A hyper-heuristic optimization multi-task allocation in mobile crowdsensing based on inherent attributes

Abstract

Task allocation is a critical issue in mobile crowdsensing (MCS) that significantly impacts the overall sensing quality of the system. However, previous research has often focused on improving sensing quality through single indicators such as user coverage or user reliability, neglecting the inherent attributes of users and tasks as well as the variability in user abilities. This oversight can lead to unreliable sensing abilities among recruited users, thereby affecting the system’s overall sensing quality. In this paper, we first analyze the intrinsic attributes of users and tasks and propose an aggregative indicator and user enhancement model for better assessment and description of user sensing abilities. To improve the system’s overall sensing quality, the task allocation problem is modeled as a multi-constraint single-objective optimization problem. To address this problem, a Simulated Annealing-based Random Selection Hyper-Heuristic Optimization Algorithm (SARSHHOA) has been developed. This algorithm begins by generating an initial allocation scheme using a greedy approach, then applies randomly selected search operators to various allocation schemes and utilizes simulated annealing to selectively accept solutions. Finally, the effectiveness of the proposed aggregative indicator and task allocation algorithm is validated through simulation experiments on real datasets.