6533b835fe1ef96bd129f475
RESEARCH PRODUCT
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
Sadia KhafGhulam AbbasLei JiaoZiaul Haq AbbasZaiwar AliThar Bakersubject
QA75General Computer ScienceComputer scienceDistributed computingenergy efficient offloading02 engineering and technologyVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 42001 natural sciencesuser equipmentComputational offloadingServer0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Mobile edge computingbusiness.industryDeep learning010401 analytical chemistryGeneral Engineeringdeep learning020206 networking & telecommunicationsEnergy consumption0104 chemical sciencesUser equipmentArtificial intelligencemobile edge computinglcsh:Electrical engineering. Electronics. Nuclear engineeringbusinesslcsh:TK1-9971Efficient energy usedescription
Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 | IEEE Access |