MEC-MS: A novel optimized coverage algorithm with mobile edge computing of migration strategy in WSNs

Author:

Sun Zeyu1,Liao Guisheng2,Zeng Cao2,Lv Zhiguo3,Xu Chen4

Affiliation:

1. National Key Laboratory of Radar Signal Processing, Xidian University, Xian, China + School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China + Collaborative Innovation Center of Information Sensing, Xidian University, Xian, China

2. National Key Laboratory of Radar Signal Processing, Xidian University, Xian, China + Collaborative Innovation Center of Information Sensing, Xidian University, Xian, China + International Coopreation Base of Integrated Electronic Information System of Ministry of Science and Technology, Xidian University, Xian, China

3. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China

4. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

Abstract

The traditional network coverage mode with the cost of deploying a large number of sensor nodes has poor coverage effect. Aiming at this problem, this paper proposes a Novel Optimized Coverage Algorithm with Mobile Edge Computing of Migration Strategy (MEC-MS). First, the algorithm uses the network coverage model to give the expression method of the distance measurement and the judgment conditions of the best and worst paths. Secondly, it analyzes the necessary conditions for improving the coverage quality and the prerequisite for the existence of redundant coverage for adjacent the redundant coverage nodes by the theory of probability. Thirdly, using the precondition of redundant coverage, we give the calculation process of the sensor nodes own redundant coverage and the calculation method of the redundant node coverage expectation. Finally, the algorithm compares the number of working sensor nodes with the other two algorithms under different parameters. The experimental results show that the average number of working sensor nodes in the MEC-MS algorithm is 9.74% lower than that of the other two algorithms, and the average value of network coverage is 9.92% higher than that of the other two algorithms, which verify the effectiveness of the algorithm in this paper.

Publisher

National Library of Serbia

Subject

General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Iot Data Processing and Scheduling Based on Deep Reinforcement Learning;INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL;2023-10-30

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