A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation
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 t...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8777080/ |
| _version_ | 1819291229570990080 |
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| author | Constandinos X. Mavromoustakis George Mastorakis Jordi Mongay Batalla |
| author_facet | Constandinos X. Mavromoustakis George Mastorakis Jordi Mongay Batalla |
| author_sort | Constandinos X. Mavromoustakis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T03:35:19Z |
| format | Article |
| id | doaj.art-1ba3f329222f414ca7f5df3114baad66 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T03:35:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1ba3f329222f414ca7f5df3114baad662022-12-21T17:17:04ZengIEEEIEEE Access2169-35362019-01-01710229510230310.1109/ACCESS.2019.29313628777080A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy ConservationConstandinos X. Mavromoustakis0https://orcid.org/0000-0003-0333-8034George Mastorakis1https://orcid.org/0000-0002-6733-5652Jordi Mongay Batalla2https://orcid.org/0000-0002-1489-5138Department of Computer Science, University of Nicosia, Nicosia, CyprusDepartment of Management Science and Technology, Hellenic Mediterranean University, Heraklion, GreeceNational Institute of Telecommunications, Warsaw University of Technology, Warsaw, PolandThis 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.https://ieeexplore.ieee.org/document/8777080/Edge computingenergy conservationIoT offload metricsoffload processingresource-aware recommendationoffload-oriented mobile cloud |
| spellingShingle | Constandinos X. Mavromoustakis George Mastorakis Jordi Mongay Batalla A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation IEEE Access Edge computing energy conservation IoT offload metrics offload processing resource-aware recommendation offload-oriented mobile cloud |
| title | A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation |
| title_full | A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation |
| title_fullStr | A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation |
| title_full_unstemmed | A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation |
| title_short | A Mobile Edge Computing Model Enabling Efficient Computation Offload-Aware Energy Conservation |
| title_sort | mobile edge computing model enabling efficient computation offload aware energy conservation |
| topic | Edge computing energy conservation IoT offload metrics offload processing resource-aware recommendation offload-oriented mobile cloud |
| url | https://ieeexplore.ieee.org/document/8777080/ |
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