A Multi-Task Scheduling Mechanism Based on ACO for Maximizing Workers' Benefits in Mobile Crowdsensing Service Markets With the Internet of Things

Mobile Crowdsensing (MCS) is a new mode of sensing for the Internet of Things, which has become a research hotspot. In an MCS market, there are usually three parties, i.e. requesters, workers and the platform. Each party of the crowdsensing market wants to obtain more benefits, so different mechanisms of task assignment need to be provided respectively to meet the different needs of the three parties. Great efforts have been invested on task assignment mechanisms from the perspective of the platform or requesters, i.e. a user recruitment algorithm of profits-maximizing for the platform under budget constraint, an efficient and truthful pricing mechanism for team formation and so on. However, to the best of our knowledge, there is rare mechanism for the task scheduling or planning from the perspective of workers, without considering how to maximize the benefits of workers in the case of multitasking. In this paper, a theoretical analysis on the calculation model of workers' benefits is conducted to investigate the infiuence factors of workers' income and its relation. Consequently, a heuristic multi-task scheduling algorithm based on Ant Colony Optimization algorithm (ACO) is proposed to determine a task scheduling strategy to maximize the workers' benefits. Finally, extensive experiments are carried out by using the STSP dataset available online, and it is shown that the proposed algorithm significantly reduces the cost of completing multiple tasks, and substantially improves the workers' benefits.