Abstract
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.
Highlights
With the gradual popularization of 5G technology, the Internet of Everything (IoE) is no longer a fantasy
According to Cisco’s estimation, there will be 29.3 billion devices connected to network worldwide by 2023, of which about 14.7 billion Internet of ings (IoT) devices will be connected to the Internet, roughly accounting for 50% of networking devices [1]. e IoT has been applied to various fields, which brings huge breakthroughs, especially in human-computer interaction, artificial intelligence (AI), augmented reality (AR), smart cities, smart healthcare, etc
We propose a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm to solve optimization problem P1. is algorithm achieves the optimal offloading decision based on deep neural networks (DNN) with both offloading decision and resource allocation taken into account, and it is combined with the momentum gradient descent to optimize the allocation of resources, further significantly reducing the system energy consumption
Summary
With the gradual popularization of 5G technology, the Internet of Everything (IoE) is no longer a fantasy. (i) Based on the joint consideration of offloading decision and resource allocation, an optimization problem is formulated to minimize the total energy consumption of all terminal devices. E objective of minimizing total energy consumption is achieved by jointly optimizing the computation offloading decision, time allocation ratio of energy harvesting, and CPU computing capability of the local device. Is algorithm constructs a deep neural network architecture incorporating regularization method, and the training process is rapidly converged by adding regularization paradigm in the network parameter updating process It can obtain the optimal local computing capability and time allocation ratio of energy harvesting and achieve the minimization of total energy consumption for all terminal devices by integrating the momentum gradient descent.
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