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On precise path planning algorithm in wireless sensor network
Author(s) -
Farhanda Javed,
Samiullah Khan,
Asfandyar Khan,
Alweena Javed,
Rohi Tariq,
Matiullah,
Faheem Khan
Publication year - 2018
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147718783385
Subject(s) - computer science , energy consumption , wireless sensor network , node (physics) , algorithm , path (computing) , real time computing , path length , energy (signal processing) , motion planning , efficient energy use , computer network , artificial intelligence , mathematics , statistics , electrical engineering , structural engineering , robot , engineering
Wireless sensor network is a collection of small devices called sensors nodes, which are deployed in the sensing field to monitor physical and environmental information. Location information of sensor node is a critical issue for many applications in wireless sensor network. The main problem is to design a path for a mobile landmark to maximize the location accuracy as well as to reduce energy consumption. Different path planning schemes have been proposed for localization. Here, this study focused only on static path planning scheme. In this article, the performance of five static path planning schemes is evaluated, namely, random way point, Scan, D-Scan, Hilbert, and Circles based on three parameters such as location error ratio, energy consumption, and number of references. Network simulator-2 is used as a simulation tool. Simulation scenarios with three node densities are used in this research study such as sparse node density, medium node density, and dense node density. The analysis of simulation results concludes that random way point has higher performance efficiency compared to rest of the static path planning algorithms concerning location error ratio (accuracy), energy consumption, and number of references in medium and dense node density scenarios. Hilbert performance was found good only in sparse node density scenario.

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