Towards time-constrained task allocation in semi-opportunistic mobile crowdsensing

Abstract

Mobile crowdsensing (MCS) is an emerging sensing paradigm that leverages mobile users carrying smart devices to perform sensing tasks. On the one hand, as the sensing scenarios become more realistic and complex, the impact of time attributes on task allocation is significantly increased. However, most of the existing works only treat time attributes as supplementary constraints and ignore the existence of multiple time constraints. On the other hand, considering the exploration and application of emerging models, making MCS more adaptive and diversified also becomes a research focus. In this paper, we propose the Multiple Time constrained Task Allocation problem in Semi-Opportunistic mobile crowdsensing (SO-MTTA), with the goal of maximizing the sensing value obtained by the platform. We prove that the SO-MTTA problem is NP-hard and design a Multi-Layer Genetic Algorithm (MLGA), which uses a low space complexity encoding method for three layers: User-path-task, and adds a conflict elimination operation to correct possible conflicts of constraints. Finally, we conduct experiments on both synthetic and real-world datasets to successfully verify the effectiveness of our proposed algorithm.