{"id":11604,"date":"2026-09-06T15:23:40","date_gmt":"2026-09-06T07:23:40","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11604"},"modified":"2026-09-06T16:50:54","modified_gmt":"2026-09-06T08:50:54","slug":"jase-202612-35-013","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-013","title":{"rendered":"Secure Offloading and Data Privacy Protection Strategies for Edge Computing in Multi-Robot Systems"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Duanjiao Li1<\/sup>, Yongchao Liang1<\/sup>, Wenxing Sun1<\/sup>, Zibin Zhu2<\/sup>, Jianguo Zhang3<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Guangdong Power Grid Co., Ltd, Guangzhou Guang Dong, 510030, China<\/p>\n\n\n\n

2<\/sup>Zhaoqing Power Supply Bureau, Guangdong Power Grid, Zhaoqing GuangDong, 526060, China<\/p>\n\n\n\n

3<\/sup>Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Guangdong, 518129, China<\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 16, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Real-scene visual comparison of local map fusion and trajectory privacy under different algorithms <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.013<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Multi-robot systems in intelligent warehousing, industrial inspection, disaster relief, and UAV collaboration face complex tasks including target detection, map fusion, path planning, status diagnosis, and log uploading. Limited onboard computing, heterogeneous edge resources, and fluctuating links expose trajectories, maps, images, and status data during offloading, and traditional latency- or energy-only optimization fails to address scalability, security, and privacy together. This study proposes SPP-ESO, a security- and privacy-aware edge offloading strategy based on a three-layer robot-edge-cloud architecture, integrating task profiling, privacy level identification, dynamic trust assessment, candidate node filtering, multi-objective reinforcement learning, differential privacy perturbation, data minimization, and edge-failure migration into a unified model optimizing latency, energy, load balancing, security risk, privacy risk, and failure penalty together. With 200 robots, SPP-ESO achieves 218 ms average latency, 4.73 J energy consumption, 96.4% task completion, 3.6% SLA violation, 3.1% attack success, and 0.22 privacy risk, outperforming local-only, cloud-only, greedy-latency, DRL-offloading, and trust-aware baselines. At 300 robots under high concurrency, completion rate remains ~94%.<\/p>\n\n\n\n

Keywords: multi-robot system, scalable information system, edge computing, secure offloading, privacy protection, trust assessment, differential privacy, multi-agent reinforcement learning<\/em><\/p>\n\n\n\n

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