Cluster Head Selection In Wireless Sensor Networks Research Articles

An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

SummaryIn this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN).

Cluster Heat Selection Optimization in Wsn Via Genetic Based Evolutionary Algorithm and Secure Data Transmission Using Paillier Homomorphic Cryptosystem

Introduction: Wireless Sensor Networks (WSNs) consist of sensor nodes requiring energy-saving measures to extend their lifespan. Traditional solutions often lead to premature node failure due to non-adaptive network setups. Differential Evolution (DE) and Genetic Algorithms (GA) are two key evolutionary algorithms used for optimizing cluster head (CH) selection in WSNs to enhance energy efficiency and prolong network lifetime.Methods: This study compares DE and GA for CH selection optimization, focusing on energy efficiency and network lifespan. It also introduces an improved decryption method for the Paillier homomorphic encryption system to reduce decryption time and computational cost.Results: Experiments show GA outperforms DE in the number of rounds for the first node to die (FND) and achieves a longer network lifespan, despite fewer rounds for the last node to die (LND). GA has slower fitness convergence but higher population fitness values and significantly faster decoding speeds.Conclusion: GA is more effective than DE for CH selection in WSNs, leading to an extended network lifespan and better energy efficiency. Despite slower fitness convergence, GA's higher fitness values and improved decoding speeds make it a superior choice. The enhancements to the Paillier encryption system further increase its efficiency, offering a robust solution for secure and efficient WSN operation

Dynamic Cluster Head Selection in WSN

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge to the longevity and performance of WSNs. To address this challenge, this paper presents an Optimized Energy Efficient Protocol in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics (OEE-WCRD). This research paper presents a novel approach to cluster head selection in WSNs by harnessing a combination of residual energy and distance metrics. The proposed method aims to significantly enhance the energy efficiency of WSNs by prioritizing nodes with ample residual energy and proximity to their neighbors as cluster heads. Through extensive simulations and evaluations, we demonstrate the effectiveness of this approach in prolonging network lifetime, optimizing data aggregation, and ultimately advancing the energy efficiency of WSNs, making it a valuable contribution to the field of WSNs protocols.

A Novel Hybridization of ML Algorithms for Cluster Head Selection in WSN

Generally, Wireless Sensor Networks (WSNs) are infrastructure-less networks with thousands of sensor nodes that sense or monitor the physical and environmental changes and forward the collected data to a central node. Besides, WSN has become the most efficient technology for handling Internet of Things (IoT) devices. Still, challenges such as node failures, high traffic among the nodes, link failures, etc., limit the performance of WSNs. To solve the challenges in WSN, this paper aims to develop a novel non-uniform clustering model, where the Cluster Heads (CHs) are selected based on the candidate CH selection strategy that transfers the data. Moreover, unbalanced energy utilization and data redundancy are eliminated via multi-hop communication. For attaining the non-uniform clustering model, the routing among the data packets is done by the efficiency of the hybridization of the Machin Learning (ML) algorithms viz Genetic Algorithm (GA) and Lion Algorithm (LA) with the consideration of energy, cost, time, network lifetime, and data accuracy. Finally, the performance of the proposed model is verified and validated through a comparative study with the existing models.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

An Energy Efficient Cluster Head Selection in WSN Based on Enhanced Chicken Swarm Optimization

Clustering of data are ab effective energy saving method for wireless sensor networks (WSN). However, they incur additional costs in data collections from sensor nodes and sending them to base station (BS) after aggregations. Cluster heads (CHs) in hierarchical cluster-based WSNs use more energy. Therefore, choosing a suitable CHs is essential to conserve energy for sensor nodes and prolong the life of the WSN. Provide energy-saving cluster head selection techniques in this work. where enhanced fuzzy means clustering is used to perform the first cluster construction. Enhanced Chick Swarm optimization (ECSO) selects CHs which is evaluated strictly for varying WSN scenarios and counts of CHs and nodes for values of remaining energies of sensor nodes, sink distances, and distances within clusters. To demonstrate that the proposed technique is superior, the results are compared with those of several other currently used algorithms.

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc. However, it remains challenging to make optimal clustering process of WSN that makes the routing process more effective. Thereby, this work proposes a new Namib Merged Jelly Search Optimization Algorithm (NMJSOA) approach for the optimal CH selection in WSN by the different constraints. Initially, the sensor nodes are grouped together to form the clusters by the k-means clustering techniques. Subsequently, allocating the CH for each cluster by computing the weight function for each cluster depends on the conditions such as energy, delay, distance and security. According to the NMJSOA method, the Namib Beetle Optimization (NBO) searching position and the Jellyfish swam old position are added together to get the best optimum position. Finally, the performance of the suggested model is investigated over the conventional methods in terms of different performance measures.

A novel self‐adaptive hybrid slime mould naked mole‐rat algorithm for numerical optimization and energy‐efficient wireless sensor network

SummaryA wireless sensor network (WSN) is made up of widely spaced nodes inside the sensor field. Clustering is an effective data collection approach that lowers energy consumption by organizing nodes into groups. So, choosing the right cluster head (CH) and employing an effective routing protocol is required to prolong the lifetime of WSNs. This paper develops a novel self‐adaptive hybrid slime mould naked mole‐rat algorithm (SM‐NMRA) based on the hybridization of the slime mould algorithm (SMA) and naked mole‐rat algorithm (NMRA) to solve the above‐said problem of WSN. The new approach integrates SMA's wrap food abilities with NMRA to improve the working capabilities of the original NMRA. A new stagnation phase inspired by the grey wolf optimizer and cuckoo search algorithms has been integrated to handle the local optima stagnation problem. In addition, self‐adaptivity has been added for SM‐NMRA's parameters. Here, the performance of SM‐NMRA is evaluated for CEC 2005 and CEC 2014 numerical test suites. The statistical outcomes along with Freidman's test, Wilcoxon's rank‐sum test, and convergence profiles validate the superior performance of SM‐NMRA. The proposed clustering protocol for efficient CH selection in WSN using SM‐NMRA outperforms other state‐of‐the‐art techniques with improved network lifespan and reduced energy consumption.

A Novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) for Energy-Based Cluster-Head Selection in WSNs

AI is getting increasingly complex as a result of its widespread deployment, making energy efficiency in Wireless Sensor Network (WSN)-based Internet of Things (IoT) systems a highly difficult problem to solve. In energy-constrained networks, cluster-based hierarchical routing protocols are a very efficient technique for transferring data between nodes. In this paper, a novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) is proposed to improve the lifetime of the network and less energy consumption. The proposed SMOFCM technique makes use of the Fuzzy C-means clustering framework to build up the cluster formation, and the Spider Monkey Optimization technique to select the Cluster Head (CH). MATLAB was used to model the suggested SMOFCM. The suggested framework's network lifetime, number of alive nodes (NAN), energy consumption, throughput, and residual energy are compared to those of more established frameworks like LEACH, K-MEANS, DRESEP, and SMOTECP. SMOFCM technique improves the network lifetime by 11.95%, 7.59%, 4.97% and 3.83% better than LEACH, K-MEANS, DRESEP, and SMOTECP. According to experimental findings, the proposed SMOFCM technique outperforms the existing model.

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

A Review on Energy-Efficient Mechanisms for Cluster-Head Selection in WSNs for IoT Application

Internet of things (IoT) incorporates sensing, connectivity, networking, or cloud computing technology into a wide variety of control fields. The wireless sensor networks (WSNs) are better placed for IoT applications. Developing an energy-efficient (EE) cluster head (CH) system is of great importance to enhance the network life of WSNs. WSNs are now becoming an emerging research field through technological growth. Sensors are integrated to gather the data from which this is known in a collective of clusters. CH maintains a process of transmission that requires high energy. This review paper discusses various techniques, advantages, disadvantages, objectives, and different methods for EE clustering techniques in WSN on IoT-related research. This study is beneficial to identify the problems associated with EE-CHS in WSN and how to resolve it.

Modified threshold for cluster head selection in WSN using first and second order statistics

Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy-constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first-order and second-order statistical parameters, such as mean average low-energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%.

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

SummaryA wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.

Performance of Quasi Oppositional Butterfly Optimization Algorithm for Cluster Head Selection in WSNs

Abstract Wireless sensor networks (WSNs) technology is attracting the most of researchers and industry due to its application in wide contexts that include the military, surveillance, monitoring of habitat, health care, public safety and environmental monitoring. The consumption of energy is a crucial challenge faced in the WSN research. Clustering is an efficient technique for limiting the energy consumption of WSNs. But considering the hierarchical and cluster-based WSNs, the cluster heads utilize a high-level of energy because of data aggregation from various sensor nodes and transmit the same to the sink node. So, proper cluster head (CH) selection is required for sensor nodes’ energy conservation to prolong the network’s lifespan of the WSNs. In this paper, a CH selection protocol is proposed that is based on the quasi oppositional butterfly optimization algorithm (QOBOA). The proposed method is compared with original butterfly optimization along with certain existing algorithms with respect to the networks’ lifetime and efficiency of energy. Simulation results show that the QOBOA based CH selection scheme outperforms the existing ones.

An Efficient Variable Distance Measure K-Means [VDMKM] Algorithm for Cluster Head Selection in WSN

Wireless sensor networks (WSNs) provide an empirical and explorative ways to handle and collection of the data in the centralized way. In this paper an efficient variable distance measure k-means [VDMKM] algorithm for cluster heads (CHs) selection in WSN has been presented and analyzed. This approach is divided into two phases. In the first phase data preprocessing has been performed. In this phase size-based weight and threshold assignment have been done on the scaling factor of 1-10. In the second phase VDMKM approach has been applied. The main benefit of this approach is the capability of smaller distance assignments automatically based on different distance measures. The distance measures used here are Euclidean, Pearson coefficient and Manhattan correlation. The complete cluster correlated delay has been calculated along with the packet delivery time. Our distance measure based automatic distance adjustment approach provides better suited distance-based packet delivery in less time. The results are more prominent in the case of Euclidean and Manhattan.

Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Hybrid model for security‐aware cluster head selection in wireless sensor networks

Wireless sensor network (WSN) is considered as the resource constraint network, in which the entire nodes have limited resources. In WSN, prolonging the lifetime of the network remains as the unsolved point. Accordingly, this study intends to propose a hybrid GGWSO (Grouped Grey Wolf Search Optimisation) algorithm to improve the performance of a cluster head selection in WSN, so that the network's lifetime can be extended. The proposed method concerns the main constraints associated with distance, delay, energy, and security. This study compares the performance of the proposed GGWSO with several traditional algorithms like artificial bee colony (ABC), fractional ABC, group search optimisation and Grey Wolf optimisation-based cluster head selection. During the performance analysis, the various ranges of risk, such as 20, 60, and 100% are added to validate the performance variations, by evaluating the number of alive nodes, and normalised network energy remained in the network. The simulation results have shown that there is a need for a hybrid model for attaining the superior results.

A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN

The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of wireless sensor networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. In many critical applications such as military and monitoring the eco system, disaster management, etc., data routing is very crucial. Multi hop low-energy adaptive clustering hierarchy protocol has been proposed in literature but is proved to be inefficient. Cluster head optimization is a NP hard. This paper deals with selection of optimal path in routing which improves network lifespan, as well as network’s energy efficiency. Various meta-heuristic techniques particularly particle swarm optimization (PSO) has been effectively used but with poor local optima problem. The proposed method is on the basis of PSO as well as Tabu search algorithms. Results show the efficiency of the proposed Tabu PSO by enhancing the number of clusters formed, percentage of nodes alive and shows the reduction of average packet loss rate and average end to end delay.

Analytical network process based optimum cluster head selection in wireless sensor network.

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.