Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Abstract

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

Analysis and performance evaluation of computation models for node localization in deep sea using UWSN

“…The process of localization is performed in two parts: (1) Offline phase for training EELA, train data for several routing protocols, (2) online phase, the anchor node sends the awakening signal with the least amount of transmission energy to begin the localization process. 51 iii. Iterative localization mechanism based on error control (ILMEC): Prior to localization, ILMEC develops a multi-path generation approach to establish the localization order and transmission power of each known node.…”

“…Inside this approach, the regular node serves as the leader, while the reference node serves as the follower, capable of locating nodes by using the least amount of power. Adaptive EELA: It employs a fuzzy player approach, whereas the Stackelberg game is used to design the interactions between sensor and anchor nodes in UWSNs, as well as this technique is used to fix the appropriate usage activities. The process of localization is performed in two parts: (1) Offline phase for training EELA, train data for several routing protocols, (2) online phase, the anchor node sends the awakening signal with the least amount of transmission energy to begin the localization process 51 Iterative localization mechanism based on error control (ILMEC): Prior to localization, ILMEC develops a multi‐path generation approach to establish the localization order and transmission power of each known node.…”

Game theory based hybrid localization technique for underwater wireless sensor networks

“…RSS-based energy-efficient localization algorithm (EELA) 25,26 Transmission power Provide optimal solution for efficient power usage.…”

“…Yuan et al 26 have suggested the “Adaptive Energy‐Efficient Localization Algorithm” named as adaptive EELA. This algorithm consumes less energy of nodes whilst responding to dynamic topology variations.…”