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A Blockchain-Based Reward Mechanism for Mobile Crowdsensing - 2020

A Blockchain-Based Reward Mechanism For Mobile Crowdsensing

Research Area:  Blockchain Technology

Abstract:

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator-s utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.

Keywords:  

Author(s) Name:   Jiejun Hu; Kun Yang; Kezhi Wang; Kai Zhang

Journal name:  IEEE Transactions on Computational Social Systems

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TCSS.2019.2956629

Volume Information:  ( Volume: 7, Issue: 1, February 2020) Page(s): 178 - 191