TY - GEN
T1 - Comparative analysis of PSO-SGO algorithms for localization in wireless sensor networks
AU - Nagireddy, Vyshnavi
AU - Parwekar, Pritee
AU - Mishra, Tusar Kanti
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - The Wireless sensor networks (WSN) combine autonomous wireless electronic devices which have abilities like sensing, processing, and communication. It is a self-organizing network constructed with immense number of sensors. Localization is about detecting a node at particular geographical position usually titled as range. Nodes in WSN can be installed uniformly, with formation of grid or randomly. When nodes are installed randomly it is important to determine the exact location of the node. But this approach is expensive and not always feasible using geographical positioning system (GPS). It will not provide definite location results in indoor surroundings. The challenging task of WSN includes improving accuracy in approximating position of a sensor node based on anchor nodes. They are incorporated in a network, such that their coordinates play an essential role in location estimation. A well-organized localization algorithm is capable of determining the accurate coordinates for position of nodes by making reference from sensor nodes. Optimization algorithms like Particle swarm optimization (PSO) and Social group optimization (SGO) are implemented with the fitness equation and the performance of both the algorithms are compared. This paper projects a fitness equation such that the results of PSO and SGO are validated by comparing error accumulation factor in both the algorithms.
AB - The Wireless sensor networks (WSN) combine autonomous wireless electronic devices which have abilities like sensing, processing, and communication. It is a self-organizing network constructed with immense number of sensors. Localization is about detecting a node at particular geographical position usually titled as range. Nodes in WSN can be installed uniformly, with formation of grid or randomly. When nodes are installed randomly it is important to determine the exact location of the node. But this approach is expensive and not always feasible using geographical positioning system (GPS). It will not provide definite location results in indoor surroundings. The challenging task of WSN includes improving accuracy in approximating position of a sensor node based on anchor nodes. They are incorporated in a network, such that their coordinates play an essential role in location estimation. A well-organized localization algorithm is capable of determining the accurate coordinates for position of nodes by making reference from sensor nodes. Optimization algorithms like Particle swarm optimization (PSO) and Social group optimization (SGO) are implemented with the fitness equation and the performance of both the algorithms are compared. This paper projects a fitness equation such that the results of PSO and SGO are validated by comparing error accumulation factor in both the algorithms.
UR - https://www.scopus.com/pages/publications/85061610052
UR - https://www.scopus.com/pages/publications/85061610052#tab=citedBy
U2 - 10.1007/978-981-13-3329-3_37
DO - 10.1007/978-981-13-3329-3_37
M3 - Conference contribution
AN - SCOPUS:85061610052
SN - 9789811333286
T3 - Advances in Intelligent Systems and Computing
SP - 401
EP - 409
BT - Information Systems Design and Intelligent Applications - Proceedings of 5th International Conference INDIA 2018 Volume 1
A2 - Senkerik, Roman
A2 - Satapathy, Suresh Chandra
A2 - Bhateja, Vikrant
A2 - Somanah, Radhakhrishna
A2 - Yang, Xin-She
PB - Springer Verlag
T2 - 5th International Conference on Information System Design and Intelligent Applications, INDIA 2018
Y2 - 19 July 2018 through 20 July 2018
ER -