CHASTE: Incentive Mechanism in Edge-Assisted Mobile Crowdsensing

Mobile crowdsening (MCS) recently has been regarded as a newly-emerged sensing paradigm, which consists of a centralized cloud-based platform, some data demanders and some workers. However, since the platform needs to process tons of sensory data which is requested by the demanders and submitted by a large number of workers, this traditional MCS system may cause a heavy latency and serious network congestion, and thus can not be employed to some real-time and large-scale applications. Therefore, a new MCS system has been proposed recently by combining the edge computing, where some edge nodes (e.g., the base stations, laptops, smartphones) are added between the platform and workers to offload some operations from the platform to the network edges. Similar to the traditional MCS system, since participating in such edge-assisted MCS system is costly, it is important to attract more participation. However, this is more difficult since the platform and workers do not have direct communications with each other, which makes the edge nodes arbitrarily manipulate the information they transfer. Therefore, unlike the prior arts, we propose a novel incentive mechanism, namely, CHASTE, for such edge-assisted MCS system consisting of multiple demanders, some edge nodes and a crowd of workers where all of them behave strategically to maximize their own utility. Specifically, CHASTE is able to stimulate the participation, and satisfies the three-party truthfulness, three-party individual rationality, budget balance, as well as high social welfare. The desirable properties of CHASTE are validated through both theoretical analysis and extensive simulations.