# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 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 (CO\({}_{2}\) and NO) and presence.