Router deployment of Streetside Parking Sensor Networks in Urban Areas

The deployment of municipal wireless mesh networks is of vital importance to collect urban information. As the increasing parking search problem, we select the popular smart parking application and study its deployment problems. In particular, on-street parking applications require such an infrastructure for enabling large scale system at reasonable costs. Current existing smart parking projects, e.g., SFpark and SmartSantander, generally comprise in-ground sensors, routers and gateways. The common in-ground parking sensor is magnetometer which only detects ferrous metals. The in-ground sensor, restricted by the soil medium and battery-limited energy, is reduced function device (RFD) which communicates directly with roadside overground routers/gateways. Router and gateway, both full function devices (FFD) and as parts of urban infrastructure, collect the parking available information from sensors to internet. Thus, the network connectivity and performance are strongly determined by the density of routers and gateways. In order to reach a maximum coverage, routers and gateways are generally installed at crossroads and manage sensors directly, then a mesh network is formed among routers and gateways. [1]–[3] all proposed a multi-objective evolutionary approach to aid the sensor deployment with a multi-objective problem. However, we do not see a real map to be considered in a multi-objective optimization. In this article, we are mainly interested in the map impact to the deployment of urban infrastructure for wireless parking sensor networks. We consider a multi-objective problem and optimize it using real on-street parking maps. Our contributions is to formulate the multi-hop mesh networks among urban infrastructure and solve them by a multi-objective optimization in order to aim at the smart street parking application. We also highlight the following insights on parking sensor network design: First, the total energy consumption of sensors is strongly related to the amount of intersections and of active FFDs. Second, the gateway deployment can be seen as a cluster problem. Once the selection of FFD is once, the cluster number will be the minimum amount of deployed gateway. Third, the sensing information delay is related to the average degree of the street parking graph. Thus, the complexity of city map is an important factor while building urban infrastructure.