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Syst."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:p>\n                    Recent research highlights a critical gap between the computing capabilities of modern IoT devices and the computational demands of Artificial Intelligent (AI) applications. The edge computing paradigm offers a promising solution by providing reliable and fast computing services close to the data source. Due to their inherent resource constraints, a single edge server often cannot handle the heavy computational load from nearby IoT devices, requiring multi-server collaboration. However, achieving efficient cooperation is challenging due to dynamic workload fluctuations and uneven data distribution. To address these issues, this article presents a novel solution that involves optimizing task execution paths and resource management to enhance the performance of edge servers, particularly in scenarios with unbalanced data or uneven distribution of IoT devices. Our approach not only deploys multiple edge servers, but also focuses on the intelligent allocation and management of computing tasks. Specifically, we propose\n                    <jats:italic toggle=\"yes\">c2mec<\/jats:italic>\n                    , a cooperative multi-split and multi-hop edge computing framework. The proposed\n                    <jats:italic toggle=\"yes\">c2mec<\/jats:italic>\n                    framework uses problem decomposition to efficiently decouple the variables that need to be optimized. In addition,\n                    <jats:italic toggle=\"yes\">c2mec<\/jats:italic>\n                    employs a multi-agent Deep Reinforcement Learning (DRL) based algorithm to mitigate the negative impact of decoupling and provides a flexible data splitting strategy. Finally,\n                    <jats:italic toggle=\"yes\">c2mec<\/jats:italic>\n                    designs an energy-aware training method for IoT devices to reduce their long-term training cost. Through our comprehensive experimental results, we demonstrate how\n                    <jats:italic toggle=\"yes\">c2mec<\/jats:italic>\n                    achieves notable improvement in energy saving across different scenarios compared to existing solutions, such as full and partial task offloading.\n                  <\/jats:p>","DOI":"10.1145\/3772281","type":"journal-article","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:19:06Z","timestamp":1760782746000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["c2mec: Cooperative Multi-split and Multi-hop Edge Computing Based on Deep Reinforcement Learning"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6576-1876","authenticated-orcid":false,"given":"Xiaojie","family":"Zhang","sequence":"first","affiliation":[{"name":"Hunan First Normal University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0433-6148","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hunan First Normal University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5598-0271","authenticated-orcid":false,"given":"Shima","family":"Yousefi","sequence":"additional","affiliation":[{"name":"City University of New York The Graduate Center","place":["New York, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4783-119X","authenticated-orcid":false,"given":"Saptarshi","family":"Debroy","sequence":"additional","affiliation":[{"name":"City University of New York The Graduate Center","place":["New York, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York","place":["New Paltz, United States"]},{"name":"Hunan University","place":["New Paltz, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et\u00a0al. 2023. 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