No abstract

The main objective of this research paper is to review and analyze the different existing localization algorithms techniques that are used to overcome the localization issue in the wireless sensor network. Underwater Wireless sensor networks consists up of small sensor nodes that are placed in huge quantity over a large water surface region to perform several tasks like sensing the data and communicate with other devices. Most of the applications of underwater wireless sensor networks like forest fire detection required the exact position of the sensing element. The main motive of the localization process is to localize the coordinates to the every node with unidentified location in the sensing area of underwater. In this paper, we have discussed various localization algorithms for localizing the sensor nodes like particle swarm optimization; bees optimization algorithm, bat algorithm, cuckoo optimization and butterfly optimization algorithm etc. are reviewed. The detail analysis of these techniques in terms of localization error, computation time and amount of localized nodes has been discussed in this paper.

In Wireless Sensor Network (WSN), localization process is considered as a major challenge which is intended to maximize with minimized traveling distance of the beacon node. Further, the important issue is to improve coverage area of the anchor-based node and accuracy in calculation of the location of nodes. This paper mainly focuses on an enhanced path planning model using beacon node based upon their location. The proposed model focuses to improve coverage of the network topology by moving in zig-zag path fashion so that it will enhance the reachability of message in almost every possible corner of the deployed area. The proposed model is simulated extensively in a self-simulator with different scenarios and compared with SCAN and anchor-based model. The tested performance of the model is presented along with its analytical model. The simulation result shows that the proposed model gives the better performance as compared to all others existing model in terms of percentage of nodes settled and energy consumption.