ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1624913
This article is part of the Research TopicSecurity, Governance, and Challenges of the New Generation of Cyber-Physical-Social Systems, Volume IIView all 13 articles
P3TRTA-RDQL: A Crowdsensing Task Allocation Scheme Integrating Privacy Protection Protocol and Low-Dimensional Reinforcement Learning
Provisionally accepted
- Power Dispatching and Control Center of Guangdong Power Grid, Guangzhou, China
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Abstract—Crowdsensing, as an emerging data collection mode, demonstrates great potential in Internet of Things (IoT) applications. However, it faces a critical trilemma: accurate task allocation depends on node proximity to the task location, but disclosing location data risks privacy leakage, while concealing it reduces allocation precision. Existing solutions either incur high computational overhead (encryption), rely on unfeasible trusted third parties (anonymization), or degrade data utility (obfuscation), failing to balance privacy, accuracy, and efficiency. To address these issues, this paper proposes the P3TRTA-RDQL scheme, combining a symmetric encryption-based privacy protection protocol (P3TRTA) with a low-dimensional Q-learning algorithm (RDQL). The P3TRTA protocol uses a location-based symmetric key generator (LSKeyGen) to protect node/task location privacy and proxy re-encryption to secure task content, eliminating reliance on trusted third parties. The RDQL algorithm reduces state dimensionality by 60% compared to traditional reinforcement learning, enhancing large-scale task allocation efficiency. Experimental results show that P3TRTA-RDQL outperforms existing methods by 30% in privacy protection strength, achieves 98% task allocation accuracy, and reduces allocation time for 1000 tasks by 40%. This work provides technical support for crowdsensing's widespread IoT applications.
Keywords: Crowdsensing, task allocation, Privacy protection, Symmetric encryption, reinforcement learning, Low-Dimensional Q-Learning Algorithm
Received: 08 May 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Li, Chen and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qian Li, a15968377016@sina.com
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