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.

TPLE: A Reliable Data Delivery Scheme for On-Road WSN Traffic Monitoring

“…Figure 2 shows typical on-road monitoring settings. Several sensor nodes are deployed along a road, the sensor nodes are fixed in position, the distance between two neighbor nodes is d(For the simplicity consideration, here we suppose the distance between neighbor nodes are equal in one system) [ 22 ]. Also, we suppose that the vehicle target is in a state of uniform rectilinear motion.…”

Localization with Graph Diffusion Property

“…Node localization is a fundamental issue in wireless sensor networks (WSNs) that has also been studied for a long time and has received many classic and improved theories [ 1 , 2 , 3 , 4 ] for practical applications, e.g., border security and surveillance [ 4 ], item tracking [ 5 , 6 ], road monitoring [ 7 ], or environment data collection [ 8 ]. Two basic methods, namely, range-based and range-free schemes, are proposed for node localization.…”