A novel experience-based incentive mechanism for mobile crowdsensing system
Wenan Tan, Zihui Jiang · 2019
While sensor networks have been pervasively deployed in the real world, more and more mobile crowdsensing (MCS) applications have come into realization to collaboratively detect events and collect data. This paper aims to design a novel incentive mechanism to achieve good services for mobile crowdsensing applications. Responding to insufficient participants, we propose a novel Experience-Based incentive mechanism using Reverse Auction (EBRA). Additionally, it can also guarantee fair competition while maximizing the total profit of the service platform. Through strictly proving, our proposed EBRA incentive mechanism satisfies four properties: computational efficiency, individual rationality, profitability, and truthfulness. The extensive simulations show that the proposed EBRA method has a better performance over 20% than other benchmark mechanisms.