2019
DOI: 10.1109/access.2019.2933037
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Communication and Computation Cooperation in Wireless Network for Mobile Edge Computing

Abstract: The advantages of mobile edge computing (MEC) in lower energy consumption, improved bandwidth and reduced delay have attracted extensive studies. We consider a multiple smart wearable devices (SWDs) single smart mobile device (SMD) MEC system where the computation-intensive tasks can be offloaded to SMD and MEC server to improve resources utilization in this paper. To further minimize the energy consumption and satisfy the delay constraints of SWDs, SMD cooperation is introduced and we jointly optimize two-tie… Show more

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Cited by 10 publications
(2 citation statements)
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References 35 publications
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“…[14] considers a threenode mobile edge computing (MEC) system including user nodes, auxiliary nodes, and access point (AP) nodes.Then it develops an energy-saving design framework for partial and binary offloading. [15] studies the cooperation between mobile devices and MEC in the use scenario of wearable devices, and builds a two-layer task unloading model to minimize the energy consumption of Mobile devices and MEC under the constraint of service's delay. [16] studies cooperative communication method in the wireless transmission MEC system of two users to minimize AP transmission energy, and a two-stage method is proposed to obtain the optimal resource allocation strategy.…”
Section: Related Workmentioning
confidence: 99%
“…[14] considers a threenode mobile edge computing (MEC) system including user nodes, auxiliary nodes, and access point (AP) nodes.Then it develops an energy-saving design framework for partial and binary offloading. [15] studies the cooperation between mobile devices and MEC in the use scenario of wearable devices, and builds a two-layer task unloading model to minimize the energy consumption of Mobile devices and MEC under the constraint of service's delay. [16] studies cooperative communication method in the wireless transmission MEC system of two users to minimize AP transmission energy, and a two-stage method is proposed to obtain the optimal resource allocation strategy.…”
Section: Related Workmentioning
confidence: 99%
“…However, with the progress of the IoT, 6 the amount of data has increased exponentially. A series of problems have appeared: insufficient bandwidth, 7 high communication costs, increasing latency, and server computing power overload 8 . Therefore, the traditional centralized data processing model in cloud computing is not enough to support the IoT 9,10 …”
Section: Introductionmentioning
confidence: 99%