Energy-efficient computation offloading for vehicular edge computing networks

2021 ◽  
Vol 166 ◽  
pp. 244-253
Author(s):  
Xiaohui Gu ◽  
Guoan Zhang
Keyword(s):  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Efficient Computation

scholarly journals ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110353
Author(s):  
Mohammad Babar ◽  
Muhammad Sohail Khan
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Computation Time ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Efficient Computation ◽  
Smart Systems ◽  
Iot Devices ◽  
Delay Tolerant

Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.

Energy-efficient computation offloading in 5G cellular networks with edge computing and D2D communications

2019 ◽  
Vol 13 (8) ◽  
pp. 1122-1130 ◽  
Author(s):  
Qingmin Jia ◽  
Renchao Xie ◽  
Qinqin Tang ◽  
Xiaolu Li ◽  
Tao Huang ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Efficient Computation ◽  
D2d Communications

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

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1010 ◽  
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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