LAUSR

Mobile crowdsensing has become increasingly popular due to its ability to collect a massive amount of data with the help of many individual smartphone users. A crowdsensing platform can utilize the collected data to extract effective information and provide diverse services. Designing an incentive mechanism to compensate the participants for their resources consumption is critical in attracting more participation. Offline incentive mechanism design has been widely studied in various crowdsensing applications, whereas the online scenario, is much more challenging due to the unavailability of future information when the platform makes user selection decisions. In this paper, we investigate the problem of online crowdsensing by considering a critical property that the values of users’ contributions decrease as time goes by. The time-discounting property is common in inter-temporal choice scenarios but has not been carefully addressed from the perspective of mechanism design. To handle this problem, we propose a new method to select users based on a time-dependent threshold, and present a strategy-proof framework where participants prefer to submit their true types, instead of manipulating the market by misreporting their private information. We consider two cases, one is that the total value is the summation of each participant's contributing value, the other is more general that the total value function is submodular. We call these two mechanisms TDM and TDMS, respectively. We prove that our two mechanisms can achieve computational efficiency, budget feasibility, strategy-proofness, and a constant competitive ratio, in the context of time-discounting values. By comparing our mechanisms with the state-of-the-art methods, we show that our design achieves better performance in terms of the total value.