Privacy-Aware Task Allocation Based on Deep Reinforcement Learning for Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is a new paradigm for data collection, data mining and intelligent decision-making using large-scale mobile devices. The efficient task allocation method is the key to the high performance of MCS. The traditional greedy algorithm or ant algorithm assumes that workers and tasks are fixed, which is not suitable for the situation where the location and quantity of workers and tasks change dynamically. Moreover, the existing task allocation methods usually collect the information of workers and tasks by the central server for decision-making, which is easy to lead to leakage of workers’ privacy. In this paper, we propose a task allocation method with privacy protection using deep reinforcement learning (DRL). Firstly, the task allocation is modeled as a dynamic programming problem of multi-objective optimization, which aims to maximize the benefits of workers and platform. Secondly, we use DRL for training and learning model parameters. Finally, the local differential privacy method is used to add random noise to the sensitive information, and the central server trains the whole model to obtain the optimal allocation strategy. The experimental results on the simulated data set show that compared with the traditional methods and other DRL based methods, our proposed method has significantly improved in different evaluation metrics, and can protect the privacy of workers.