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A Node Location Algorithm Based on Improved Grasshopper Optimization in Wireless Sensor Network


Affiliations
1 Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India
     

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As the use of swarm intelligence algorithms grows, so does the interest in placing nodes in a Wireless Sensor network. For this reason, the RSSI range model positioning algorithm has been replaced by a more accurate one. With the help of this paper, you can solve complex structural optimization problems with the Grasshopper Optimization Algorithm (GOA). Optimization problems can be solved using this algorithm, which was inspired by the behaviour of grasshopper colonies. CEC2005 is used to test the GOA algorithm quality and quantitative performance. Trusses with a total of 53 and 3 cantilever beams are used to demonstrate the design practicality. It appears that the proposed algorithm outperforms well-known and recently developed algorithms in this area. GOA ability to solve real-world problems with unknown search spaces is demonstrated by its use in the real world.

Keywords

WSN (Wireless Sensor Network), GOA (Grasshopper Optimization Algorithm), RSSI, Whale Optimization Algorithm
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  • A Node Location Algorithm Based on Improved Grasshopper Optimization in Wireless Sensor Network

Abstract Views: 207  |  PDF Views: 1

Authors

D. Archana
Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India
S. Prakasam
Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India

Abstract


As the use of swarm intelligence algorithms grows, so does the interest in placing nodes in a Wireless Sensor network. For this reason, the RSSI range model positioning algorithm has been replaced by a more accurate one. With the help of this paper, you can solve complex structural optimization problems with the Grasshopper Optimization Algorithm (GOA). Optimization problems can be solved using this algorithm, which was inspired by the behaviour of grasshopper colonies. CEC2005 is used to test the GOA algorithm quality and quantitative performance. Trusses with a total of 53 and 3 cantilever beams are used to demonstrate the design practicality. It appears that the proposed algorithm outperforms well-known and recently developed algorithms in this area. GOA ability to solve real-world problems with unknown search spaces is demonstrated by its use in the real world.

Keywords


WSN (Wireless Sensor Network), GOA (Grasshopper Optimization Algorithm), RSSI, Whale Optimization Algorithm

References