Low Energy Adaptive Clustering Hierarchy Algorithm Research Articles

Wireless optimization for sensor networks using IoT-based clustering and routing algorithms.

Wireless sensor networks (WSN) are among the most prominent current technologies. Its popularity has skyrocketed because of its capacity to operate in difficult situations. The WSN market encompasses various industries, including building automation, security networks, healthcare systems, logistics, and military operations. Therefore, increasing the energy efficiency of these networks is of utmost importance. Hierarchical topology, which typically uses a clustering methodology, is one of the most well-known methods for WSN energy optimization. To achieve energy efficiency in WSN, hierarchical topology low-energy adaptive clustering hierarchy (LEACH) was first introduced, and this served as the foundation. However, conventional LEACH has several limitations, which have led to extensive research into improving LEACH's efficacy in its current form. The use of particular algorithms and strategies to enhance the functionality of the conventional LEACH protocol forms the basis of ongoing efforts. Utilizing this enhanced LEACH, performance in terms of throughput and network life may be enhanced by concentrating on elements such as cluster head formation and transmission energy consumption. The enhanced LEACH algorithm demonstrates significant improvements in both throughput and network lifetime compared with conventional LEACH. Through rigorous experimentation, it was found that the enhanced algorithm increases the throughput by 25% on average, which is attributed to its dynamic clustering and optimized routing strategies. Furthermore, the network lifetime is extended by approximately 30%, primarily because of enhanced energy efficiency through adaptive clustering and transmission power control.

Ad hoc wireless network implementing BEE-LEACH

Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.

A NEW APPROACH TO WIRELESS CHARGING AND ON-DEMAND DATA GATHERING (SSWCODDG) FOR RWSN

These days, WSNs (wireless sensor networks) are all about collecting data and reducing energy use. Mobile sinks and chargers can visit Cluster Heads (CHs) to gather data and recharge their batteries, respectively. A mobile VAN (MV) combines a mobile charger and sinks to improve the energy efficiency of a WSN. Determining the CHs’ guest instructions for wireless data collecting and charging is difficult. In response to these problems, the authors of this study present SSWCODDG (Santosh Soni Wireless Charging and on Demand Data Gathering), a new approach to wirelessly charging and data collection for rechargeable wireless sensor networks. Utilizing mobile VAN, the proposed algorithm gathers data from CHs and charges them according to their choices and needs. Here, Mobile VAN maintains a routing table and, in response to requests, visits CHs. To divide the network into clusters, the proposed method employs the LEACH clustering algorithm. One full cycle is executed by the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm. To back up its claims, this study compares the recently created SSWCODDG algorithm to the SPSS and M2C algorithms that came before it. In addition, SSWCODDG outperforms SPSS (Single path scheduling scheme) and M2C (Mobile to Cluster) for a range of sensor node densities by 38.62 and 25.78 percentage points, respectively.

Machine learning‐based data fusion method for wireless sensor networks

AbstractFor wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks

This research has a research object, namely the optimization of the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm in the context of wireless sensor networks. The problem in this research is the imbalance in energy consumption across clusters, which has an impact on battery life and affects network performance. Other problems include selecting a cluster head that is not focused so that it is difficult to balance network performance as well as computational limitations that require optimization. The results obtained from this research are in the form of optimizing the leaching algorithm by modifying the clustering-based leaching algorithm that will be used in wireless sensor networks. In carrying out modifications, this research uses several stages in the process of selecting sensor nodes that will become members who function as cluster heads in a cluster that will be used in a wireless sensor network. In the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm the cluster head will be selected based on the modified probability value. Modifying the algorithm by considering two factors, namely distance and remaining energy used in the Cluster Head selection process on the network and increasing network usage time must be based on the energy consumption used and then compared with the remaining energy. When modifying the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm, it is necessary to pay attention to the distance factor between the nodes on a sensor and the selected cluster so that it can result in increased network performance. Network lifetime is indicated by the average death time of the first Node in the network. This research is novel in producing a modified leaching algorithm by improving network performance and extending battery life so that it can be used for wireless sensor networks in the context of natural disaster mitigation

Smart Grid Simulation with LEACH (Low-Energy Adaptive Clustering Hierarchy) Clustering

Power industry applications for smart grid are quite promising. In recent years, network life extension has attracted a lot of study attention. By examining the business needs of the Smart grid, this study enhances the wireless sensor network LEACH algorithm. To ensure the rationality of the cluster head selection, the remaining energy and node density are first taken into consideration. The cluster radius of various sizes is then constructed while clustering using a non-uniform clustering process. To achieve the goal of balancing the energy load, data is transmitted to the base station using the multi-hop mode, and the distance between the cluster head nodes and the remaining energy are utilised as the selection criteria for the next-hop relay node. In order to provide the computational intelligence required to create the Smart Grid, this paper discusses the future of the electric energy system, covering every aspect from power generation to substations, distribution, and customer feedback loops.

Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Wireless Sensor Network (WSN) is a group of small, intelligent sensors with limited resources. WSN has limited energy restrictions, so, the network lifetime is the major challenge that directly affect the efficiency of the network. This work presents an energy-saving clustering hierarchical algorithm for WSNs; it is an improvement of Low-Energy adaptive Clustering Hierarchy (LEACH) algorithm. The aim of this algorithm is to minimize power consumption by the appropriate election of new cluster heads in every data transfer round and avoid network collisions. This goal achieved by using an efficient function to select the best cluster heads nodes in each round, which takes into account the current energy in the sensors. The proposed algorithm improves the cluster formation process by relying on the shorter distance to the base station. The Time Division Multiple Access (TDMA) mechanism also utilized to schedule the transmission of data packets to cluster heads nodes and to avoid data packet collisions at the base station. Experiments conducted in MATLAB R (2020a) software showed that the suggested algorithm extended the network lifetime by 14.5%, and improved the network throughput by 16.8% compared to the LEACH algorithm. That means, the proposed energy-saving clustering hierarchy algorithm has improved the performance of the LEACH algorithm in term of enhancing network lifetime and increasing network throughput.

Improving WSNs execution using energy-efficient clustering algorithms with consumed energy and lifetime maximization

<span lang="EN-US">Wireless sensor networks (WSNs) has a major designing feature representing by energy. Specifically, the sensor nodes have limited battery energy and are deployed remote from base station (BS); therefore, the actual enhancement dealing with energy turns into the Clustering routing protocols fundamentals which concerned in network lifetime improvement. Though, unexpected and energy insensible of the clusters head (CH) selection is not the best of WSN for greatly lowering lifetime network. A presentation article of an WSNs incoming routing approach using a mix of the fuzzy approach besides hybrid energy-efficient distributing (HEED) algorithm for increasing the lifetime and node’s energy. The FLH-P proposal algorithm is split into two parts. The stable election protocol HEED approach is used to arrange WSNs into clusters. Then, using a combination of fuzzy inference and the low energy adaptive clustering hierarchy (LEACH) algorithm, metrics like residual energy, minimal hops, with node traffic counts are taken into account. A comparison of FLH-P proposal algorithm with LEACH algorithm, fuzzy approach, and HEED utilizing identical guiding standards was used for demonstrating the performance of the suggested technique from where corresponding consumed energy as well as lifetime maximization. The suggested routing strategy considerably increases the network lifetime and transmitted packet throughput, according to simulation findings.</span>

Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning.

The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k-means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k-means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k-means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer.

Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach

PurposeFlying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system.Design/methodology/approachThis paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses.FindingsThe proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time.Originality/valueThis study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency.

Improved uplink throughput and energy efficiency of LoRaWAN using 2-hop LEACH protocol

The low power wide area networks (LPWAN) is the new connectivity technology that is geared towards energy constrained internet of things (IoT) devices, is starting to become one of the drivers of the re-accelerating IoT market and has one goal: ensure the wide range distance while reducing the battery energy consumption. We focus in this paper on the evaluation of the uplink throughput of the long-range wide area networks (LoRaWAN) then we attempt optimize the throughput and power dissipation using low energy adaptive clustering hierarchy (LEACH) protocol. Therefore, we exploit a novel module developed in NS-3 simulator for obtaining the first measurements scenario, then the LEACH algorithm for the second optimization case. As result, the simulation analysis will help us to add a new LoRaWAN routing protocol feature.

Design and Implementation of LEACH Protocol for Wireless Sensor Network to Reduce for Network Area Energy Improvement and Security Using MATLAB

In this Paper Wireless Sensor Networks (WSNs) have exploded in popularity in recent years as one of the fastest-growing developing technologies for delivering data over the internet. WSN is rapidly expanding its branches in practically every field of science and technology today. The WSN is made up of several tiny nodes that handle sensing, data collecting, aggregation, compression, and transmission. Because the sensor nodes are so small, the little battery only has a limited amount of power. As a result, the key issue for WSN is to effectively employ this insufficient battery capacity to extend the sensor networks' lifetime while reducing energy usage. However, when it comes to numbers, However, a number of advanced clustered routing protocols have already been used in WSN to reduce the amount of energy consumed. The study's main goal is to improve the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol by implementing a new clustering routing topology. The process for selecting cluster heads in our proposed model is identical to that used in the standard Leach protocol. We have, however, partitioned the network's whole area into many rectangle dispersed sections. The LEACH algorithm was used in each area.

Organized Selection Cluster Head on Fuzzy Low-Energy Adaptive Clustering Hierarchy Protocol in Three-Dimensional Wireless Sensor Networks

Background: In this paper, A definition of proposed enhancement approach to reduce the energy consumption, extend the network lifetime and improve the performance of protocol Fuzzy Low-Energy Adaptive Clustering Hierarchy Algorithm (Fuzzy LEACH). Objective: This improvement will augment the energy balancing in clusters among all sensor nodes and minimize the energy dissipation during network communications. Methods: The proposed method is based on a cluster head selection method. Moreover, an enhanced organization of this selection has been implemented. Results: Mathematical analysis and MATLAB 2013a simulation results show the effectiveness of the proposed approach. The new approach reduce the energy consumption of Wireless Sensor Network (WSN) about 0.99% to 5.64%, prolongs the network life cycle by 42% and increases the number of packets sent by 86% to 732%. Conclusion: The developed approach indicates a progress in terms of network lifetime, energy consumption and number of packets transferred to BS compared to Low-Energy Adaptive Clustering Hierarchy Algorithm (LEACH) and other related extended spaces protocols.

WLEACH: Real-Time Meteorological Data Based Wind LEACH

Introduction:Nowadays, Wireless Sensor Network (WSN) plays an important role in various fields. The limited power capability of the sensor nodes in the WSN brings constraints on the performance of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) is a promising protocol for WSN that suffers from higher energy consumption.Objective:The primary objective of this study is to give an alternate harvesting resource power to sensor nodes in the LEACH algorithm which can be equally capable of providing the same or sometimes better results.Methods:This study is based on real-time meteorological data. A real-time wind speed data is taken for the starting of a day to the end of the day on an hourly basis from the weather forecast. Now to convert this rotational energy into electrical energy, we used two types of wind turbines. For the proposed methodology, a micro wind turbine generator and 300watt wind turbine are used. Then this converted electrical energy is given to sensor nodes. For the clustering, the wind power operated nodes are given maximum preference to be elected as the cluster heads based on realtime wind meteorological data. We consider 10 wind-powered sensor nodes. As we increase the number of wind-powered sensor nodes in the network, the performance is increased in terms of a lifetime but then increases the complexity of the network. These wind-powered nodes remain alive in the network. Since the deployment of the sensor nodes is random, each simulation runs for 5 times and the average of first node dead, half node dead and last node dead is considered.Results:The experimental results for the micro wind turbine generator are compared based on with and without the MPPT controller. MPPT controller gives the maximum power by using the tip speed ratio control, power signal feedback control, and hill climb search control method. Therefore, the network lifetime should be higher for the MPPT based wind generator. Network lifetime and Energy consumption are compared for a micro wind turbine generator and 300watt wind turbine. Finally, the performance of the proposed system is compared with the modified solar LEACH implemented using real-time meteorological data.Conclusion:This paper has investigated the wind-based LEACH which uses the real-time meteorological data for the selection of the cluster head. Two types of wind generators are considered for the implementation and it is found that the performance of the commercial 300W wind turbine and the micro wind turbine with and without MPPT is almost similar since the data from both wind turbines are given on hourly basis. The performance of the wLEACH is compared with the sLEACH which shows that the network lifespan of the wLEACH is also nearly the same compared to the sLEACH. However, it was found that wind power generation is cheaper and efficient than solar power generation. Therefore, it is inferred that this proposed wLEACH provides a costefficient solution.

Energy optimized hybrid PSO and wolf search based LEACH

In wireless sensor networks (WSN), computational challenges exist in determining a global energy optimized communication routing in large spaced network. WSN challenges can be handled by applying heuristic bio-inspired computational intelligence optimization methods. In WSN, Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm is most frequently used hierarchical routing algorithm in spite of certain limitations. The proposed work is addressed in this direction, to improve issues of LEACH, such as identification of reliable CH, selection of energy efficient inter and intra route communication using relay nodes, so as to extend the network lifespan. The proposed work applies Particle Swarm Optimization (PSO) and Wolf Search optimization methods to improve the performance of LEACH algorithm. PSO is castoff for cluster formation and Wolf search for identification of two relay nodes: intra and inter relay node. The Spyder-py3 tool is used to simulate the proposed algorithm: LEACH PSO Wolf search based Optimization (LEACH-PWO). The proposed work compared with original LEACH, power-efficient gathering in sensor information systems, Ant Cuckoo optimized using energy-efficient data aggregation, and Genetic Algorithm data Aggregation LEACH protocols indicate prolonged lifetime of network and increased throughput.

Performance analysis of two layer leach algorithm based on area partition (tl-leach-p) for wsn

Currently, much research has been done to develop routing algorithms to improve the lifetime of sensors from Wireless Sensor Network (WSN). Routing protocols based on the network structure can be classified into two categories: flat protocols and hierarchical protocols. One of the most popular hierarchical protocols is the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. However, LEACH routing has a high packet loss in a busy network. This research propose Two Layer LEACH based on area Partition (TL-LEACH-P) routing algorithm based on the LEACH routing algorithm where the cave layer is done to prioritize the node that can become Cluster Head to decrease energy consumption. LEACH and TL-LEACH-P algorithms are simulated using MATLAB. The results show that the TL-LEACH-P algorithm has a better performance of 60% reaching LEACH in terms of network lifetime. The TL-LEACH-P algorithm has a 50% increase in the round where 50% of the nodes are dead from LEACH. Best TL-LEACH-P algorithm performance is when the k cluster value = 5. And best performance for LEACH algorithm is when the k cluster value = 15.

Enhancement of LEACH Based on K-means Algorithm and Stochastic Optimization

In Wireless Sensor Networks (WSNs), maximizing the life of the Sensor Nodes (SNs), and energy conservation measures are essential to enhance the performance of the WSNs. A Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocol has been proposed specifically for WSNs to increase the network lifetime. However, in LEACH protocol the criteria for clustering and selecting Cluster Heads (CHs) nodes were not mentioned. Accordingly, researchers have been focusing on ways to strengthen the LEACH algorithm to make it more efficient. In this paper, we propose to improve the LEACH protocol by combining the use of K-means algorithm for clustering and Slime Mould Algorithm (SMA), a new stochastic optimization to select nodes as CHs. The proposed routing algorithm, called SMA-LEACH, is superior to other algorithms, namely PSO-LEACH, BA-LEACH, which using Particle Swarm Optimization (PSO), Bat Algorithm (BA) to improve LEACH, respectively. Simulation analysis shows that the SMA-LEACH obviously reduces network energy consumption and extends the lifetime of WSNs.

Analysis of Binary and Discrete Grey Wolf Optimization Algorithms Applied for Enhancing Performance of Energy Efficient Low Energy Adaptive Clustering Hierarchy

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

An Energy Efficient Enhanced Dual-Fuzzy Logic Routing Protocol for Monitoring Activities of the Elderly Using Body Sensor Networks

Wireless body area networks (WBANs) are an important application in wireless sensor networks (WSNs). Specifically, in healthcare monitoring systems, it is important to screen the patient’s biometric signals. For example, the elderlies’ vital signs, such as ECG (Electrocardiogram), blood pressure, heart rate, and blood glucose, can be used as measures of their well-being and are all critically important for remote elderly care in tracking their physical and cognitive capabilities. Therefore, WBANs require higher energy efficiency and data transmission. This paper proposes a cluster-based routing protocol which is suitable for WBANs while analyzing energy efficiency issue in data transmission. Considering the importance of sensor nodes in a specific environment for improving the network’s lifetime, the protocol based on the LEACH (low energy adaptive clustering hierarchy) algorithm is proposed. Due to its avoidance of long-distance transmission, the clustering technique is an efficient algorithm for prolonging the lifetimes of sensor networks. Therefore, this paper suggests an enhanced LEACH-dual fuzzy logic (ELEACH-DFL) protocol based-on clustering for CH (cluster head) selection and cluster configuration in wireless sensor networks. The simulation and analysis results address that the enhanced algorithm reduces the energy consumption effectively and extends the lifespan of the entire network. For wired sensors, attaching sensors to the user may cause problems and inconvenience of mobility. This leads to the use of wireless sensors to proceed with body sensors, which should consider the problem of battery efficiency, which concerns the configuration of wireless sensors. The LEACH protocol is energy efficient until the first node dead is generated. However, there is a sharp drop in energy efficiency after that. The ELEACH-DFL protocol has the advantage of maintaining energy efficiency even after the first node dead is generated, with the utmost consideration being given to stability in consideration of cluster selection and cluster head selection. In a field of 50 × 50, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 32%. In addition, in a field of 50 × 150, the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately 159%.