Figure - available from: IET Wireless Sensor Systems
This content is subject to copyright. Terms and conditions apply.
Source publication
![The physical nodes equipped on the float in this aquaculture network [23].](publication/369243725/figure/fig2/AS:11431281179284528@1691169040092/The-physical-nodes-equipped-on-the-float-in-this-aquaculture-network-23_Q320.jpg)
Localization is one of the essential problems in wireless sensor applications (WSNs). Most range-free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be so...
Citations
... Since the nodes become high cost and more utilization of energy, the WSN fails to determine the location of unknown nodes. Some localization algorithms face three most challenging issues as follows: (i) the ranging problem as Non-Line-Of-Sight (NLOS), which occurs when any hindrance or obstacles are blocked to compute the distance among non-anchor nodes and anchor nodes [32]. It leads to rendering the maximum localization error, and (ii) the accuracy and energy consumption come under another challenging issue. ...
The evolutionary growth of Wireless Sensor Networks (WSN) exploits a wide range of applications. To deploy the WSN in a larger area, for sensing the environment, the accurate location of the node is a prerequisite. Owing to these traits, the WSN has been effectively implemented with devices. Using various localization techniques, the information related to node location is obtained for unknown nodes. Recently, node localization has employed the standard bio-inspired algorithm to sustain the fast convergence ability of WSN applications. Thus, this paper aims to develop a new hybrid optimization algorithm for solving the node localization problems among the unknown nodes in WSN. This hybrid optimization scheme is developed with two efficient heuristic strategies of Black Widow Optimization (BWO) and Honey Badger Algorithm (HBA), named as Hybridized Black Widow-Honey Badger Optimization (HBW-HBO) to achieve the objective of the framework. The main objective of the developed heuristic-based node localization framework is to minimize the localization error between the actual locations and detected locations of all nodes in WSN. For validating the developed heuristic-based node localization scheme in WSN, it is compared with different existing optimization strategies using different measures. The experimental analysis proves the robust and consistent node localization performance in WSN for the developed scheme than the other comparative algorithms.
