Design of a New Lightweight Accurate Grid-Based Localization Method for WASN

Abstract

Existing solutions designed for acoustic target localization based on the received signal energy in WSN suffer either from low localization accuracy due to suboptimal convergence or high computations requirements due to the application of complex algorithms. The main contribution of this paper is the design of a new lightweight energy-based acoustic source localization solution intended for implementation in wireless sensors. The novelty of this solution resides in devising lightweight computation of the cost function in the location estimator algorithm and the application of the Tabu metaheuristic method to enhance the localization accuracy. In terms of localization accuracy, the proposed scheme outperforms many existing solutions with reduced localization error by up to 88% while using only three energy measurements, which saves around 85% of node energy. Furthermore, the performance analysis results showed a stable behavior of the proposed algorithm across a wide range of parameters variations of the energy-decay model, which demonstrates the robustness of the scheme.

The hardware simulation of ratio metric vector iteration algorithm in wireless sensor networks

“…This solution developed a new formula using tabu simulations to increase localization accuracy and energy efficiency. This algorithm shows stable behaviour over several parameter variables for the power decay model (Al-Quayed et al, 2021).…”

“…However, energy consumption for data transmission and node utilization and node failure for cost and size may occur. Moving sensors for acoustic WSN, for example, underwater measurement, cannot use GPS to determine position (Strumberger et al, 2019;Singh and Mittal, 2020;Al-Quayed et al, 2021).…”

A Study on Energy Efficiency of a Distributed Processing Scheme for Image-Based Target Recognition for Internet of Multimedia Things

“…One of the potential solutions to this problem would be to process the captured image locally and to communicate only the useful data to the remote-control server through the network. While this approach looks very suitable for the image-based recognition application, it needs a careful effort to design a low-complexity sensing scheme that provides a tradeoff between the accuracy of target recognition and the energy-saving at the source sensor [7,8]. The accuracy of the target recognition in this approach depends basically on the efficiency of the extracted features to ensure a high discrimination level between different objects.…”