title: Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks.
abstract: Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. niuq@cumt.edu.cn.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Niu
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. yamgxu@cumt.edu.cn.
authors.forename: Xu
authors.initials: X
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. gaoshouwan@cumt.edu.cn.
authors.forename: Shouwan
authors.initials: S
authors.lastname: Gao
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. chenp@cumt.edu.cn.
authors.forename: Pengpeng
authors.initials: P
authors.lastname: Chen
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. 08133468@cumt.edu.cn.
authors.forename: Shibing
authors.initials: S
authors.lastname: Chan
keywords: localization; time stamps; traffic lights; wireless sensor network