Alien vs. Mobile user game: Fast and efficient area coverage in crowdsensing

Mobile crowdsensing enables real-time sensing of the physical world. However, providing uniform sensing coverage of an entire area may prove difficult. It is possible to collect a disproportionate amount of data from very popular regions in the area, while the unpopular regions remain uncovered. To address this problem, we propose a model for collecting crowdsensing data based on incentivizing smart phone users to play sensing games, which provide in-game incentives to convince participants to cover all the regions of a target area. We designed and implemented a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. Our prototype Android game collects WiFi data to create a campus coverage map. The results from a user study show that mobile gaming ensures high coverage, and we observe that the proposed game design succeeds in achieving good player engagement. Furthermore, we compare three strategies for area coverage in terms of coverage time and coverage effort for users. The simulation results demonstrate that Progressive Movement is the best strategy because it manages to quickly entice users from popular regions to unpopular ones with a reasonable coverage effort.