基于深度强化学习和隐私保护的群智感知动态任务分配策略  

Dynamic Task Allocation for Crowd Sensing Based on Deep Reinforcement Learning and Privacy Protection

作　　者：傅彦铭[1,2,3] 陆盛林 陈嘉元 覃华 FU Yanming;LU Shenglin;CHEN Jiayuan;QIN Hua(School of Computer,Electronic and Information,Guangxi University,Nanning 530000,China;Key Laboratory of Parallel,Distributed and Intelligent Computing(Guangxi),Nanning 530000,China;Guangxi Intelligent Digital Services Research Center of Engineering Technology,Nanning 530000,China)

机构地区：[1]广西大学计算机与电子信息学院,南宁530004 [2]广西高校并行分布与智能计算重点实验室,南宁530004 [3]广西智能数字服务工程技术研究中心,南宁530004

出　　处：《信息网络安全》2024年第3期449-461,共13页Netinfo Security

基　　金：国家自然科学基金[61962005]。

摘　　要：在移动群智感知(Mobile Crowd Sensing,MCS)中,动态任务分配的结果对提高系统效率和确保数据质量至关重要。然而,现有的大部分研究在处理动态任务分配时,通常将其简化为二分匹配模型,该简化模型未充分考虑任务属性与工人属性对匹配结果的影响,同时忽视了工人位置隐私的保护问题。针对这些不足,文章提出一种基于深度强化学习和隐私保护的群智感知动态任务分配策略。该策略首先通过差分隐私技术为工人位置添加噪声,保护工人隐私;然后利用深度强化学习方法自适应地调整任务批量分配;最后使用基于工人任务执行能力阈值的贪婪算法计算最优策略下的平台总效用。在真实数据集上的实验结果表明,该策略在不同参数设置下均能保持优越的性能,同时有效地保护了工人的位置隐私。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.

关 键 词：群智感知 深度强化学习 隐私保护 双深度Q网络 能力阈值贪婪算法 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]