Abstract
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.
Highlights
The Internet of things (IoT) is changing our lives drastically
I =1 j =1 where ci,j denotes the computation time and di,j denotes the transmission delay. ζ, η, θ are used to denote the status of task offloading execution, task dropping, and local execution based on the executing tasks according to the following scenarios
The proposed method allows switching between different modes of offloading execution, task dropping, and local execution based on the executing tasks
Summary
The Internet of things (IoT) is changing our lives drastically. Connectivity among people, things, and businesses are increasing exponentially. Their proposed algorithm can dynamically coordinate and allocate resources to fog nodes They focused on subproblems such as latency, consumption of power by EH devices, and the priority of mobile devices. Liu et al [8] have used a queuing model to achieve objective optimization in fog computing scenarios Their proposed system can help to minimize energy consumption and improve delay performance. They optimize the payment cost for mobile devices. A smart and energy-efficient computation offloading algorithm for multi-user and multi-server MEC system is designed and which have different EH devices. Proposing a dynamic framework for energy-efficient computation offloading approach based on linear programming in multi-users, multi-servers MEC environment.
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