Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting f...

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Bibliographic Details
Published in:Security and communication networks 2021-12, Vol.2021, p.1-10
Main Authors: Zhang, Yawen, Miao, Yifeng, Pan, Shujia, Chen, Siguang
Format: Article
Language:English
Subjects:
Artificial neural networks
Cloud computing
Communication
Computation offloading
Computational efficiency
Computer architecture
Convergence
Edge computing
Energy consumption
Energy harvesting
Greedy algorithms
Internet of Things
Machine learning
Mathematical programming
Optimization
Optimization algorithms
Regularization
Resource allocation
Simulation
Wireless networks
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Summary:In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm.
ISSN:1939-0114
1939-0122
DOI:10.1155/2021/8167796