochastic models, which are the most widely used for propagation prediction in mobile communication channels, e.g., the work presented in [22] presents a V2V geometrical channel model considering that the local scatters move with random velocities in random directions. In [23], a new geometric-stochastic channel modeling is presented in which the delay-dependent Doppler probability density functions (pdfs) are derived for general V2V propagation environments, to cope with the non-stationarity of these channels. In reference [24], Pätzold and Borhani presented a non-stationary multipath fading channel model incorporating the effect of velocity variations of the mobile station. Reference [25] presents an adaptive reduced-rank estimation of non-stationary time-variant channels using subspace selection. However, none of the approaches described above consider all the elements of the environment, and because of that, they could fail in specific situations when the surroundings have a great impact on the electromagnetic propagation, such as a complex intersection in a large city, which could have vegetation, different type of scatters including different types of traffic lights, buildings, walking citizens, different materials, etc. Because of that, the fourth group of channel modeling approaches corresponds to deterministic methods, which are widely used for propagation prediction given a specific environment. They can be roughly divided into two categories: those which are based on ray optics (ray launching (RL) or ray tracing (RT) techniques) or the full-wave simulation techniques based on solving the Maxwell’s equations (method of moment (MoM), finite difference time domain (FDTD), etc.). These methods are precise but are time-consuming due to inherent computational complexity. Thus, methods based on geometrical optics (ray launching or ray tracing) offer a reasonable trade-off between precision and required calculation time, for radio planning purposes, with strong diffractive elements [26]. The main difference between the RL and RT techniques is that in the RL technique, rays are launched from a transmitter and, at the locations where rays intersect and object; the new reflected, transmitted, diffracted, or scattered rays begin. On the other hand, in the RT approach, the number of possible paths that rays follow from the transmitter to the receiver are encountered by imaging techniques. In this work, deterministic modeling, specifically a RL technique, has been used to characterize the physical channel for radio planning purposes in an urban area for a context-aware system that benefits from traffic light infrastructure, given the ease of deployment as well as the vicinity to users under analysis, i.e., pedestrians as well as vehicles. Such system can obtain information in relation with pedestrian behavior (e.g., density of users in pedestrian crossings, pedestrian speed, incidentals in pedestrian movement), vehicle behavior or ambient information, among others. One of the main goals is to model the radio wave propagation adequately in order to optimize the distance between devices in an actual network deployment, by means of an in-house generated application which has been tested under real conditions. A real time monitoring application has been implemented in order to provide context-aware interactivity within the scenario under analysis, in relation with pedestrian/vehicle movement as well as environmental information which can be correlated with pedestrian/vehicle behavior. This paper is divided into the following sections: Section 2 describes the

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simulation procedure for the channel characterization of the considered scenarios and the scenarios description. 3 shows simulation results such as bi-dimensional planes of received power, Sensors 2016, 16, Section 1140 4 of 25 power delay profiles and delay spread, among others. Section 4 presents the experimental-setup in the real scenario and the comparison between simulation and measurements. Section 5 describes the radio planning forfor different technologies (ZigBee, Bluetooth and the and 802.11p standard). planninganalysis analysis different technologies (ZigBee, Bluetooth the radio 802.11p radio Section 6 describes proposed application within thewithin considered scenario,scenario, followedfollowed by the last standard). Section 6 the describes the proposed application the considered by section concluding remarks.remarks. the last presenting section presenting concluding 2. RayLaunching LaunchingTechnique Technique and and Simulation Simulation Scenarios Scenarios 2. Ray 2.1. Ray Launching Launching Technique Technique 2.1. Ray A A deterministic deterministic method method based based on on an an in-house in-house developed developed 3-D 3-D RL RL code code has has been been used used to to analyze analyze the radio electric behavior of the considered scenario. The 3D RL algorithm is based on Geometrical the radio electric behavior of the considered scenario. The 3D RL algorithm is based on Geometrical Optics Theory of of Diffraction Diffraction (GTD). (GTD). It It has has been been verified verified in Optics (GO) (GO) and and Geometrical Geometrical Theory in the the literature literature with with different such as as the the analysis analysis of of wireless wireless propagation propagation in in closed closed environments environments [27–30], [27–30], different applications, applications, such interference main interference analysis analysis [31] [31] or or electromagnetic electromagnetic dosimetry dosimetry evaluation evaluation in in wireless wireless systems systems [32]. [32]. The The main principle principle of of the the RL RL techniques techniques is is to to identify identify aa single single point point on on the the wave wave front front of of the the radiated radiated wave wave with a ray that propagates in the space following a combination of optic and electromagnetic theories. with a ray that propagates in the space following a combination of optic and electromagnetic This phenomenon is illustrated in Figurein1Figure for the1considered scenario. Each ray propagates in the theories. This phenomenon is illustrated for the considered scenario. Each ray propagates space a single optic ray. The incident electricelectric field (Efield by an antenna at a distance r in ther i ) created in the as space as a single optic ray. The incident (Ei) created by an antenna at a distance free space can be calculated by [33]: in the free space can be calculated by [33]: c Kk Ei

“∥ =

jβ 0 r Prad Dt pθ(t , φ, t q η)0 e´ e X Kk∥LKk∥ 2π2π r

(1) (1)

where Prad is the radiated power with a directivity D¯t pθt , φt q, where the sub-index t refers to ´ is the radiated power with a directivity ( , ), where the where ? sub-index t refers to the K k the transmitted angle, and polarization ratio X , X∥ . β 0 “ 2π f c ε 0 µ0 , ε 0 “ 8.854 ˆ 10´12 , transmitted angle, and polarization ratio ( , ) . = 2π , = 8.854 × 10 , ´7 ∥ the path loss coefficients for each polarization. The parameter j µ0 = “ 4π ˆ 120π. LKk are × 10 10 and andη0 “ = 120π. are the path loss coefficients for each polarization. The in Equationj in (1)Equation refers to the complex number. parameter (1) refers to the complex number.

Figure 1. 1. Wave the Figure Wave front front propagation propagation with with rays rays associated associated with with single single wave wave front front points points in in the considered scenario. considered scenario.

When the rays impinge with an obstacle in their path, a reflected and a transmitted ray are When the rays impinge with an obstacle in their path, a reflected and a transmitted ray are created with new angles provided by Snell’s law [34]. It is important to take into account that the created with new angles provided by Snell’s law [34]. It is important to take into account that the rays rays considered in the GO only approach are only direct, reflected and refracted rays, leading to the considered in the GO only approach are only direct, reflected and refracted rays, leading to the existence existence of abrupt areas, which correspond to the boundaries of the regions where these rays exist. of abrupt areas, which correspond to the boundaries of the regions where these rays exist. Because of Because of that, the diffracted rays are introduced with the GTD and its uniform extension, the Uniform GTD (UTD). The main purpose of these diffracted rays is to remove field discontinuities and to introduce proper field corrections, especially in the zero-field regions predicted by GO. The diffracted field is calculated by [35]:

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that, the diffracted rays are introduced with the GTD and its uniform extension, the Uniform GTD (UTD). The16,main Sensors 2016, 1140 purpose of these diffracted rays is to remove field discontinuities and to introduce 5 of 25 proper field corrections, especially in the zero-field regions predicted by GO. The diffracted field is calculated by [35]: c e´ jks1 Kk ∥ s1 ´ jks2 (2) EUTD “=e0 D (2) ( +s q)e s1 s2 ps1 ` 2 where the receiver receiver point, point, where s1 ,, s2 are arethe thedistances distancesfrom fromthe thesource source to to the the edge edge and and from from the the edge edge to to the K k ∥ respectively. D are respectively. are the the diffraction diffraction coefficients coefficientsgiven givenby by[35,36]. [35,36]. The RL algorithm is performed three-dimensionally, The RL algorithm is performed three-dimensionally, with with angular angular resolution resolution (horizontal (horizontal and and vertical planes) in a predefined solid angle that considers the radiation diagram of the vertical planes) in a predefined solid angle that considers the radiation diagram of the transceivers transceivers sources. Spatial is also defined by a uniform hexahedral mesh. Parameters such as frequency sources. Spatialresolution resolution is also defined by a uniform hexahedral mesh. Parameters such as of operation, radiation patterns of the antennas, number of multipath reflections, separation angle frequency of operation, radiation patterns of the antennas, number of multipath reflections, between rays, andbetween cuboid dimension be taken into account. all the material properties for separation angle rays, and can cuboid dimension can beBesides, taken into account. Besides, all the all the elements within the scenario can also be considered, given the dielectric constant and the loss material properties for all the elements within the scenario can also be considered, given the tangent at constant the frequency range of tangent operation thefrequency system under analysis. When aofray an dielectric and the loss atofthe range of operation theimpacts systemwith under obstacle, reflection, refraction diffraction will occur, depending on the geometry and will the electric analysis. When a ray impactsand with an obstacle, reflection, refraction and diffraction occur, properties of the object, as is depicted in Figure 2. depending on the geometry and the electric properties of the object, as is depicted in Figure 2.

Figure 2. Schematic representation of the principle of operation of the in-house developed Figure 2. Schematic representation of the principle of operation of the in-house developed 3D 3D algorithm. RL RL algorithm.

2.2. Simulation Scenario Description 2.2. Simulation Scenario Description The simulation scenario implemented for calculation by means of the in-house developed 3D The simulation scenario implemented for calculation by means of the in-house developed 3D RL RL code consists of an outdoor environment of an urban city area, placed at the old town of code consists of an outdoor environment of an urban city area, placed at the old town of Pamplona Pamplona (Navarre, Spain). The considered scenario consists of an outdoor environment with trees (Navarre, Spain). The considered scenario consists of an outdoor environment with trees of different of different heights, grass, junctions of roads, cars, people and different types of buildings. This heights, grass, junctions of roads, cars, people and different types of buildings. This environment environment can be considered as really complex, in terms of radio wave propagation, because it has can be considered as really complex, in terms of radio wave propagation, because it has a broad a broad range of elements with different material properties within it. The dimensions of the range of elements with different material properties within it. The dimensions of the scenario are scenario are (150 m, 130 m, 30 m). Figure 3 presents a view of the real and schematic considered (150 m, 130 m, 30 m). Figure 3 presents a view of the real and schematic considered scenarios. scenarios. All the material properties for all the elements within the scenarios have been considered, given the All the material properties for all the elements within the scenarios have been considered, given dielectric constant and the loss tangent at the frequency range of operation of the system under analysis. the dielectric constant and the loss tangent at the frequency range of operation of the system under Taking into account that radio wave propagation in this scenario could change depending of the analysis. Taking into account that radio wave propagation in this scenario could change depending weather, as it is an outdoor environment, it is relevant to consider different conditions for the material of the weather, as it is an outdoor environment, it is relevant to consider different conditions for the properties of the vegetation. For that purpose, the values obtained in [37] for the material properties of material properties of the vegetation. For that purpose, the values obtained in [37] for the material the wood and the foliage of the vegetation have been used. A human body model [32], which take properties of the wood and the foliage of the vegetation have been used. A human body model [32], into account all the elements within the human body, and a car model [38], which also consider all the which take into account all the elements within the human body, and a car model [38], which also elements within a car, has also been used in the simulations. These models have been created to be consider all the elements within a car, has also been used in the simulations. These models have been created to be implemented in the in-house 3D RL algorithm. The material parameters used in the simulation are defined in Table 1 [37–40].

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implemented in the in-house 3D RL algorithm. The material parameters used in the simulation are Sensors 2016, 16, 1140 6 of 25 defined in Table 1 [37–40].

Figure 3. Real (left) and schematic (right) view of the considered scenario for simulation in the 3D Ray Figure 3. Real (left) and schematic (right) view of the considered scenario for simulation in the 3D Launching Algorithm. Ray Launching Algorithm. Table Table 1. 1. Material Material properties properties in in the the RL RL simulation. simulation.

Parameter Parameter Air Air Concrete Concrete Aluminum Aluminum Polypropylene Polypropylene Brick Brick Glass Glass Plasterboard Plasterboard Grass Grass Trunk TrunkTree Tree Tree Treefoliage foliage Human body model Human body model Car model Car model

Permittivity Permittivity (ε (εrr)) 1 1 25 25 4.5 4.5 33 4.44 4.44 6.06 6.06 2.02 2.02 30 30 [37] [37] [37] [37] [32] [32] [38] [38]

Conductivity Conductivity(σ) (σ)(S/m) (S/m) 0 0 0.02 0.02 7 7 44׈1010 0.11 0.11 0.11 0.11 ´12 10 10−12 00 0.01 0.01 [37] [37] [37] [37] [32] [32] [38] [38]

3. SimulationResults Results 3. Simulation Once hashas been defined, simulation results can becan obtained. These results Once the thesimulation simulationscenario scenario been defined, simulation results be obtained. These have been validated with real measurements which have been made in a campaign of measurements results have been validated with real measurements which have been made in a campaign of in the same scenario, is shownwhich in Section 4. measurements in the which same scenario, is shown in Section 4. For the simulations, 16 antennas have been placed in the scenario, one for each For the simulations, 16 antennas have been placed in considered the considered scenario, one fortraffic each light. The specific position of the antennas in the scenario is shown in Figure 4. To reduce interferences traffic light. The specific position of the antennas in the scenario is shown in Figure 4. To reduce with people and vehicles, antenna has placed cm above traffic light. The radiating interferences with people each and vehicles, eachbeen antenna has10been placedeach 10 cm above each traffic light. element is a wireless ZigBee mote which has been configured as a dipole, transmitting 18 dBm at The radiating element is a wireless ZigBee mote which has been configured as a dipole, transmitting 2.41 GHz. Simulation parameters are shown in Table 2. 18 dBm at 2.41 GHz. Simulation parameters are shown in Table 2. Table Table 2. 2. 3D 3D Ray Ray Launching Launching simulation simulation parameters. parameters.

Parameter Parameter Frequency Frequency Transmitted Power Level Transmitted Power Level Vertical plane angle resolution Vertical plane angle resolution ∆θ∆θ Horizontal plane angle resolution Horizontal plane angle resolution ∆ϕ∆φ Reflections Reflections Cuboids size Cuboids size

Value Value 2.41 GHz 2.41 GHz 1818dBm dBm 2°2˝ 2°2˝ 66 2 2mm

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Figure 4.4.Schematic view of the position of the different antennas within the considered scenario. Figure Schematic the different different antennas antennaswithin withinthe theconsidered consideredscenario. scenario. Figure4. Schematic view view of the position of the

Figures555and and shows distribution within the scenario for Figures and 66 shows power distribution within the considered considered scenario fordifferent different Figures 6 shows thethe power distribution within the considered scenario for different heights heights andand planes, and for for different sensorsinplaced in traffic be heights and planes, and placed in different different traffic lights. Asit itcan canthe beseen, seen,the the and planes, for different sensors placed different traffic lights. Aslights. it canAs be seen, influence influence of the the(like obstacles buildings, can be influence of obstacles (likestreetlights, the trees, streetlights, streetlights, buildings, etc.) can beeasily easilyappreciated. appreciated. Itisis of the obstacles the trees, buildings, etc.) can beetc.) easily appreciated. It is shownItthat shown that the the morphology morphology of considered scenario aanoticeable shown that well asofthe topology of the the considered scenariohas has noticeable the morphology as well as theas topology thetopology considered scenario has a noticeable impact on radio impact onradio radio wave wave propagation. propagation. impact on wave propagation.

(a) (a)

(b) (b)

(c) (d) (c) (d) Figure 5. Estimation of received power (dBm) on the considered scenario (XY planes) for different Figure Figure 5. 5. Estimation Estimation of of received received power power (dBm) (dBm) on on the the considered considered scenario scenario (XY (XY planes) planes) for for different different heights obtainedby by the3D 3D Ray Launching Launching algorithm (a) (a) 1.4 m height for the sensor #1; (b) 3 m height heights heights obtained obtained by the the 3D Ray Ray Launching algorithm algorithm (a) 1.4 1.4 m m height height for for the the sensor sensor #1; #1; (b) (b) 33 m m height height forsensor sensor#1; #1;(c) (c)1.4 1.4m mheight heightfor for sensor sensor #6; #6; (d) (d) 33 m m height height for sensor #6. for for sensor #6. for sensor #1; (c) 1.4 m height for sensor #6; (d) 3 m height for sensor #6.

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(a) (a)

(b) (b)

Figure 6.6. Estimation of received power (dBm) on the considered considered scenario(YZ (YZ planes)for for different Figure6. Estimation of of received received power power (dBm) (dBm) on Figure Estimation on the the considered scenario scenario (YZplanes) planes) fordifferent different distances of X obtained by the 3D Ray Launching algorithm (a) X = 73 m for the sensor#6; #6; distances of X obtained by the 3D Ray Launching algorithm (a) X = 73 m for the sensor distances of X obtained by the 3D Ray Launching algorithm (a) X = 73 m for the sensor #6; (b) X = 55 m (b) X = 55 m for sensor #9. (b)sensor X = 55#9. m for sensor #9. for

Figure of estimated estimated received received power power extracted extracted from fromthe thedata dataofof Figure 77 shows shows the the different different zones zones of Figure 7 shows the different zones of estimated received power extracted from the data of Figure power results results are are accurate accurate but but not not homogeneous, homogeneous,and and Figure 5a,c. 5a,c. ItIt can can be be seen seen that that received received power Figure 5a,c. It can be seen that received power results are accurate but not homogeneous, and because because important role. role. Different Differentvalues valuesof ofthe themean meanreceived received becauseof ofthat, that,the the radio radio planning planning task has an important of that, the radio planning task has an important role. Different values of the mean received power are power are obtained depending of the obstacle density of the different zones. Table 3 show the power are obtained depending density of the different zones. Table 3 show the obtained depending of the obstacle density of the different zones. Table 3 show the different values of different deviation and and the the obstacle obstacle density densityofofthe thezone zone different values values of of power power mean mean with its standard deviation power mean It with deviation and the obstacle of the zone considered. It canbut be itseen considered. be seen the obstacle hasdensity great influence influence in received receivedpower, power, but itisis considered. It can canits bestandard seen that that density has great in that the obstacle density has great influence in received power, but it is also relevant to consider also zone and and the the distance distance from fromthe thetransmitter transmitterthe alsorelevant relevant to to consider consider the the area area of the considered zone ofof area of the considered zone and the distance from the transmitter of each zone. From these results, each received power power in in different different points pointsofofthe the scenarios eachzone. zone. From From these these results, results, it is shown that received scenarios it is shown that received powerof inthe different pointsthe of obstacle the scenarios depends greatly of the and position depends greatly of transmitter, the obstacle density ofthe thedifferent different zones andthe the depends greatly of the the position position density of zones of the transmitter, the obstacle density of the different zones and the different material properties of differentmaterial material properties properties of of the obstacles. different the obstacles. An important important radioelectric radioelectric phenomenon in this An this type type of of environment environment isis given given by bymultipath multipath An important radioelectric phenomenon in this for type environment given by multipath propagation. To illustrate illustrate this fact, the scenario and the delay propagation. To this the delay spread spread for theofwhole whole scenariois and thepower power delay propagation. To illustrate this fact, the delay spread for the whole scenario and the power delay profile profilefor forthe the central central location location of the scenario has been profile been obtained obtained and and ititisisshown shownin inFigures Figures88and and9,9, for the centralThe location ofspread the scenario has been obtained is shown in Figures 8 and 9, the respectively. respectively. The delay spread as the time between the and respectively. delay has been defined theand timeitdelay delay between thefirst first and thelast lastray ray The delay spread has been defined as the time delay between the first and the last ray which arrives which arrives to each spatial point. It has been calculated using as threshold the noise floor, with which arrives to each spatial point. calculated using as threshold the noise floor, withatoa each spatial point. It has been calculated using as threshold the noise floor, with a value of ´100 dBm. value of −100 dBm. value of −100 dBm.

(a) (a)

Figure 7. Cont.

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(b) Figure 7.7.Estimation Estimationofofreceived received power (dBm) on the considered scenario (XY planes) divided by Figure power (dBm) on the considered scenario (XY planes) divided by zones zones obtained by the 3D Ray Launching algorithm (a) 1.4 m height for the sensor #1; (b) 1.4 m height obtained by the 3D Ray Launching algorithm (a) 1.4 m height for the sensor #1; (b) 1.4 m height for for sensor sensor #6. #6. Table 3. 3. Simulation Simulation results results divided divided by by zones. zones. Table Zone Zone

Area (m2) Area (m2 )

I II I III II IV III V IV VI V VI VII VII VIII VIII

700 875 700 3675 875 5600 3675 400 5600 4550 400 4550 2100 2100 1600 1600

−54.57 −53.27 ´54.57 −68.47 ´53.27 −47.86 ´68.47 −36.81 ´47.86 −57.28 ´36.81 ´57.28 −58.96 ´58.96 −52.27 ´52.27

I II I III II IV III IV V V VI VI VII VII

1400 9750 1400 4300 9750 800 4300 800 100 100 1750 1750 1400 1400

−65.10 −54.86 ´65.10 −45.50 ´54.86 −41.71 ´45.50 ´41.71 −35.51 ´35.51 −53.98 ´53.98 −63.28 ´63.28

Power Mean (dBm) Standard Deviation (dB) Obstacles Density (%) Power Mean (dBm) Standard Obstacles Density (%) CASE I Sensor #1 Deviation (dB) 1.96 CASE I Sensor #1 1.70 1.96 9.091.70 2.279.09 5.162.27 1.085.16 2.221.08 1.142.22 CASE II Sensor #6 1.14 CASE II Sensor 5.61 #6 1.645.61 1.461.64 1.811.46 2.941.81 3.032.94 7.123.03 7.12

0.43 0.57 0.43 0.04 0.57 0.86 0.04 0.10 0.86 0.02 0.10 0 0.02 0.26 0 0.26 0.80 0.38 0.80 0.14 0.38 0 0.14 0 0 0.59 0 0.59 0.43 0.43

From Figure 8 it can be seen that multipath phenomena have a lot of influence in the environment. It is8 itobserved thatthat themultipath delay spread is higher in athe closestintothe theenvironment. transmitter From Figure can be seen phenomena have lot areas of influence antenna and itthat is lower in other areasisbecause thethe radiation powertointhe these parts is smaller toitthe It is observed the delay spread higher in areas closest transmitter antennadue and is larger in distance. Eachbecause spatial the sample of thepower delayinspread is associated with power delaydistance. profile, lower other areas radiation these parts is smaller dueato the larger shown in Figure 9 for the delay central position of the scenario. it isdelay observed, theshown scenario is really Each spatial sample of the spread is associated with a As power profile, in Figure 9 complex and there are several echoes inAs the duethe to this multipath channel behavior. for the central position of the scenario. it scenario is observed, scenario is really complex and there are several echoes in the scenario due to this multipath channel behavior.

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(a) (a)

(b) (b)

Figure8.8.Estimation Estimation of delay delay spread (ns) (ns) on the Figure the considered considered scenario scenariofor fordifferent differentpositions positionsofof ofthe the Figure 8. Estimation of of delay spread spread (ns) on on the considered scenario for different positions the transmitterantenna antenna in in different different traffic traffic street street lights (a) 33 m height for sensor #5; (b) 3 3mmheight forfor transmitter lights (a) m height for sensor #5; (b) height transmitter antenna in different traffic street lights (a) 3 m height for sensor #5; (b) 3 m height for sensor#6. #6. sensor sensor #6.

Figure 9. Power Delay Profile at a given cuboid, located at the central location in the considered scenario. Figure 9. Power Power Delay DelayProfile Profileat ataagiven givencuboid, cuboid,located locatedat atthe thecentral centrallocation locationin inthe theconsidered consideredscenario. scenario.

As previously shown, a clear dependence on the topology and morphology of the scenario as As previously shown, clearisdependence dependence onmust the topology topology and morphology of the the scenario as wellAs as previously on the system parameters observed and be carefully considered in order to scenario assess theas shown, aa clear on the and morphology of well as on on the the system system parameters iswithin observed and must must be carefully carefully considered in order order to assess assess the the performance of multiple systemsis a particular scenario. Theseconsidered results allow the optimization well as parameters observed and be in to performance of multiple systems within a particular scenario. These results allow the optimization of the designofofmultiple the WSN since the designer can select the minimum numberallow of nodes required to performance systems within a particular scenario. These results the optimization of the design of the WSN since the designer can select the minimum number of nodes requiredtoto to grant a certain communication level, and the optimal emplacements of these nodes. It is important of the design of the WSN since the designer can select the minimum number of nodes required grant a certain communication level, and the optimal emplacements of these nodes. It is important to consider also that a larger density of nodes can lead to increased interference levels, which could grant a certain communication level, and the optimal emplacements of these nodes. It is important consider also that a larger to which could could degrade system performance. This of comment also applies in the caseinterference of changes levels, in the transmitted to consider also that a largerdensity density ofnodes nodescan canlead lead to increased increased interference levels, which degrade system performance. This comment also in case of changes changes in the the transmitted service, system associated with a certain allocated bandwidth and hence, in sensitivity levels. degrade performance. This comment also applies applies in the the changes case of in transmitted The computational time is also highly important when analyzing these complex scenarios. service, associated with a certain allocated bandwidth and hence, changes in sensitivity levels. service, associated with a certain allocated bandwidth and hence, changes in sensitivity levels. In thisThe case, simulations have been performed in an Intel Xeon CPU X5650 @ complex 6.67complex GHzscenarios. andscenarios. 2.66 GHz, The computational time also highly important when analyzing these In computational time isisalso highly important when analyzing these In this andcase, the simulations simulation time been ofCPU 43,537 s using software from this have been performed in an Intel Xeon CPU X5650 @ 6.67 GHz and 2.66 GHz, case, simulations have computational been performed in anhas Intel Xeon X5650 @ 6.67the GHz and 2.66Matlab GHz, and the Mathworks (Natick, computational MA, USA). It must behas pointed out that the input parameters have been chosen and the simulation time been of 43,537 s using the software Matlab from simulation computational time has been of 43,537 s using the software Matlab from Mathworks according to(Natick, a convergence analysis of the algorithm previously for different types ofbeen scenarios Mathworks It must bethat pointed out that thedone input parameters haveaccording chosen (Natick, MA, USA). ItMA, mustUSA). be pointed out the input parameters have been chosen to a [41]. according to a convergence analysis of the algorithm previously done for different types of scenarios convergence analysis of the algorithm previously done for different types of scenarios [41]. key parameter that is used in assessing systems that transmit digital data from one location to [41]. AAkey parameter that is used in assessing systems that transmit digital data from one location to another is parameter the Bit Error Rate (BER).inThe BER expression for QPSK modulation canfrom be calculated by: to A key is(BER). used assessing systems that transmit digital data one location another is the Bit Errorthat Rate The BER expression for QPSK modulation can be calculated by: another is the Bit Error Rate (BER). The BER expression (3) = a ( 2for⁄QPSK ) modulation can be calculated by: BERQPSK “ Qp 2Eb {N0 q (3) (3) = ( 2 ⁄ )

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⁄ b and andN N0 = The received power has been been where ⁄ and N0 0isisthe whereEb “=PRX {R thenoise noisevalue. value. The received received power power PRX has has beencalculated calculatedwith withthe the 3D RL algorithm for each spatial sample in the considered scenario. With these values of PRX , the 3D RL algorithm for each spatial sample in the considered scenario. With these values of , the BER has been calculated and it is shown in Figures 10 and 11, for different values of data rate ((Rb)) BER has been calculated and it is shown in Figures 10 and 11, for for different different values values of of data data rate rate ( ) and different different valuesofofN0 . It. It can seen the high variability between the different cases,higher with be be seen the high variability between the different cases, with and differentvalues values of . can It can be seen the high variability between the different cases, with higher of values of BERN0when isInhigher. In differences addition, differences between thedata different data rates values BER when is higher. addition, between the different rates considered higher values of BER when is higher. In addition, differences between the different data rates considered areleading observed, leading to a lower BER with for all with therate lowest data rateThese considered. are observed, to a lower BER all cases thecases lowest data considered. results considered are observed, leading to aforlower BER for all cases with the lowest data rate considered. These results can be in really helpful in order to optimize the design of and deployment of aon WSN can be really helpful order to optimize the design and deployment a WSN depending the These results can be really helpful in order to optimize the design and deployment of a WSN depending on the modulation, the used data rate and the level of . modulation, thethe used data rate and the level N0 .and the level of depending on modulation, the used dataofrate .

Figure 10. Bit Error Rate for QPSK modulation for different values ofof N for data rate of 250 Kbps. Figure10. 10.Bit BitError ErrorRate Ratefor forQPSK QPSKmodulation modulationfor fordifferent differentvalues valuesof Figure 0 for data rate of 250 Kbps.

Figure 11. Bit Error Rate for QPSK modulation for different values of for data rate of 57,600 bps. Figure fordata datarate rateof of57,600 57,600bps. bps. Figure 11. 11. Bit Bit Error Error Rate Rate for for QPSK QPSK modulation modulation for for different different values values of of N0 for

4. Experimental Setup 4. Experimental 4. Experimental Setup Setup In order to validate the results previously obtained, real measurements in the same scenario Inorder ordertotovalidate validate the results previously obtained, real measurements in thescenario same scenario In results previously obtained, real measurements in the shown shown in Figure 3 havethe been performed. A transmitter antenna, connected to asame signal generator at shown in Figure 3 have been performed. A transmitter antenna, connected to a signal generator at in Figure 3 have been performed. A transmitter antenna, connected to a signal generator at 2.41 GHz 2.41 GHz and 5.9 GHz has been located at the coordinates (X = 31 m, Y = 76.8 m, Z = 2.05 m), just 2.41 GHz and 5.9 GHz has been located at the coordinates (X = 31 m, Y = 76.8 m, Z = 2.05 m), just and 5.9 the GHztraffic has been located at theemployed coordinates (X = 31 m, Y = 76.8 Z = 2.05 m), just above the above light #1. The signal generator is am,portable N1996A (Agilent abovelight the#1. traffic light #1.signal The generator employedis asignal generator is a portable N1996A (Agilent traffic The employed portable N1996A (Agilent Technologies, Santa Clara, Technologies, Santa Clara, CA, USA) unit and the spectrum analyzer is an Agilent N9912 Field Fox. Technologies, Santa Clara, CA, USA) unit and theAgilent spectrum analyzer is an Agilent N9912 Field CA, USA) unit andare the spectrum analyzer is an N9912 Field The antennas used Fox. are The antennas used ACA-4HSRPP-2458 from Zentri (Los Gatos, CA,Fox. USA), both omnidirectional The antennas used are ACA-4HSRPP-2458 from Zentri (Los Gatos, CA, USA), both omnidirectional ACA-4HSRPP-2458 from Zentri (Los Gatos, CA, USA), both omnidirectional antennas, with a gain of antennas, with a gain of 0.3 dB for 2.41 GHz and a gain of 3.74 dB for 5.9 GHz. Measurements have antennas, withGHz a gain ofa0.3 dBoffor 2.41 GHz and a gain of 3.74 dB for 5.9 been GHz.performed Measurements have 0.3 dB for 2.41 and gain 3.74 dB for 5.9 GHz. Measurements have along the been performed along the measurement points depicted in Figure 12 each 5 m at a height of 1.4 m. been performed along the measurement in Figure 12m. each 5 m at a height of 1.4 m. measurement points depicted in Figure 12points each 5depicted m at a height of 1.4

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Figure12. 12. View Viewof of the the considered considered scenario scenario with with the the transmitter Figure transmitter position position and and the the measurement measurementpoints. points. Figure 12. View of the considered scenario with the transmitter position and the measurement points.

To visualize the frequency variation with time and the possible influence of pre-existent To To visualize visualize the the frequency frequency variation variation with time and and the the possible possible influence influence of pre-existent pre-existent interference in the scenario, a spectrogram has been measured before starting the measurement interference in the scenario, a spectrogram has been measured before starting starting the the measurement measurement campaign. Figure 13 shows the measured spectrogram for both frequencies in Max Hold mode, for campaign. forfor both frequencies in Max Hold mode, for the campaign. Figure Figure13 13shows showsthe themeasured measuredspectrogram spectrogram both frequencies in Max Hold mode, for the transmitter position represented in Figure 12 in the considered scenario, with the aid of the transmitter position represented in Figure 12 in the considered scenario, with the aid the of the the transmitter position represented in Figure 12 in the considered scenario, with aidAgilent of the Agilent N9912 Field Fox portable spectrum analyzer. N9912 FoxField portable spectrumspectrum analyzer.analyzer. AgilentField N9912 Fox portable

Figure 13. Measured spectrogram in the 2.41 GHz (top) and 5.9 GHz band (bottom). Figure 13.13. Measured spectrogram in the 2.41 GHz (top) and 5.95.9 GHz band (bottom). Figure Measured spectrogram in the 2.41 GHz (top) and GHz band (bottom).

It can be seen that the measured values can be considered as noise and there is not a significant It can be seen that the measured values can be considered as noise and there is not a significant pre-existent interference at the frequency bands of interest. It is also observed that the noise is pre-existent interference at the frequency bands of interest. It is also observed that the noise is

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It can be16,seen Sensors 2016, 1140that the measured values can be considered as noise and there is not a significant 13 of 25 pre-existent interference at the frequency bands of interest. It is also observed that the noise is slightly higher forhigher 5.9 GHz This is due to the factto the thegain antenna 5.9 GHzinis5.9 3.44 dB is slightly forfrequency. 5.9 GHz frequency. This is due thegain factofthe of theinantenna GHz higher than at 2.41 GHz 3.44 dB higher than at frequency. 2.41 GHz frequency. Figure 14 shows the comparison between simulation and measurement results for both frequencies. Figure 14 shows the comparison between simulation and measurement results for both Measurements were performed with 100 MHzwith bandwidth 2.41 GHz and 5.9GHz GHzand frequency. frequencies. Measurements were performed 100 MHzatbandwidth at 2.41 5.9 GHz The measurement time at each point 60 s,point and the received power by frequency. The measurement time was at each wasvalue 60 s,ofand the value of represented received power each point is the peak of shownpeak by the analyzer considered bandwidth represented byhighest each point is power the highest of spectrum power shown by for thethe spectrum analyzer for the (MaxHold function in the(MaxHold spectrumfunction analyzerinofthe Agilent). The received values estimated by considered bandwidth spectrum analyzer of power Agilent). The received power simulation have been for the same spatial samples thesame real measurements, considering values estimated byobtained simulation have been obtained forasthe spatial samples as the real the correspondingconsidering cuboid in the mesh in of cuboids in which the scenario beenin measurements, thethree-dimensional corresponding cuboid the three-dimensional mesh ofhave cuboids divided. It is scenario observedhave a good agreement simulation and measurement results,simulation with a mean which the been divided.between It is observed a good agreement between and error of 0.27 dBm with a standard deviation of 1.13 dB for 2.41 GHz frequency, and 0.66 dBm with a measurement results, with a mean error of 0.27 dBm with a standard deviation of 1.13 dB for 2.41 GHz standard deviation of 1.46 dBwith for 5.9 GHz frequency. Theofresulting very low,The indicating frequency, and 0.66 dBm a standard deviation 1.46 dB error for 5.9means GHz are frequency. resulting that the means proposed RL low, simulation method properly,3D validating in the same way works the simulation error are3D very indicating thatworks the proposed RL simulation method properly, results shown thesame previous sections of thisresults work. shown in the previous sections of this work. validating ininthe way the simulation

Figure Comparison simulation vs. measurements 2.41 andin 5.9 GHz in considered. the scenario Figure 14.14. Comparison simulation vs. measurements for 2.41for GHz andGHz 5.9 GHz the scenario considered.

5. Radioplanning Analysis 5. Radioplanning Analysis The deployment of three different communication systems has been analyzed within the The deployment of three differentofcommunication systems analyzed withinfor the considered scenario. Firstly, an analysis the received power andhas the been receiver’s sensitivity considered scenario. Firstly, analysis of theleading received andefficient the receiver’s sensitivity the the different technologies has an been analyzed, to power the most coverage ratio forfor the different technologies has been analyzed, leading to the most efficient coverage ratio for the deployment of the different systems. The first technology evaluated has been ZigBee technology. deployment the different systems. first technology evaluated been technology. Specifically, theofwireless devices used forThe simulation have been both, thehas XBee ProZigBee and XBee motes Specifically, the wireless devices used for simulation have been both, the XBee Pro and XBee motes from Digi International Inc. (Minnetonka, MN, USA). The main difference between them is the larger from Digi International Inc. (Minnetonka, MN, USA). The main difference between them is the transmitter power of the XBee Pro, leading to longer range distance. In order to analyze the worst larger transmitter power of thepower XBee levels Pro, leading to longer range distance. In order to analyze the case conditions, the transmitted considered has been reduced to the minimum default worst case conditions, the transmitted power levels considered has been reduced to the minimum value. After that, the second technology evaluated has been Bluetooth, specifically Bluetooth Low default value. After that,Bluetooth. the second technology hasa been Bluetooth, specifically Bluetooth Energy (BLE) and classic BLE has beenevaluated designed as low-power solution for control and Low Energy (BLE) and classic Bluetooth. BLE has been designed asthe a low-power solutionevaluated for control monitoring applications, in contrast with classic Bluetooth. Finally, third technology and monitoring applications, withtoclassic Bluetooth. Finally, the third technology has been the 802.11p radio, whichinis contrast the standard add wireless access in vehicular environments evaluated has been the 802.11p radio, which is the standard to add wireless access in (WAVE). It must be pointed out that for this technology we have performed new simulationsvehicular in the (WAVE). It must modulations be pointed out fordifferent this technology have new 5.9environments GHz frequency. Three different withthat three data rateswe have beenperformed analyzed for simulations in the 5.9 GHz frequency. Three different modulations with three different data rates the 802.11p radio standard. The transmitted power and sensitivity for the different communication have been analyzed for thein802.11p systems considered is shown Table 4.radio standard. The transmitted power and sensitivity for the different communication systems considered is shown in Table 4.

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Sensors 2016, 16, 1140 Table 4. Parameters

for the different considered wireless communication systems.

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Table 4. Parameters for the different considered wireless communication systems.

Transmitted Power

ZigBee XBee Pro ZigBee XBee Pro ZigBee XBee ZigBee XBee

BLE

BLE

Minimum Transmitted Power Minimum Transmitted Power Maximum Transmitted Power

Maximum Transmitted Power

Bluetooth Bluetooth Class 1 Class 1 Bluetooth Bluetooth Class 2 Class 2 Bluetooth Bluetooth Class 3 Class 3 802.11p

802.11p

BPSK–3 Mbps BPSK–3 Mbps 16 QAM–18 Mbps 16 QAM–18 Mbps 64 QAM–27 Mbps 64 QAM–27 Mbps

Transmitted Power 10 dBm 10 dBm 10 dBm 10 dBm ´20 dBm −20 dBm 10 dBm 10 dBm 20dBm dBm 20 dBm 44dBm dBm 00dBm 24 24dBm dBm 24 24dBm dBm 21 21dBm dBm

Sensitivity

Sensitivity ´100 dBm −100´92 dBmdBm −92 dBm ´93 dBm −93 dBm ´87 dBm −87 dBm −90 ´90 dBm dBm −90 ´90 dBm dBm −90 ´90 dBm dBm −95 ´95 dBm dBm −83 ´83 dBm dBm −77 ´77 dBm dBm

Figure 15 shows the linear distributions of received power along the X-axis for different values

Figure 15different shows the linearindistributions of received power radio alongand the for X-axis forheight different values of Y of Y for heights the case of ZigBee and 802.11p 1.4 m in the case of for different heights in the case of ZigBee and 802.11p radio and for 1.4 m height in the case of Bluetooth. Bluetooth. In order to clarify which distribution lines are those represented in Figure 15 in the real In order to clarify lines are those represented in Figure 15 in16. theItreal scenario,that the aerial scenario, thewhich aerialdistribution view of these distribution lines are shown in Figure is observed the view ofdistribution these distribution are shown Figure 16. It is observed that the distribution of received lines of lines received power, in which corresponds with the transmitter antennalines placed at the light #1 are represented. simulations results are at shown in a bi-dimensional plane in power,traffic which corresponds with the These transmitter antenna placed the traffic light #1 are represented. Figure 5a. These simulations results are shown in a bi-dimensional plane in Figure 5a.

(a)

(b)

(c)

(d) Figure 15. Cont.

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(e)

(f)

(e) (f) Figure 15. Comparison of radial of received power (dBm) along the X-axis with the receiver Figure(a)15. Comparison of and radial of received power along theXbEe X-axis receiver sensitivity ZigBee XBee Pro ZigBee XBee for Y = along 6(dBm) m; (b) ZigBee Prowith andthe ZigBee XBee Figure 15. Comparison of radial of received power (dBm) the X-axis with the receiver sensitivity sensitivity (a) ZigBee XBee Pro and ZigBee XBee for Y = 6 m; (b) ZigBee XbEe Pro and ZigBee XBee forZigBee Y = 14 m; (c) Pro BLEand system; (d) XBee Classic (e)ZigBee 802.11pXbEe Radio forand Y =ZigBee 6 m; (f)XBee 802.11p (a) XBee ZigBee forBluetooth; Y = 6 m; (b) Pro for Radio Y = 14 for m; for Y = 14 m; (c) BLE system; (d) Classic Bluetooth; (e) 802.11p Radio for Y = 6 m; (f) 802.11p Radio for Y = 14 m. (c) BLE system; (d) Classic Bluetooth; (e) 802.11p Radio for Y = 6 m; (f) 802.11p Radio for Y = 14 m. Y = 14 m.

Figure 15a,b shows the comparison between the received power of XBee Pro and XBee with the shows the comparison between thereceived receivedpower power of XBee with thethe FigureFigure 15a,b15a,b shows the comparison between the XBeePro Proand andXBee XBee with sensitivity of each of them. Figure 15c shows the comparison between BLE with the maximum and sensitivity of each of them. Figure shows comparisonbetween between BLE and sensitivity of each of them. Figure 15c15c shows thethe comparison BLE with withthe themaximum maximum and minimum transmitted power andand thethe higher and lower receiver sensitivity dependingofofwhich which type minimum transmitted power higher and lower receiver sensitivity depending type minimum transmitted power and the higher and lower receiver sensitivity depending of which type of of BLE being used.used. Figure 15d15d shows thethe comparison between Class 1, 1, Class2,2,and and Class 3 of of has BLE has being Figure shows comparison between 3 of BLE has being used. Figure 15d shows the comparison between Class Class 1, ClassClass 2, and ClassClass 3 of classic classicclassic Bluetooth. Figure 15e,f15e,f shows thethecomparison deviceswith withdifferent different Bluetooth. shows comparisonbetween between different different devices Bluetooth. Figure 15e,f Figure shows the comparison between different devices with different modulations modulations and and datadata ratesrates of the 802.11p standard, of Y. Y.ItItcan canbebeseen seen that modulations of the 802.11p standard,for fordifferent different values values of that and data rates of the 802.11p standard, for different values of Y. It can be seen that there are some are some points where signal goes down belowthe thesensitivity sensitivity level. there there are some points where the the signal goes down below level. points where the signal goes down below the sensitivity level.

Figure 16. Aerial view of the radial lines, which are represented in Figure 15, along the X-axis and Y-axis.

Figure 16. Aerial Aerialview viewofofthe theradial radiallines, lines, which represented in Figure along the X-axis which areare represented in Figure 15, 15, along the X-axis and and Y-axis. Y-axis.The comparison of the linear distribution of received power along the X-axis and Y-axis for

different lines in the considered scenario with the different sensitivity levels, leads to radio planning

The comparison ofthe the linear distribution of received received power along the X-axis and Y-axis for for engineers to knowof radio coverage of the different systems. Thesealong results, along withand the different The comparison the linear distribution of power the X-axis Y-axis different lines in the considered scenario with the different sensitivity levels, leads to radio planning planes of received power achieved for the whole scenario with the 3D Ray Launching Code, gives different lines in the considered scenario with the different sensitivity levels, leads to radio planning precise results but noradio homogeneous. before and it isThese represented Figure 7, the engineers to coverage of As the different systems. results, along with thereceived different engineers toknow knowthe theradio coverage ofstated the different systems. These in results, along with the planes of received power achieved for the whole scenario with the 3D Ray Launching Code, gives different planes of received power achieved for the whole scenario with the 3D Ray Launching precisegives results but no homogeneous. As stated before and itbefore is represented Figure 7, the received Code, precise results but no homogeneous. As stated and it is in represented in Figure 7,

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power can bepower approximated for regular coverage zones.coverage Depending of these results, and considering the received can be approximated for regular zones. Depending of these results, also considering the capacityalso of the anofoptimal design the wireless can be and thesystem, capacity the system, anofoptimal designsensor of the network wireless coverage sensor network obtained. can In order to represent a practical example,a apractical schematic view of a final implementation with coverage be obtained. In order to represent example, schematic view of a final omnidirectional with antennas for the different systems analyzed in the considered scenario shown in implementation omnidirectional antennas for the different systems analyzed in the is considered Figure 17.is Itshown must be out It that the be different radio of the different circles hasofbeen scenario in pointed Figure 17. must pointed out coverage that the different radio coverage the obtained circles according to simulation represented in Figure 15,represented and also taking into 15, account the different has been obtained results according to simulation results in Figure and also complete planes of received power,planes whichof have been obtained for thehave whole scenario and for several taking into account the complete received power, which been obtained the examples have and beenseveral represented in Figure 5 (XYrepresented planes), Figure 6 (YZ planes) and Figure 67 whole scenario examples have been in Figure 5 (XY planes), (XY planes) planes divided by zones). (YZ and Figure 7 (XY planes divided by zones). Working scenario

ZigBee XBee Pro

BLE with Maximum Transmitted Power

Bluetooth Class 1

802.11p Radio–BPSK–3 Mbps

Figure 17. Radioplanning coverage for different technologies within the considered environment. Figure 17. Radioplanning coverage for different technologies within the considered environment.

Another important parameter which is important when designing a WSN is capacity. Once Another parameter is important when designing a WSN capacity. coverage levelsimportant are satisfied, we mustwhich have adequate capacity in order to have a goodis quality of Once coverage levels are satisfied, we must have adequate capacity in order to have a good quality service in the communication link. The channel capacity depends on the number of users who are of service intothe link. The capacity depends theofnumber of users who connected thecommunication same communication linkchannel at the same time, the dataon rate the transceivers and are the number of gateways in which the information is gathered. Figures 18 and 19 show the channel capacity vs. the number of users for different data rates considered and for different number of

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connected same Sensors 2016,to16,the 1140 Sensors 2016, 16, 1140

communication link at the same time, the data rate of the transceivers and 17the of 25 17 of 25 number of gateways in which the information is gathered. Figures 18 and 19 show the channel capacity vs. the number ofobserved users for as different datathat rates considered and for different ofsame gateways. It isof gateways. expected the channelcapacity capacity increasesnumber forthe thesame number gateways. ItItisisobserved as expected that the channel increases for number of observed as every expected thatrate the considered, channel capacity increases for theissame number of users, for every dataof users, for data if the information gathered with a higher number users, for every data rate considered, if the information is gathered with a higher number of rate considered, if the information is gathered with a higher number of gateways. gateways. gateways.

(a) (a)

(b) (b)

(c) (c)

(d) (d)

Figure18. 18.Channel Channelcapacity capacity(bps) (bps)vs. vs.the thenumber numberof ofusers usersfor fordifferent differentnumber numberof gatewaysconsidered. considered. Figure 18. Channel capacity (bps) vs. the number of users for different number ofofgateways gateways considered. Figure (a) Data Rate = 250 Kbps; (b) Data Rate = 13 Mbps; (c) Data Rate = 18 Mbps; (d) Data Rate = 27 Data Rate Rate ==250 250Kbps; Kbps;(b) (b)Data DataRate Rate==1313Mbps; Mbps;(c)(c) Data Rate = 18 Mbps; Data Rate = Mbps. 27Mbps. Mbps. (a) Data Data Rate = 18 Mbps; (d)(d) Data Rate = 27

Figure 19. Channelcapacity capacity (bps)vs. vs. thenumber number of usersfor for differentdata data ratesfor for eight Figure Figure 19. Channel Channel capacity (bps) (bps) vs. the the numberofof users users for different different data rates rates for eight gateways considered. gateways gateways considered. considered.

Thegreat greatdifference differenceininthe thechannel channelcapacity capacitydepending dependingon onthe thedata datarate rateconsidered consideredisisalso alsovery very The significant.This Thisleads leadsusustotoconclude concludethat thatititisishighly highlyimportant importanttotoadequately adequatelyfix fixthe thenumber numberofof significant. gateways in the design phase, depending of the data rate of the transceivers and the expected gateways in the design phase, depending of the data rate of the transceivers and the expected number of users. number of users.

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The great difference in the channel capacity depending on the data rate considered is also very significant. This leads us to conclude that it is highly important to adequately fix the number of gateways in the design phase, depending of the data rate of the transceivers and the expected number of users. 6. Application Once we have modeled the scenario in terms of potential restrictions given by wireless channel operation and the required radio-planning phase, the main goal of the system is to produce a visualization platform presenting a city’s traffic status on real time. The main stakeholder is the municipality. It is worth noting that the results previously obtained in Section 3 provide insight into the characteristics of wireless propagation for the scenario under analysis. In this way, estimation of path loss is derived, a required information in order to perform the radio-planning analysis which is described in Section 5. These results provide the density of nodes as a function of the employed transceiver. This information in turn is used in order to adequately dimension the applications which can be executed, as well as the location of the nodes within the context of the specific items of the application, such as pedestrian monitoring. The proposed application that has been implemented, its characteristics and the functional/system architecture will be described in this section. The traffic in a city, as many other parameters, is very time/day dependent. Mobility patterns highly depend on the day and time of the day. Therefore, traffic light policy is a very complex task and difficult to execute in real time. Additionally, each decision at one place in the city impacts directly or indirectly on other areas of the city. It is a very complex task to maintain a fluid traffic flow in the whole city. In fact, it could be impossible in some situations like blocked streets due to meteorological issues. In order to analyze the traffic in a city, usually the city is logically divided in areas. Each area defines a conflict region where cars, pedestrian and bicycles are inputs, which should be processed to get out of the conflict region (some of them could remain inside). The conflict area is modeled using queuing theory. Each cross is a join of streets at a common point. Then, each street is modeled as a queue and server, with a processing policy. Mainly, this policy is first-in-first-out (FIFO). Traffic lights are the components of the system that regulate the order to process inputs on queues (server order). In this model, the arrival process of the inputs is a key factor to analyze each conflict area. Such a model is applied mainly for worst and best case modeling. This approach models different conflict areas independently, and not the whole city. In order to make decisions about traffic light policies, it is important to monitor the traffic, and to store historic data. Such data will be used to adjust the model: determine the arrival process, and the server processing time. However, such simulations are particular cases and while useful to analyze worst case, and average case, it is difficult to optimize general parameters like average time to cross the city. In our case, we propose another option to analyze a city’s traffic by gathering traffic information on-line and presenting it on a visual interface. In this case, traffic managers could analyze in real time the situation of the traffic in the city. The collected information could be used to adjust the previous model (queue network model to analyze the traffic by conflict areas). In our case, we gather such information based on a WSN. Each sensor is located at a traffic light. Each one collects information about the number of cars and the time interval between each pair of cars. Periodically, we integrate this information in a single packet, and transfer it to a central control, where data is stored and processed. In the initial test, we have deployed 20 nodes within the scenario under analysis, although there was no limitation to increase the network size, scalable as a function of the system requirements. In relation with the sensors deployed, we have focused mainly on weather, pollution (CO2 and NO) and presence. The employed nodes allow monitoring other items, such as multiple polluting substances, ambient conditions, liquid flow or existence of electric currents, to name a few.

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The graphical interface has been implemented in order to illustrate the city’s traffic state with the aid of lines and different colors. We have parameterized our sensors to evaluate levels of congestion. We use two parameters to establish line’s color, and type (points or partial lines): arrival rate (AR), to count the number of cars arriving at the traffic light each time unit; and processing rate (PR), Sensors 2016, 16, 1140 19 of 25 to measure the time the car requires to pass next to the traffic light. Both parameters and integrated integratedperiodically periodicallytotoevaluate evaluate Both parametersare aremeasured measured at at the the traffic traffic light, light, and thethe average value of the above parameters. Based on them, we evaluate the Congestion Threshold average value of the above parameters. Based on them, we evaluate the Congestion Threshold (CTh, as as thethe ratio between ARAR and PR), which represents thethe level of congestion of aoftraffic lane. With (CTh, ratio between and PR), which represents level of congestion a traffic lane. blue lines welines identify fluid traffic (CTh =(CTh 1, orange is used to denote partial congestion (1