Abstract
To address the problems of high energy consumption and time delay of the offloading strategies in traditional edge computing, a computation offloading strategy for the Internet of Things (IoT) using the improved Particle Swarm Optimization (PSO) in edge computing is proposed. First, a system model and an optimization objective function are constructed based on the communication model for the uplink transmission and the multiuser personalized computation task load model while considering constraints from multiple aspects. Then, the PSO is used to update the position of particles by encoding them and calculating the fitness values to find the optimal solution of the task offloading strategies, which greatly reduces the energy consumption during the task allocation process in the system. Finally, the simulations are conducted to compare the proposed method with two other algorithms in terms of the average time delay and energy consumption under different numbers of user mobile devices and data transmission rates. The simulation results showed that the average time delay and energy consumption of the proposed method are the smallest in different cases. And, the average delay and energy consumption are 0.205 s and 0.2 J, respectively, when the number of users’ mobile devices is 80, which are better than the other two comparison algorithms. Therefore, the proposed method can reduce the task execution delay with less energy consumption.
Highlights
In recent years, with the rapid development of mobile Internet and wireless communication technology, the human society has entered the 5G era [1]
It is clear that the numerous objective function constraints make it much more difficult to obtain the optimal computation offloading strategy. erefore, an improved Particle Swarm Optimization (PSO) is introduced to find an optimal solution of task offloading strategy
Based on the particle swarm algorithm, the optimal solution of task offloading strategy is obtained by encoding the particles and calculating the fitness value so as to update the positions of different particles, which minimizes the energy consumption produced by the system task allocation. e proposed strategy is analyzed by simulation experiments
Summary
With the rapid development of mobile Internet and wireless communication technology, the human society has entered the 5G era [1]. As the sudden task offloading of mobile users when they are located in hotspots may lead to overloading of several edge servers, a load balancing algorithm for mobile devices in edge cloud computing environments based on genetic algorithms was given by Lim and Lee [18] This algorithm does not consider the minimization of the task latency and the network energy consumption. To solve the problem of limited computational resources in edge computing architectures, Shi et al studied the problem of cross-server computation offloading and the collaboration between multiple edge servers for multitask mobile edge computing and proposed a greedy approximation algorithm, which can greatly reduce the overall energy consumption [20] This method does not give a specific approach to improve the QoS of users. The individual particles are encoded by introducing the improved particle swarm algorithm to improve the algorithm’s performance in finding the optimal solution, compared with the traditional computation offloading strategies for IoT
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