Two-Sided Learning: A Techno-Economic View of Mobile Crowdsensing Under Incomplete Information

In Mobile Crowdsensing (MCS) a mobile crowd-sensing platform (MCSP) collects sensing data from mobile units (MUs) in exchange for payment. The MCSP broadcasts a list of available sensing tasks. Based on this list, each MU solves a task proposal problem to decide which task it is willing to perform and sends a proposal to the MCSP. Based on the MUs' proposals, the MCSP solves a task assignment problem. There are two challenges when finding efficient task proposal strategies for the MUs and an efficient task assignment strategy for the MCSP (i) The techno-economic perspective of MCS: From the technical perspective, MCS should maximize the data quality while minimizing time and energy consumption. From the economic perspective, there are two sides, the MUs and the MCSP which act as selfish decision-makers, who aim at maximizing their own income. (ii) Incomplete information at two sides: Initially, the MCSP does not know the expected data quality and the MUs do not know the expected effort required for task completion. To overcome these challenges, we propose a novel Two-Sided Learning (TSL) approach. At the MU side, TSL is based on an innovative gradient-based multi-armed bandit solution to maximize the MUs' utility under incomplete information about the strategies of other MUs. At the MCSP side, a learning strategy is used to find the task assignment strategy that maximizes its utility. Simulation results show that TSL achieves near-optimal social welfare, which is the sum of MUs' and MCSP's utilities, and a near-optimal energy efficiency.