# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
###### Abstract
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 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.