A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation

Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This paper elaborates on alleviating the energy conservation problem in wireless devices operating under the Internet of Things (IoT) environments, by using machine-to-machine (M2M) communication mechanisms. Such IoT wireless terminals, such as wearables, smart glasses, and smart objects, are able to distress energy consumption levels of the IoT environments, playing a crucial role to the quality of service (QoS) or quality of experience (QoE) provision for end users under several high demand scenarios, while they are on the move. In this context, this paper proposes a new offload-aware recommendation scheme, towards allowing the effective monitoring of high energy consumption applications that run in the mobile devices of such IoT ecosystems. The proposed model enables mobile users having a nonstop provision of on-demand services, while such devices are able to provide the available required resources that are to be exploited in IoT environments. The proposed system model and the M2M offloading mechanisms and mathematical foundations, this paper exploits an edge-based communication mechanism, towards enabling resource-aware recommendation. The performance evaluation results validate the proposed approach, by assessing the provided model in the framework of the reliability provision for the IoT terminals under the use of the recommendation scheme, as well as the energy conservation provision for several mobile devices that are included in the IoT environment during the offloading procedure.

Original languageEnglish
Article number8777080
Pages (from-to)102295-102303
Number of pages9
JournalIEEE Access
Publication statusPublished - 2019


  • Edge computing
  • energy conservation
  • IoT offload metrics
  • offload processing
  • offload-oriented mobile cloud
  • resource-aware recommendation


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