Hybrid Bird Swarm Optimized Quasi Affine Algorithm Based Node Location in Wireless Sensor Networks

2021 ◽  
Author(s):  
E. M. Malathy ◽  
Mythili Asaithambi ◽  
Alagu Dheeraj ◽  
Kannan Arputharaj
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Location

Privacy Intensity Policy for Optimizing Source Node Location in Wireless Sensor Networks

2020 ◽  
Vol 57 (24) ◽  
pp. 241017
Author(s):  
蒋占军 Jiang Zhanjun ◽  
周涛 Zhou Tao ◽  
杨永红 Yang Yonghong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Source Node ◽  
Wireless Sensor ◽  
Node Location

scholarly journals A Node Location Method in Wireless Sensor Networks Based on a Hybrid Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Zhigang Du ◽  
Huiqi Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Search Space ◽  
Wireless Sensor ◽  
Practical Applications ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Node Location ◽  
Received Signal Strength Indication

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

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