Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing

Abstract

Mobile Crowd Sensing (MCS) has emerged as a new sensing paradigm due to its cost efficiency, mobility, and expandability. However, user selection for task allocation is a significant challenge in MCS. Most previous studies concentrate on two selection modes, opportunistic and participatory selection. Recent research has proposed a hybrid user selection mode that combines both advantages. However, existing hybrid user selection systems all rely on a centralized architecture, which is vulnerable to malicious attacks, and they do not consider the reliability of users and data availability. Moreover, they cannot ensure the individual rationality of users. To overcome these shortcomings, we propose a blockchain-based hybrid reliable user selection scheme for task allocation in MCS. Specifically, we replace the traditional central server with the blockchain and handle various sensing task operations using smart contracts on the blockchain to ensure system reliability and security. In addition, we design a user reputation calculation algorithm based on semi-Markov and a sensing data anomaly detection algorithm based on Long Short-Term Memory (LSTM) to ensure user reliability and data availability, and also a novel hybrid user selection algorithm, especially in the participatory user selection stage, where we use a user selection algorithm based on reverse auction to ensure the individual rationality of each user. Experimental results demonstrate the effectiveness of the proposed scheme through simulation experiments on GeoLife and sound-sensing public datasets.