Mobile Crowdsensing Coverage Degree-Probability Enhancement based on Urban Vehicles
Ting Liu, Chaowei Wang, Xiga Gaimu, Weidong Wang · 2020
Mobile crowdsensing (MCS) is a promising diagram for data collecting based on smart mobile terminal. Nowadays, vehicles with embedded multiple sensors have been increasingly adopted as participants to complete various sensing tasks. Most existing researches are conducted in terms of MCS coverage, energy consumption or incentive mechanisms etc. In this paper, a new utility function F(Ω) for measuring the quality of MCS coverage is proposed, F(Ω) includes coverage percentage and coverage degree. We formulate the selection of taxis as an optimization of coverage quality. Therefore, an improved greedy algorithm to optimize the coverage quality (CQO) is proposed. We evaluate the proposed algorithm with trajectory dataset and study several factors influencing coverage quality. The results show that the proposed algorithm achieves a better coverage quality.