Energy Aware Clustering Research Articles

Energy-aware clustering method for cluster head selection to increasing lifetime in wireless sensor network

Energy-aware clustering method for cluster head selection to increasing lifetime in wireless sensor network

Enhancing VANET communication using squid game optimization based energy aware clustering approach

Enhancing VANET communication using squid game optimization based energy aware clustering approach

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Optimal Deployment of Wireless Sensor Nodes using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)

Abstract: Wireless Sensor Networks (WSNs) are emerging as a significant area of research due to their potential to autonomously monitor physical and environmental conditions. These networks comprise spatially distributed, low-cost sensor nodes with limited transmission range, processing capabilities, storage, and energy resources. The primary function of these networks is to collect data from various nodes and transmit it to a base station for subsequent processing. WSNs present several challenges, including optimal sensor deployment, node localization, base station placement, target node location, energy-aware clustering, and data aggregation. Recently, global researchers have been employing a bio-inspired optimization algorithm, Particle Swarm Optimization (PSO), to enhance the efficiency of WSNs. This report explores the application of the PSO algorithm for optimal sensor deployment in WSNs, contributing to the ongoing efforts to maximize the potential of these networks.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN

WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

SEACDSC: secure and energy-aware clustering based on discrete sand cat swarm optimization for IoT-enabled WSN applications

SEACDSC: secure and energy-aware clustering based on discrete sand cat swarm optimization for IoT-enabled WSN applications

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

The effectiveness of jaya optimization for energy aware cluster based routing in wireless sensor networks

The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%.

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.

Green Anaconda Optimization Based Energy Aware Clustering Protocol for 6G Wireless Communication Systems

Green Anaconda Optimization Based Energy Aware Clustering Protocol for 6G Wireless Communication Systems

Energy Aware Clustering System for Wireless Sensor Networks utilizing Rider Sunflower Optimization Approach

Energy Aware Clustering System for Wireless Sensor Networks utilizing Rider Sunflower Optimization Approach

Intelligent energy aware optimization protocol for vehicular adhoc networks

Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy acts as an essential factor in the design of VANET, energy-aware clustering protocols depending upon metaheuristic optimization algorithms are required to be developed. This study introduces an intelligent energy-aware oppositional chaos game optimization-based clustering (IEAOCGO-C) protocol for VANET. The presented IEAOCGO-C technique aims to select cluster heads (CHs) in the network proficiently. The proposed IEAOCGO-C model constructs clusters based on oppositional-based learning (OBL) with the chaos game optimization (CGO) algorithm to improve efficiency. Besides, it computes a fitness function involving five parameters, namely throughput (THRPT), packet delivery ratio (PDR), network lifetime (NLT), end to end delay (ETED) and energy consumption (ECM). The experimental validation of the proposed model is accomplished, and the outcomes are studied in numerous aspects with existing models under several vehicles and measures. The simulation outcomes reported the enhanced performance of the proposed approach over the recent technologies. As a result, it has resulted in maximal NLT (4480), minimal ECM (65.6), maximal THRPT (81.6), maximal PDR (84.5), and minimal ETED (6.7) as average values over the other methods under all vehicle numbers.

Highly Optimized Energy Saving Protocol for Flying ad-hoc Network

bstract: FANET has opened up a wide arena for the study and implementation of drone and UAV (unmanned aerial vehicle) efficiency in a variety of military and rescue applications. In this paper, we propose a hybrid energy-aware routing protocol. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with other bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization, and grey wolf optimization. we propose a hybrid energy-aware clustering technique using a genetic algorithm for cluster formation and management. The proposed scheme shows better results as compared to other bioinspired clustering algorithms on the basis of evaluation benchmarks such as end-to-end latency, packet delivery ratio, energy consumption, time complexity, and throughput, and as a result, hybrid ACOPSO is used to enhance ACOPSO efficiency. The results indicate that the proposed scheme has improved throughput by 60% and 38% with respect to ant colony optimization and, grey wolf optimization, respectively. In terms of average cluster building time while average energy consumption has improved by 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively. From the comparison graph, it is concluded that our proposed method has achievable throughput which is significantly increasing compared to other methods.

Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.

Improved Congestion Control in Wireless Sensor Networks Using Clustering with Metaheuristic Approach

The wireless sensor network (WSN) assists an extensive range of sensor nodes and enables several real-time uses. Congestion on the WSN is based on high pocket traffic and low wireless communication capabilities under network topology. Highly loaded nodes will consume power quickly and increase the risk of the network going offline or breaking. Additionally, loss of packet and buffer overflows would result in an outcome of increased end-to-end delay, performance deterioration of heavily loaded nodes, and transport communication loss. In this paper, a novel congestion control system is proposed to diminish the congestion on network and to enhance the throughput of the network. Initially, cluster head (CH) selection is achieved by exhausting K-means clustering algorithm. After the selection of cluster head, an efficient approach for congestion management is designed to select adaptive path by using Adaptive packet rate reduction (APTR) algorithm. Finally, Ant colony optimization (ACO) is utilized for enhancement of wireless sensor network throughput. The objective function increases the wireless sensor network throughput by decreasing the congestion on network. The proposed system is simulated with (Network Simulator NS-2). The proposed K-means C-ACO-ICC-WSN attains higher throughput 99.56%, 95.62% and 93.33%, lower delay 4.16%, 2.12% and 3.11% and minimum congestion level 1.19%, 2.33% and 5.16% and the proposed method is likened with the existing systems as Fuzzy-enabled congestion control through cross layer protocol exploiting OABC on WSN (FC-OABC-CC-WSN), Optimized fuzzy clustering at wireless sensor networks with improved squirrel search algorithm (FLC-ISSA-CC-WSN) and novel energy-aware clustering process through lion pride optimizer (LPO) and fuzzy logic on wireless sensor networks (EAC-LPO-CC-WSN), respectively. Finally, the simulation consequences demonstrate that proposed system may be capable of minimizing that congestion level and improving the throughput of the network.