Optimized sensor placement for dependable roadside infrastructures

We present a multi-stage optimization method for efficient sensor deployment in traffic surveillance scenarios. Based on a genetic optimization scheme, our algorithm places an optimal number of roadside sensors to obtain full road coverage in the presence of obstacles and dynamic occlusions. The efficiency of the procedure is demonstrated for selected, realistic road sections. Our analysis helps to leverage the economic feasibility of distributed infrastructure sensor networks with high perception quality.Comment: 6 pages, 5 figures; IEEE Intelligent transportation systems conference 201

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio