Abstract:

In mobile crowd sensing(MCS), the outcome of dynamic task allocation is crucial for enhancing system efficiency and ensuring data quality. Most existing studies simplify dynamic task allocation into a bipartite matching model, which fails to sufficiently consider the impact of task and worker attributes on the matching results and overlooked the protection of worker location privacy. To address these shortcomings, this paper presents a privacy-preserving dynamic task allocation strategy for MCS based on deep reinforcement learning and privacy protection. The strategy first employed differential privacy techniques to add noise to worker locations, protecting their privacy. It then adapted task batch assignments using deep reinforcement learning methods. Finally, it employed a greedy algorithm based on worker task capability thresholds to compute the maximal total utility of the platform under the optimal strategy. Experimental results on real-world datasets demonstrate that the strategy maintains superior performance under various parameter settings while effectively safeguarding worker location privacy.

Key words: crowd sensing, deep reinforcement learning, privacy protection, double deep Q-network, capacity threshold greedy algorithm