A localization algorithm for compensating stratification effect based on improved particle swarm optimization in underwater acoustic sensor network

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

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Dolphin swarm optimization algorithm for software‐defined antenna selection algorithm in underwater acoustic sensor network

International Journal of Communication Systems ◽

10.1002/dac.4903 ◽

2021 ◽

Author(s):

Sankar Ganesh S ◽

S. Rajaprakash

Keyword(s):

Sensor Network ◽

Optimization Algorithm ◽

Antenna Selection ◽

Acoustic Sensor ◽

Selection Algorithm ◽

Swarm Optimization ◽

Underwater Acoustic ◽

Underwater Acoustic Sensor Network

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Three-Dimensional Node Localization Algorithm Based on Communication Radius Refinement and Improved Particle Swarm Optimization

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210617142339 ◽

2021 ◽

Vol 14 ◽

Author(s):

Songhao Jia ◽

Cai Yang ◽

Haiyu Zhang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Three Dimensional ◽

Particle Swarm ◽

Localization Algorithm ◽

Node Localization ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Learning Factor

Background: With the development of the Internet of things, WSN node positioning is particularly important due to its core technology. One of the most widely used algorithms, the DV-hop algorithm, has many advantages, such as convenient operation, use of no additional equipment, etc. At the same time, it also has some disadvantages, like large location error and insufficient robustness. Particle swarm optimization algorithm is advantageous in dealing with nonlinear optimization problems. Therefore, the improved particle swarm optimization algorithm is introduced to solve the problem of inaccurate positioning. Objective: This study aimed to determine the problem of large positioning error in three-dimensional node localization algorithm. Furthermore, this paper proposes an intelligent node localization algorithm based on hop distance adjustment. The algorithm is used to optimize the hop number of nodes and make the distance calculation more accurate. At the same time, particle swarm optimization is used to intelligently solve the problem of choosing the most valuable node position. Methods: Firstly, this paper analyzes the errors caused by the 3D DV-hop localization algorithms. Then, a new method of distance estimation and coordinate calculation is provided. At the same time, mutation factor and learning factor based on the particle swarm optimization algorithm are introduced. Then, a three-dimensional node localization algorithm based on ranging error correction and particle swarm optimization algorithm is proposed. Finally, the improved algorithm is simulated and compared with similar algorithms. Results: The simulation results show that the proposed algorithm has good convergence. It improves the positioning accuracy without additional hardware conditions and effectively solves the problem of inaccurate node positioning. The proposed algorithm creatively combines the hop number correction and particle swarm optimization algorithm to improve the accuracy of node positioning and robustness. However，the amount of computation is increased. Conclusion: Overall, it is within acceptable limits. It is worthwhile to improve the performance with a little increase in the amount of computation. The algorithm is worth popularizing.

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