Energy-Efficient Mobile Edge Computing System Based on Full-Duplex Energy Harvesting Relay Network

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [ O ( 1 / V ) , O ( V ) ] . V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.

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An Energy Efficient Design of Computation Offloading Enabled by UAV

Sensors ◽

10.3390/s20123363 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3363

Author(s):

Linpei Li ◽

Xiangming Wen ◽

Zhaoming Lu ◽

Wenpeng Jing

Keyword(s):

Unmanned Aerial Vehicle ◽

Energy Efficient ◽

Computing System ◽

Edge Computing ◽

Mobile Edge Computing ◽

Efficient Design ◽

Scheme Design ◽

Computing Services ◽

Efficient Scheme ◽

Aerial Vehicle

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

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Energy-efficient Resource Allocation for UAV-empowered Mobile Edge Computing System

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) ◽

10.1109/ucc48980.2020.00064 ◽

2020 ◽

Author(s):

Yu Cheng ◽

Yangzhe Liao ◽

Xiaojun Zhai

Keyword(s):

Resource Allocation ◽

Energy Efficient ◽

Computing System ◽

Edge Computing ◽

Mobile Edge Computing ◽

Efficient Resource ◽

Energy Efficient Resource Allocation

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Energy-Efficient Mobile Edge Hosts for Mobile Edge Computing System

2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS) ◽

10.1109/iciafs.2018.8913354 ◽

2018 ◽

Cited By ~ 3

Author(s):

Shanmuganathan Thananjeyan ◽

Chien Aun Chan ◽

Elaine Wong ◽

Ampalavanapillai Nirmalathas

Keyword(s):

Energy Efficient ◽

Computing System ◽

Edge Computing ◽

Mobile Edge Computing

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Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Electronics ◽

10.3390/electronics9061010 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1010 ◽

Cited By ~ 6

Author(s):

Prince Waqas Khan ◽

Khizar Abbas ◽

Hadil Shaiba ◽

Ammar Muthanna ◽

Abdelrahman Abuarqoub ◽

...

Keyword(s):

Energy Harvesting ◽

Mobile Devices ◽

Energy Efficient ◽

Linear Optimization ◽

Edge Computing ◽

Computation Offloading ◽

Optimization Approach ◽

Mobile Edge Computing ◽

Efficient Computation ◽

Integer Linear Optimization

Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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