Balancing Energy Consumption and Network Longevity: A Review of LEACH Protocol Enhancements through Computational Intelligence

Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 723 Samuel Asare et al 723-744 Balancing Energy Consumption and Network Longevity: A Review of LEACH Protocol Enhancements through Computational Intelligence Samuel Asare 1*, William P. Rey 2 1 Mapua University / School of Graduate Studies, Manila, Philippines 2 Mapua University / School of Information Technology, Manila, Philippines Email: [email protected]; [email protected] Abstract Low Energy Adaptive Clustering Hierarchy (LEACH) is the most widely used protocol for clustering in wireless sensor networks (WSNs). These networks are composed of numerous Sensor Nodes (SNs) that are employed for data monitoring and collection from the environment. We utilized the Systematic Literature Review (SLR) approach to study a total of 960 papers from 2020 to 2025 using IEEE Xplore, Web of Science, Springer Link, Science Direct, and Scopus databases. Google Scholar was used as an additional database for verification purposes only. Research gaps and potential directions for hybrid model development were identified based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study focused on LEACH protocol enhancements through computational intelligence associated with WSNs. Reviews indicate that 22% of the reviewed papers used artificial intelligence (AI) approach, 48% of the reviewed papers used fuzzy approach, and 30% of the reviewed papers used genetic approach based on adaptability and scalability. Each of the proposed approaches consistently outperforms traditional LEACH in energy balance but couldn’t balance energy consumption and network longevity with optimal performance. The findings demonstrate the persistent challenges in communication overhead, scalability, and adaptability. The paper argued that hybrid computational intelligence frameworks offer the most promising direction for enhancing LEACH-based WSNs toward sustainable, energy-efficient, and scalable performance. Keywords: Artificial Intelligence, Clustering Algorithms, Computational Intelligence, Data Aggregation, Energy Consumption, Fuzzy Logic, Genetic Algorithm, LEACH Protocol, Machine Learning, Network Lifetime, Protocol Optimization, WSNs I. INTRODUCTION Wireless Sensor Networks are the foundation of the technology used today for data collection and monitoring systems. These networks are ideal for more complex real-time systems due to their capabilities in basic data collection and monitoring. These networks make it possible to develop applications such as factory automation and smart homes by sensing the environment and collecting data from the environment using tons of sensor nodes [1]. However, these sensor nodes usually employ non-rechargeable batteries, and they have great limitations in energy, which causes intractable problems in terms of the network lifetime and efficiency [2]. LEACH protocol is preferred as a solution to address these challenges [3]. Moreover, LEACH's hierarchical structure groups sensor nodes into clusters, and designated Cluster Heads (CHs) aggregate data and send it to a Base Station (BS) [4]. This clustering technique lowers the energy usage related to data transfer by distributing the energy load evenly among nodes, and also Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 724 Samuel Asare et al 723-744 improves the overall network performance [5]. The LEACH protocol has drawbacks with regard to the random selection of the CH despite its numerous benefits. This can result in unequal energy consumption and the formation of hotspots in the network [6]. A. Challenges of Energy Consumption and Network Longevity in WSNs WSNs face serious challenges related to energy consumption during the operation of the LEACH protocol which directly impacts their operational lifespan and overall longevity. Ensuring energy efficiency to extend the lifespan of a network is the main goal in WSNs [7]. WSN nodes rely on battery power which makes energy conservation necessary for prolonged operation. Without efficient management of energy consumption, there will be node failures and reduced network coverage reduction of battery power [8]. Furthermore, unequal energy consumption of nodes during operation of the LEACH protocol in WSNs can lead to reduction of energy. This leads to premature node failures, creating gaps in network coverage and reducing overall network resilience [9]. The overall lifespan of a WSN is directly connected to the energy efficiency of its individual nodes and the network's ability to manage energy consumption effectively. These challenges of the LEACH protocol in WSNs are serious in environments where battery replacement is difficult or impossible [10]. B. Challenges of LEACH Protocol Enhancements through Computational Intelligence Several studies have suggested different improvements in an effort to find a solution to the problems of the LEACH protocol. Computational intelligence methods such as fuzzy logic, genetic algorithms, and machine learning were utilized in their research. The improvements aimed to maximize the choice of CHs by considering several factors, such as node density, distance to the BS, and residual energy [11]. Moreover, these enhancements were introduced to help improve the reliability of data transmission, network longevity, and energy efficiency by strategically adding computational intelligence features to the base LEACH approach. To evaluate performance enhancements to the LEACH protocol, key performance metrics including network lifetime, energy consumption, and scalability were measured [12]. C. Approaches to Enhance LEACH Protocol through Computational Intelligence Table 1: Summary of Computational Intelligence to enhance LEACH Protocol Method Definition Advantage(s) Limitations AI driven methods Computational approaches that use artificial intelligence techniques to automate decision making, optimize processes, and adapt intelligently to complex problems Complex tasks are automated saving human effort. Learns from data to make better, data informed decisions. Requires large, highquality datasets. Energy and performance intensive Fuzzy Logic A computational approach that handles reasoning with uncertainty, using degrees of truth (0 to 1) instead of strict binary values, enabling approximate reasoning Improves the network lifetime by optimal selection of the cluster head. Takes care of ambiguous sensor The additional processing may be a burden on sensor battery. It is challenging in dynamic WSNs to Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 725 Samuel Asare et al 723-744 similar to human decisionmaking data for trustworthy tracking and routing. construct effective fuzzy rules. Genetic Algorithms Techniques, based on natural evolution, which use selection, crossover and mutation to approach near optimal solutions for complex problems such as routing and energy management in WSNs It achieves near optimal selection of cluster head to balance the consumption of energy. Can easily cope with dynamic WSN characteristics, such as node failure or topology change Iterative computations can quickly drain sensor nodes. Slow Convergence to reach optimal solutions in WSNs Table 1 shows a summary of computational intelligence to enhance LEACH Protocol D. Motivation This systematic literature review (SLR) is motivated by the need to balance the energy consumption and network longevity through computational intelligence. There is the need to integrate computational intelligence in LEACH protocol to balance the energy consumption and network lifetime [13]. In addition, existing approaches for balancing energy consumption and network longevity in LEACH protocol were reviewed. In addition, this paper describes the importance of optimizing energy consumption and extending network lifespan in WSN. E. Problem Statement Balancing the amount of power utilized by the sensor nodes to prolong the lifespan of the network is a challenge for the existing techniques in data-gathering sensors [14]. A key design objective for WSNs is to balance energy consumption and increase the network longevity. Recent studies have shown the importance to manage battery power in WSNs [15]. Selecting heads and relay nodes is necessary to balance the energy among the sensor nodes by considering different metrics of WSNs [16]. WSNs support a great number of applications with changing environments which need adaptive and scalable communication protocols [17]. The design of protocols that accomplish high performance in data transmission and low energy consumption to balance the amount of power utilized by the sensor nodes is a challenge. The optimal deployment schedule of the network is very essential to reduce coverage holes and make full use of network energy. Poor deployment can result in energy waste and blind spots in monitoring [18]. F. Scope This article presents a review of LEACH protocol techniques for balancing energy consumption and network longevity in WSNs using computational intelligence. A substantial part of the paper is focused on the energy efficiency which is a crucial factor, since nodes in a WSN are battery operated and they are often placed in changing environments. It discusses the use of computational intelligence in enhancing energy consumption in detail, and highlights the challenges in terms of flexibility and scalability. The paper aims at optimal balance of energy consumption and network longevity in WSNs. Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 726 Samuel Asare et al 723-744 G. Objective The focus of this SLR is to provide a comprehensive overview about computational intelligence based enhancements in the LEACH protocol to upgrade energy consumption and network lifetime. This paper will discuss the various techniques applied in order to enhance a network’s lifetime and energy consumption. In addition, it will consider the effectiveness and implications of such enhancements on future research and practical WSN deployment. By systematic review of the existing literature, this will increase understanding of how computational intelligence may be applied to enhance performance of LEACH based protocols in energy constrained environments. II. RESEARCH METHODOLOGY We followed the systematic approach to review the existing literature on energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for this review. It includes defining the research questions, selecting databases, developing search strings, establishing of inclusion exclusion criteria, and applying a quality assessment framework. The methodology is organized as follows: A. Data Sources and Search Strategy To ensure comprehensive coverage of relevant studies, the search was conducted across five (5) academic databases. These databases are IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and Scopus. Google Scholar was used as an additional database for verification purposes only. Table 2: Selection of search keywords Method Keywords AI driven method (‘AI-Driven Energy Optimization in WSN’ OR Intelligent Routing Protocols in WSN) AND (Network Lifetime Enhancement in WSN’ OR ‘Energy Consumption Minimization in WSN’) (‘Machine Learning-based Clustering in WSN’ OR ‘Reinforcement Learning Optimization in WSN’) AND (‘Data-Driven Network Management in WSN’ OR ‘Adaptive Energy Control Mechanisms using Computational Intelligence’) Fuzzy Logic (‘Fuzzy Logic Optimization in WSN’ OR ‘Energy-Efficient Routing in WSN’) AND (‘Cluster Head Selection in WSN’ OR ‘Network Lifetime Enhancement in WSN’) (‘Energy Balancing Mechanism in WSN’ OR ‘Multi-criteria Decision Making’) AND (‘Adaptive Clustering Algorithm in WSN’ OR ‘Residual Energy Estimation using Computational Intelligence’) Genetic Algorithms (‘Genetic Algorithm Optimization in WSN’) OR (‘Energy-Efficient Routing Protocol in WSN’) AND ('Network Lifetime Maximization in WSN’ OR ‘Energy Consumption Balancing in Cluster Head Selection’) (‘Intelligence Computation in Adaptive Clustering Mechanism’ OR ‘Fitness Function Design in WSN’) Table 2 shows the keywords used for the search on the various databases Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 727 Samuel Asare et al 723-744 B. Inclusion and Exclusion Criteria To filter search results, for relevant studies, we established the following inclusion and exclusion criteria: 1) Inclusion  Studies that focused on energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs.  Peer-reviewed journal articles, conference papers  Studies that provide empirical results or evaluations using datasets relevant to energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs  Publications written in English  Propose novel methods and use of simulations 2) Exclusion  Studies not related to intrusion detection algorithms designed to energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs  Publication that provide theoretical models without empirical validation  Non-peer reviewed sources such as thesis, white papers, and editorials. C. Study Selection Process Figure 3 shows the study selection process which followed the PRISMA framework using these three (3) phases:  Initial Screening: All reviewed articles were screened by titles and abstract to exclude irrelevant studies and choose those meeting the inclusion criteria for full-text review  Full-Text Review: Full text of selected articles were reviewed to determine their relevance and quality. Excluded articles that did not provide detailed information on energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs  Data Extraction and Coding: A standardized form was used to extract data from the final set of articles including energy consumption and network longevity enhancement through computational intelligence within the LEACH protocol in WSNs. Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 728 Samuel Asare et al 723-744 Data Sources and Search Strategy Figure 1: PRISMA flow diagram summarizing the study selection process Figure 2: Distribution of publications included in the review based on year Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 729 Samuel Asare et al 723-744 Figure 1 shows a PRISMA flow diagram summarizing the study selection process and figure 2 shows a distribution of publications included in the review based on year. III. LITERATURE REVIEW A. Challenges in the Original LEACH Protocol The Original LEACH has been a major progression for wireless sensor networks due to its energy efficient clustering methodology [19].. Nevertheless, there are some limitations which affect its efficiency and performance. There are several constraints of the original LEACH protocol, and one of them is energy [20]. Although it aims to reduce energy usage by rotating cluster heads, the overhead associated with this process can lead to increased energy consumption during the setup phase. Frequent re-clustering causes the batteries of the nodes to drain rapidly, and reduces their lifespan of operation and thereby affecting the network’s lifetime [21]. LEACH suffers from scalability of the network as it grows with the number of node, and hence inefficient data transfer and bottleneck may occur with decrease in performance under heavy load [22]. In response to these problems, several improvements and new protocols are proposed for minimizing the energy consumption with more scalability structure and efficient communication among nodes in WSNs. However, fuzzy logic, genetic algorithm, and AI driven technique were proposed in previous studies to increase the lifespan of the batteries and improve network performance [23]. Although the existing techniques enhance the lifespan of the batteries in WSNs, the need for further enhancement is needed to maximize the lifespan of batteries in WSNs [24]. Moreover, these advancements not only allow efficiency to be enhanced, but make scalable solutions capable of facing the most pressing challenges in urban planning, resource allocation and public health interventions [25]. Furthermore, these scalable solutions will enable organizations to extract data driven insights, drive intelligent decision making and promote responsible practices throughout many sectors [26]. B. Computational Intelligence Techniques An automated system proposed by Prithi and Sumathi [27] enhances energy consumption, network lifetime, throughput, end-to-end delay, routing, and intrusion detection in WSNs. The method aimed at creating a system that can learn, monitor, and control the changing behavior of the network environment while optimizing routing using computational intelligence techniques [28]. The proposed method was evaluated based on multiple performance metrics such as network lifetime, energy efficiency, throughput, end-to-end delay, accuracy, detection rate, computation time, and recall rate [29]. The technique can be applied in different areas that already use WSNs, including health for patient monitoring, environmental monitoring and home automation, and vehicular network security [30]. However, a novel approach which integrates Distributed Artificial Intelligence (DAI) and Adaptive Fish Swarm Optimization (AFSO) for resource allocation in WSNs with the aim of reducing energy consumption was introduced by Reddy et al. [31]. This approach reduces the challenges of efficient resource utilization in WSNs, which are constrained by computational Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 730 Samuel Asare et al 723-744 latency, communication range, bandwidth, storage, and battery power was improved [32]. The DAI focuses on inter-cluster power allocation by considering Quality of Service (QoS) and energy consumption. The proposed method improves network lifetime and throughput while minimizing computational delay and overhead in WSNs. A reduction in energy consumption by 7.86% which outperforms the existing methods was achieved by the proposed method. However, the integration of DAI and AFSO lead to additional complexity in terms of implementation and computational requirements. C. Enhancements to LEACH Protocol Buvana et al. [33] presented an enhanced optimized LEACH protocol to enhance energy utilization of the network and prolong overall network lifetime, and the primary contributions include several modifications and additions to original standard LEACH protocol. The paper introduces a new method for calculating the 'fitting factor' of the next hop in multi-hop communication among clusters [34]. This formula takes into account factors such as angle, energy, and distance, aiming to optimize inter-cluster data transmission [35]. Furthermore, a technique which was included in the proposed approach divided the network area into adjusted cluster size in changing environments [36]. The technique takes into account the distance between the nodes and residual energy of a node and also embedded a sleep mechanism to intra cluster communication in WSNs [37]. The primary goal of the method used by Khan et al. [38] is to enhance the network lifetime and reduce energy consumption in WSNs. This is achieved by optimizing the cluster head (CH) selection process using a micro genetic algorithm (µGA) integrated with the LEACH protocol. The µGA is a variant of the genetic algorithm that operates with a smaller population size, which makes it suitable for real-time applications due to its faster convergence. The structure of the proposed µGA-LEACH protocol is based on improving the performance of conventional hierarchical routing protocols, such as LEACH, LEACH-C, LEACH GA and GADA LEACH. The strategy is designed to enhance the network energy efficiency and prolong its lifetime through an efficient CH selection. This is important since the CHs are in charge of coordinating internal communication within clusters and sending aggregated data to the base station. Shen [39] proposed a fuzzy logic algorithm that enhances a cluster head selection in WSNs. This approach aims to enhance the classic LEACH protocol by integrating the fuzzy logic on node characteristics such as remaining energy, distance with base station and connectivity. The algorithm uses a fuzzy logic system to evaluate multiple parameters and this approach helps in selecting cluster heads based on a combination of factors rather than a single factor. The enhanced algorithm performed better than the traditional LEACH protocol in network lifetime, energy consumption and overall stability of the network as confirmed by extensive simulation and statistics analysis [40]. The algorithm enhances energy consumption by taking residual energy into account, but is more complex to implement [41]. The enhanced fuzzy logic based method optimally combines the multiple criteria of energy efficiency and network lifetime along with stability without complex implementation. Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 731 Samuel Asare et al 723-744 Jukuntla and Dondeti [42] discussed an enhancement to the LEACH protocol, a method for optimizing energy consumption in WSNs. The work proposed uses a fuzzy controller in the LEACH protocol so that the energy consumption and selection of nodes are improved in making decisions, which contribute to the enhancement of network lifetime [43]. One of the major contributions of this approach is the concept of Rendezvous Nodes (RNs). These nodes serve as relays that allow data to be sent directly from the sink to specific data collecting nodes through a Base Station (BS). This method aims to save energy which is one of the crucial issues in WSNs [44]. Contrary to the original LEACH protocol, the proposed protocol explicitly takes longevity into account as a constraint. Furthermore, the RNs and fuzzy controller are incorporated to enhance the performance of LEACH protocol greatly [45]. The technique provided an efficient power utilization and prolong network lifespan by using less energy to acquire data, which is desirable for a number of applications including environmental monitoring and industrial automation. Sivaraman [46] focused on increasing the network lifetime and reducing energy utilization in WSNs using Advanced LEACH (A-LEACH) protocol. The approach includes data aggregation, a traditional method to limit the redundancy of the individual node [47]. The A-LEACH protocol is used to perform the routing to assists WSN nodes in finding the best path from source to destination. The approach improves the energy consumption problem of WSN, which is an important challenge in the network lifetime [48]. The SRN-LEACH protocol used by Sanapala and Duggirala [49] aimed at enhancing energy efficiency in wireless sensor networks (WSNs) by stabilizing the random number generation used in cluster head (CH) selection. The method introduces a stable random number-based approach to improve the efficiency of CH selection. The average node energy (ANE) of the network is multiplied by a random number that is dependent on the nodes' energy levels. This approach helps in stabilizing the CH selection process, which is a critical factor in reducing energy dissipation [50]. However, a minimum average delay of 0.136 ms, a packet delivery ratio (PDR) of 0.95%, a throughput (TP) rate of 148 kbps, and a network lifetime (NLT) of 95% were achieved by the SRN-LEACH protocol. The approach proposed by Ampratwum and Nayak [51] was to prolong the network lifetime of WSNs. A nested genetic algorithm (GA) scheme is proposed in the paper as an optimization technique based on natural selection. This approach can optimize the sink node location that is important for network data collection and energy consumption [52]. It chooses cluster heads that process data information of sensor nodes and forward it to the sink node. This selection is important in order to make the energy load uniform through the nodes [53]. The proposed method was compared with four approaches in the field and demonstrated an average improvement of 15% in network performance under normal conditions and a 30% improvement in more changing environments. This indicates the robustness and effectiveness of the method in enhancing network lifetime [54]. Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 738 Samuel Asare et al 723-744 [49] The SRN-LEACH protocol effectively addresses the challenge of energy efficiency in WSNs It does not explicitly state any limitations or drawbacks [27] A comprehensive approach to enhancing the performance and security of WSNs through the use of an automaton system and computational intelligence techniques It does not detail any specific constraints, challenges, or areas where the proposed method might fall short or require further development. [41] Integration of fuzzy logic into the LEACH protocol represents a promising method for enhancing the energy efficiency of WSNs. By improving cluster head selection, the network can potentially achieve a longer lifespan The paper does not detail how these parameters were determined or if their optimal configuration is universally applicable across changing WSN environments and application scenarios [38] A comprehensive approach to addressing energy efficiency in IoT networks by integrating AI and digital twin simulations, thereby enhancing the traditional LEACH protocol Lack of detailed experimental conditions, potential computational overhead, and a narrow focus on energy efficiency over other performance metrics. [31] A robust framework for resource allocation in WSNs, leveraging the strengths of DAI and AFSO to enhance network performance and efficiency Considerations regarding complexity and scalability should be addressed in future research [38] The µGA-LEACH protocol enhances the efficiency of CH selection and contributes to the overall sustainability of the network by reducing energy wastage and prolonging the operational period of the sensor nodes Complexity of implementing genetic algorithms in resource-constrained WSN nodes, and the potential overhead introduced by the genetic algorithm [39] The improved fuzzy logic-based method offers a balanced approach by enhancing energy efficiency and network stability Complexity and scalability should be addressed in future research Table 3 shows a summary of literature review, and table 4 limitations and contributions of literature review. V. CHALLENGES AND OPEN DIRECTIONS Although there are remarkable improvements in balancing the energy consumption and network lifespan of LEACH protocol in WSNs due to the integration of computational intelligence, new challenges have emerged such as computational overhead, algorithm complexity and parameter validation in automatic WSN. AI driven methods, rely on large scale high quality datasets and also consumes more energy. While fuzzy logic enhances the network lifetime and deals with uncertain data, it may cause extra computation load on sensor. Although genetic algorithms can achieve near-optimal CHs selection in addition to adapting to dynamic WSNs, the iterative computations can be a high drain on the sensor nodes and slow down the convergence of optimal solutions. Journal of Computational Analysis and Applications VOL. 34, NO. 11, 2025 739 Samuel Asare et al 723-744 Implementation of complex techniques such as Distributed Artificial Intelligence (DAI) or Adaptive Fish Swarm Optimization (AFSO) can increase the complexity and computational demands. Also, GA with a mobile sink might be robust but may introduce computational complexity and requires experimental verification in real world. Despite improvements, the issue of load balancing among nodes, which is crucial to prolonging the network lifespan, still remains. Some complex models and mobile sinks, can however to be hampered in terms of practical applicability and deployment by the added complexity as well as the testing required after validating solely via simulations. Future research should focus on a hybrid technique that utilizes energy-efficient protocols to better extend the network lifetime and guarantee robust performance in a hybrid fashion. VI. CONCLUSION We conducted a systematic review of improvements to the LEACH protocol, especially using computational intelligence to solve energy consumption problems in WSNs. The main objective was to balance the energy consumption and network lifetime effectively using computational intelligence. The review emphasized the significance of energy efficiency for WSNs on account of resource constrained sensor nodes, especially in changing environments. It also investigated several computational intelligence approaches such as fuzzy logic, genetic algorithms and AI based techniques to improve the energy consumption of LEACH protocol. In summary, although the traditional LEACH protocol had made some effort in balancing the energy consumption using energy-efficient clustering, further development is still needed because balancing the energy consumption in WSNs cannot be handled by a single computational intelligence technique. 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