Improved RSSI localization technique for mobile WSN using genetic operators

Sudhakar, K.; Anvekar, Dinesh

doi:10.1109/rteict42901.2018.9012582

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…The triangular measurement method needs to calculate the angle between the target and node, which undoubtedly increased cost. In [17], the author proposed a new sensor node localization scheme that improved the RSSI algorithm by considering power transmission and reception parameters to estimate the initial location of the node. A genetic algorithm is used to minimize the localization error, and the optimized coordinates are obtained by combining mutation and crossover operators.…”

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

Shao

2021

Journal of Sensors

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The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

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Section: Related Workmentioning

confidence: 99%