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In IoT, services are data-intensive and event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges in the IoT scenario with service dependencies. To this end, this paper proposes an intelligent approach for minimizing latency and energy consumption which jointly considers the task scheduling and resource allocation for dependent IoT services in MEC. Specifically, we establish the system model, communication model as well as computing model for performance evaluation by fully considering the dependent relationships among services, and an optimization problem is proposed for minimizing the delay and energy consumption simultaneously. Then, we design a layered scheme to deal with the service dependencies, and present detailed algorithms to intelligently obtain optimal task scheduling and resource allocation policies. Finally, simulation experiments are carried out to validate the effectiveness of the proposed scheme.<\/jats:p>","DOI":"10.1186\/s13677-023-00477-9","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T16:02:09Z","timestamp":1689696129000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An intelligent approach of task offloading for dependent services in Mobile Edge Computing"],"prefix":"10.1186","volume":"12","author":[{"given":"Jie","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yajing","family":"Leng","sequence":"additional","affiliation":[]},{"given":"Jiwei","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"477_CR1","doi-asserted-by":"crossref","unstructured":"Chen Y, Gu W, Xu J, et\u00a0al (2022) Dynamic task offloading for digital twin-empowered mobile edge computing via deep reinforcement learning. 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