Modeling a Novel Self-Optimized Wolf Optimizer for a Heterogeneous Network Model for Energy and Node Lifetime Analysis

Summary

The research presents a protocol to implement and evaluate a self-optimized wolf optimizer (SOWO) for energy-aware clustering and routing in wireless sensor networks, with step-by-step settings and reproducible evaluation to improve lifetime, throughput, and residual energy.

Abstract

The vital services of surveillance, information collection, and data transmission from high-risk environments to safer locations are still provided by Wireless Sensor Networks (WSNs). These services are improved by the majority of energy-efficient routing protocols structured for this purpose. A homogeneous routing protocol is applied to decrease the energy utilization of far-off hubs more efficiently; however, the energy utilization rate is higher for this protocol, poorer dependability, and more unfavorable information broadcast to the Wireless Router (WR) or base station (BS) when employed for a longer timeframe. To overcome these drawbacks, a modified Self-Optimized Wolf Optimizer (SOWO) is employed in this research. Incorporating heterogeneous nodes into the current approach, selecting the head based on remaining energy introduces a multi-level interaction strategy throughout the connections. Employing an energy hole elimination method is the foundation of the developed routing technique. Each approach aims to extend the network's lifetime and reduce energy consumption. Based on the findings, the proposed routing scheme demonstrates superior consistency periods, residual energy, throughputs, and network lifespan compared to existing ones. The research addresses the classical clustered-WSN problem of maximizing lifetime and sustained delivery under tight per-node energy budgets while keeping load/fairness balanced. The simulation results show a 3.4% and 32.22% improvement in network stability and residual energy, respectively, over existing algorithms.

Introduction

Wireless Sensor Networks (WSN) and the Internet of Things (IoT) are widely applied to various technological issues¹. WSNs have been used in different conditions to help move items, such as robots executing different errands. From the origin, IoT has given basic help, particularly in collecting data from insecure fields². There are specific reasons why these techniques are currently being utilized in different systems³, for example, in horticulture, medical services, ecological observing, military investigation, structural management, traffic management, monitoring changes in the water level variat....

Protocol

This section describes the developed heterogeneous protocol in this segment. In this technique, the organization partitions the sensor hubs into four logical areas based on a pre-established edge distance. The gateway nodes and base station (BS) are put externally to the detecting field and separately at the network's center point. The hub whose distance from the gateway node is less than the pre-decided distance is allotted to fields 1 and 2. For this situation, the nodes broadcast the data to either the gateway node or the BS using direct communication. These nodes represent the homogeneous nodes. Suppose the internode space is larger than the pre-decided thresh....

Results

Here, MATLAB R2024a compares the developed heterogeneous routing protocols using simulations with routing protocols. In the simulation, the network of 100 detecting nodes is arbitrarily employed with a dimension of one node every 100 m. The WR nodes are located in the network at (50 m, 120 m) and (50 m, 50 m). Roughly 20 percent of homogeneous nodes with (m as 0.2 and as 1) have less energy than the heterogeneous nodes. After the deployment, all of the nodes remain stationary. The simulation variables used in th.......

Discussion

The proposed SOWO utilizes WRs and homogeneous nodes. The stable election protocol uses heterogeneous nodes as CHs and contains a BS node in the middle of the cluster surrounded by the sensor nodes. Increased energy is required if the base station is placed external to the region¹⁴. This leads to reduced energy, and the energy level reaches zero in a very short period. The proposed technique has a low energy reduction rate compared to traditional methods. This proves that the energy conservation m.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
12th Gen Intel(R) Core(TM) i5-1235U (1.30 GHz)	Intel Corporation, USA	–	Hardware used for simulation execution
16 GB DDR4 RAM	Kingston Technology, USA	–	Memory used during simulation runs
MATLAB	MathWorks USA	R2024a	Used for implementing algorithms, running WSN simulations, and analyzing results
Microsoft Windows 11 Home	Microsoft Corporation, USA	Build 22631	Operating system used to run simulations
Synthetic Dataset Generated in MATLAB	MathWorks, USA	R2024a	Custom dataset created for algorithm testing