IJCNC 04

EFFICIENT ENERGY-BASED CLUSTER HEAD ELECTION AND ROUTING PROTOCOL IN WSNS

Ahmad Abuashour

Department of Information Technology and Computing, Arab Open University, Arab Open University, Kuwait City, Kuwait

ABSTRACT

Wireless Sensor Networks (WSNs) comprise spatially distributed sensor nodes with limited energy and processing capabilities. Energy efficiency remains a critical concern in WSNs, as it directly impacts network stability and lifetime. Extensive research has demonstrated that clustering techniques enhance energy efficiency by improving communication reliability and extending operational longevity. In clustering, designated Cluster Heads (CHs) perform intra-cluster data aggregation and inter-cluster communication with other CHs or the sink node. This article introduces the Efficient Energy-Based Cluster Head Election (EECHE) protocol, which dynamically selects CHs based on two key criteria: residual energy and inter-CH distance. Re-clustering is initiated whenever these criteria are violated. Additionally, we propose the Efficient Energy-Based Routing (EEPR) protocol, which facilitates forwarding of aggregated data from Cluster Members (CMs) to the sink via candidate CHs. The optimal forwarding route is selected by evaluating both the shortest route and energy link stability, quantified through the longest expected route energy lifetime. By integrating these protocols with topology-aware constraints, the proposed approach achieves balanced energy distribution and maximizes the network performance. Mathematical analysis and simulation results demonstrate that the proposed protocols significantly enhance network performance—achieving higher throughput, lower delay, and improved packet delivery ratio—when compared to conventional protocols such as LEACH, ELEACH, Q-LEACH, and FLQLEACH. Simulation results show that the proposed EECHE–EEBR framework improves throughput by up to 50%, reduces energy consumption by approximately 47%, and decreases end-to-end delay by nearly 35% compared with the LEACH protocol. These results confirm the effectiveness of integrating energy awareness, spatial constraints, and route stability into a unified framework for clustering and routing

KEYWORDS

WSN, Energy-based, CH election, routing, LEACH, EECHE, EEBR.

1. INTRODUCTION

A Wireless Sensor Network (WSN) is a fundamental component of the Internet of Things (IoT), consisting of small, resource-constrained sensor nodes integrated into network infrastructures. Due to limited energy, storage, and processing capabilities, these nodes mainly perform data sensing, buffering, and forwarding operations. WSNs support a wide range of applications, including environmental monitoring, industrial automation, smart agriculture, and healthcare systems [1–3]. Typically, a WSN comprises spatially distributed sensor nodes that cooperatively collect physical or environmental data and transmit it to a Base Station (BS) or sink for further analysis [4–6].

Since WSNs are often deployed in remote or unattended environments, sensor nodes are typically powered by non-rechargeable batteries. Consequently, energy efficiency becomes a critical concern in both the design and operation of these networks. Each sensor node is responsible for two primary functions: sensing and collecting data from the surrounding environment, and relaying that data through neighboring nodes until it reaches the BS [7-9].

Among various network architectures, clustered topology is widely adopted in WSNs due to its scalability and energy efficiency. In this structure, sensor nodes are organized into hierarchical clusters consisting of Cluster Heads (CHs) and Cluster Members (CMs), where CHs are responsible for data aggregation and forwarding to the sink. By optimizing communication and reducing redundant transmissions, clustering effectively lowers energy consumption, particularly in dense network deployments [10].

In clustered WSNs, clustering approaches are generally classified into two categories: stationary clustering and dynamic clustering. In stationary clustered networks, the sensing area is partitioned into predefined subregions of equal size. After sensor nodes are randomly deployed, a CH election process is initiated within each subregion. Each static region functions as a cluster and selects a single CH, while the remaining nodes serve as CMs. The CH selection is typically based on one or more criteria such as the node’s distance from the cluster center, node mobility parameters, residual energy, or estimated node lifetime [11–18, 24].

Conversely, dynamic clustering does not rely on predefined cluster boundaries. Instead, clusters are formed on-the-fly based on the selection of CHs. Various methods have been proposed for CH election in dynamic clustering. The most basic technique involves random selection from the entire set of nodes, while more advanced approaches incorporate factors such as node location, energy level, and other performance-related metrics [18–25].

Table 1 presents a summary of the key CH election and routing criteria used in the most popular
clustered WSNs, along with the strengths and limitations of each method.

Routing strategies in WSNs aim to improve energy efficiency and communication reliability. Protocols such as LEACH [26] and SEP [29] employ single-hop transmission from Cluster Heads (CHs) to the sink, which often leads to rapid energy depletion, especially for distant CHs. QLEACH [27] enhances CH distribution using quadrant-based clustering but does not support multi-hop routing. E-LEACH [28] considers residual energy and distance in CH selection but still relies on direct communication. DEEC [30] and HEED [31] introduce energy-aware mechanisms but neglect link quality and network dynamics. FL-EEC/D [32] applies fuzzy logic to incorporate energy and node density, while FL-Q-UCR [33] combines fuzzy inference with Q-learning to enable adaptive routing. However, these intelligent approaches impose high computational overhead. Therefore, a lightweight and robust routing protocol that integrates energy efficiency, topological awareness, and link stability remains essential.

Based on the analysis presented in Table 1, the proposed Efficient Energy-Based Cluster Head Election (EECHE) protocol selects Cluster Heads (CHs) using residual energy and inter-cluster distance to achieve balanced cluster formation. In parallel, the Efficient Energy-Based Routing (EEBR) protocol employs a multi-hop strategy guided by route energy evaluation to ensure reliable and energy-efficient data transmission. By integrating optimized clustering with energyaware and stable routing, the proposed framework enhances network stability, robustness, and lifetime while ensuring fair resource distribution.

The remainder of this article is structured as follows: Section 2 presents a comprehensive review of related work. Section 3 introduces the system and mathematical models underlying the proposed protocols. Section 4 : Performance metrics calculation and numerical validation of the approach. Section 5 describes the implementation of the proposed solution in MATLAB and compares the analytical findings with simulation results. Section 6 discusses the performance evaluation and presents a detailed analysis of the results. Finally, Section 7 concludes the study and outlines potential directions for future research.

2. RELATED WORK

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [34] is one of the earliest cluster-based routing approaches for WSNs, designed to reduce energy consumption through periodic rotation of Cluster Heads (CHs). However, its random CH election mechanism often leads to uneven cluster distribution and inefficient energy usage, as it ignores important factors such as residual energy, node location, network density, and link quality.

The limited energy supply, constrained computational resources, and lack of fixed infrastructure in WSNs necessitate the development of efficient and intelligent clustering protocols. A major challenge is the selection of suitable Cluster Heads (CHs) that can support data aggregation and transmission while extending network lifetime. Several improvements have been proposed to address this issue. For example, LEACH-F [35] employs fixed CHs to reduce reclustering overhead; however, this approach does not adapt to dynamic energy depletion, which may lead to early node failure.

The Hybrid Energy-Efficient Distributed Clustering (HEED) protocol [36] selects Cluster Heads (CHs) based on residual energy and intra-cluster communication cost; however, it neglects node density and link stability. The Stable Election Protocol (SEP) [37] improves network stability by assigning weighted CH election probabilities according to initial energy levels, but it assumes static energy distributions and lacks adaptive re-evaluation. The Distributed Energy-Efficient Clustering (DEEC) protocol [38] dynamically estimates average network energy for CH selection, making it suitable for static topologies, although it does not consider link quality or node density.

Table 2 provides a comparative overview of key cluster-based routing protocols developed for WSNs. The table highlights the evolution from early approaches like LEACH, which introduced basic clustering concepts, to more advanced techniques incorporating energy awareness, spatial distribution, and intelligent decision-making using fuzzy logic and machine learning. While many protocols have made significant strides in addressing energy efficiency and clustering optimization, several still suffer from limitations such as high computational complexity, lack of adaptability, or limited consideration of network dynamics. This comparison underscores the need for a balanced, low-overhead protocol that integrates energy, topology, and stability considerations.