Improved Recruitment Algorithms for Vehicular Crowdsensing Networks

Vehicular crowdsensing aims to utilize the plethora of onboard sensors and resources on smart vehicles to gather sensing data in a large coverage area. Recruitment algorithms aim to select participants within a crowdsensing network such that the most sensing data is obtained for the lowest possible cost. In this paper, we consider two such existing recruitment problems for vehicular crowdsensing and propose several heuristics. We also show that existing algorithms to solve these problems can be arbitrarily bad in the worst case. We also compare our algorithms with both optimal solutions (returned by mixed integer programs) as well as existing heuristics. Performance evaluations on our algorithms show that our algorithms outperform existing algorithms and obtain near optimal solutions.