Optimal Cluster Head Selection Research Articles

Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network

Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network

Enhancing wireless sensor network lifespan and efficiency through improved cluster head selection using improved squirrel search algorithm

A Wireless Sensor Network (WSN) is a significant technological advancement that might contribute to the industrial revolution. The sensor nodes that are part of WSNs are battery-powered. Energy is the most crucial resource for WSNs since batteries cannot be changed or refilled. Since WSNs are a finite resource, several techniques have been devised and used throughout time to preserve them. To extend the lifespan of WSNs, this study will provide an effective method for Cluster Head (CH) selections. Many researches are employing the Swarm-based optimization algorithm to Select the optimal CH. In this study, the Squirrel Search Algorithm (SSA) is utilized to select the optimal CH Selection in WSN. The general SSA has been modified in this study to address the exact need for CH choice in WSNs. The Improved Squirrel Search Algorithm (I-SSA) integrates a series of enhancements aimed at accelerating convergence and elevating solution quality. Notably, we’ve implemented Adaptive Population Initialization, Dynamic Step Size Control, and a Local Search Algorithm to augment the exploration and exploitation capabilities of the SSA. These enhancements collectively refine the algorithm’s ability to navigate the search space effectively, resulting in more efficient convergence towards optimal solutions. The suggested formulation’s goal function takes into account the CH balance average, factor, sink distance residual energy and intra-cluster distance. The simulations are run under a variety of circumstances. The MATLAB 2021a working setting is utilised for simulation. The proposed code of conduct SSA-C is compared with the existing protocols Grey Wolf Optimization (GWO), SSA, Chernobyl Disaster Optimizer (CDO), Sperm Swarm Optimization (SSO), A Metaheuristic Optimized Cluster head selection-based Routing Algorithm for WSNs (MOCRAW), Energy-Efficient Weighted Clustering (EEWC), and Multi-agent pathfinding using Ant Colony Optimization (MAP-ACO). The ISSA-C method achieved a Packet Delivery Ratio (PDR) of 88%, outperforming GWO, SSA, and MAP-ACO. It reduced energy consumption to 210 mJ, which is lower than other methods, and showed improved bit error rates. Cluster formation and head selection times were also reduced to 82 s and 67 s, respectively.

Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes to minimize network fragmentation and balance energy consumption. However, placing gateways near the sink node can result in increased communication overhead and higher energy consumption in regions with concentrated data flow. To address these issues, we propose an energy-efficient artificial fish swarm-based clustering cognitive intelligence protocol (EAFSCCIP). EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. The protocol focuses on reducing energy consumption and extending network lifetime by considering real-time energy levels and the proximity of nodes for CH selection. Simulations have been executed in NS3 to validate and compare the performance of the proposed algorithm with the existing clustering protocols. The results indicate that EAFSCCIP significantly enhances the packet delivery ratio (PDR) by an average of 5.33% over existing methods and improves network lifetime by 6.54% compared to traditional protocols. It also reduces energy consumption by 25.6% and decreases packet loss by 50.5%, while achieving 20.4% higher throughput at the initial stage. These improvements make EAFSCCIP a promising solution for applications like acoustic monitoring in UWSNs, providing a balance between energy efficiency and reliable data transmission.

Route Optimization and Optimal Cluster Head Selection for Cluster-Oriented Wireless Sensor Network Utilizing Circle-Inspired Optimization Algorithm

A wireless sensor network (WSN)'s lifespan and optimal orientation are greatly increased by clustering. Clustering selects non-cluster heads (NCHs) and cluster heads (CHs) and integrates sensor nodes via clusters. The purpose of this research is to find the best route and select the best cluster head for the WSN with the help of optimization methodology. The adaptive multi-path routing protocol (AMPRP), which is best chosen utilizing the circle-inspired optimization algorithm (CIOA) algorithm, is proposed in this study as a method for choosing NCHs and CHs. The optimal CHs have been chosen after the probabilities have been calculated, and the findings have been utilized to identify the optimal shortest path employing the same CIOA. To find the best route, the CIOA algorithm employs a fitness function made out of a network. The superiority of the proposed model is demonstrated by validating it with various state-of-the-art methods in terms of distinct analysis. The proposed AMPRP-CIOA for the optimal route and CH selection model in terms of energy consumption is 85.71%, 86.67%, 88.89%, and 77.78% better than EAHPW-TOPSIS, DA-EECHS, GEEC, and EADCR, respectively.

Extending WSN Lifetime with Enhanced LEACH Protocol in Autonomous Vehicle Using Improved K-Means and Advanced Cluster Configuration Algorithms

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection.

Green Optimization with Load balancing in Wireless Sensor Network using Elephant Herding Optimization

Wireless sensor networks (WSNs), which provide sensing capabilities to Internet of Things (IoT) equipment with limited energy resources, are made up of specialized transducers. Since substitution or re-energizing of batteries in sensor hubs is extremely difficult, power utilization becomes one of the pivotalmattersin WSN. Clustering calculation assumes a significant part in power management for the energy-compelled network. Optimal cluster head selection suitably adjusts the load in the sensor network, thereby reduces the energy consumption and elongates the lifetime of assisted sensors. This article centers around to an appropriate load balancing and routing technique by the utilization recently developed of Elephant Herding Optimization (EHO) algorithm that alternates the cluster location amongst nodes with the highest energy. The Scheme considered various parameter residual energy, initial energy and an optimum number of cluster head for the next cluster heads selection. The proposed model increases the lifetime of the network by keeping more nodes active even after the 2700th round. The experiment results of the trials show that the proposed EHO-based CH selection strategy outperforms the cutting-edge CH selection models.

Spiking Quantum Fire Hawk Network Based Reliable Scheduling for Lifetime Maximization of Wireless Sensor Network

ABSTRACTManaging energy consumption poses a substantial challenge within Wireless Sensor Networks (WSN) due to frequent communication among sensor nodes. A reliable scheduling framework presents a promising solution for maximizing WSN lifetime, minimizing energy consumption, and ensuring robust communication. Despite various proposed methods for reliable scheduling, energy consumption remains high. To address this, a spiking quantum fire hawk network‐based reliable scheduling in WSN is introduced. This research incorporates clustering, duty‐cycle management, and reliable routing to enhance energy efficiency. An improved Nutcracker Optimization‐based Cluster Head (CH) election and Spiking Quantum Fire Hawk Network duty cycling contribute to optimal CH selection and increased network lifetime. Additionally, a Link Quality‐based Energy Aware Proficient Trusted Routing Protocol (LQEAP‐TRP) minimizes data transmission delay, offering reliable routing. Finally, the data communication is done in the reliable route. The developed approach is executed in Network Simulator and validated with the existing protocols. The results of the simulations indicate that the proposed approach achieves a network lifetime of 99.34% and a packet delivery ratio of 99.95%.

Energy-efficient location-based routing protocol for wireless sensor networks using teaching-learning soccer league optimization (TLSLO)

Energy efficiency in wireless sensor networks (WSNs) is a crucial and fundamental design consideration. These networks typically consist of numerous small, resource-constrained sensor nodes, frequently placed in isolated or difficult-to-reach areas. This research presents a comprehensive methodology for improving the performance and energy efficiency of WSNs deployed in a designated target area. The research begins with the deployment of sensor nodes equipped with location information and the initialization of critical network parameters. Novel techniques are introduced for efficient node clustering using a Haversine-based K-means Clustering algorithm (HKMC) and an advanced hybrid optimization model, teaching-learning soccer league optimization (TLSLO), for optimal cluster head selection within clusters. Data aggregation at cluster heads is crucial for conserving energy, and data compression techniques, including the novel weighted discrete wavelet transform (WDWT)), are employed to reduce data transmission size. Furthermore, deep learning models in the form of recurrent artificial neural networks (RANN) predict energy consumption patterns, enabling the optimization of node sleep-wake schedules for a prolonged network lifetime. Simulated using Python, the proposed protocol’s performance is evaluated, demonstrating its superiority in terms of energy efficiency, latency, network lifetime, and data delivery ratio compared to existing routing protocols. This research offers a holistic approach to improving WSNs enhancing their efficiency and sustainability in resource-constrained environments.

Secured osprey-based energy efficient routing and congestion control in WSN

Secured osprey-based energy efficient routing and congestion control in WSN

Retraction Note: FRHO: Fuzzy rule-based hybrid optimization for optimal cluster head selection and enhancing quality of service in wireless sensor network

Retraction Note: FRHO: Fuzzy rule-based hybrid optimization for optimal cluster head selection and enhancing quality of service in wireless sensor network

Secured and energy aware cluster‐based routing in cross‐layer–cross‐domain WSN

SummaryWireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as “(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase.” In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

OPTIMIZING NETWORK LIFETIME IN WIRELESS SENSOR NETWORKS FOR EFFICIENT CLUSTER HEAD SECTION

Wireless Sensor Networks (WSNs) play a crucial role in monitoring and gathering data from remote environments. Maximizing network lifetime is paramount due to constrained sensor node energy. This study addresses the challenge of efficient cluster head selection to prolong network operation. The problem focuses on utilizing the Harris Hawk Optimization (HHO) algorithm for selecting optimal cluster heads in WSNs. HHO mimics the hunting behavior of Harris hawks to iteratively refine the selection process, aiming to minimize energy consumption while maintaining network coverage. The method involves initializing Harris hawks (representing potential cluster heads) within the sensor field, where their movements simulate search and convergence towards optimal locations. Through computational simulations, the effectiveness of HHO is evaluated against traditional methods like LEACH and PSO. Results indicate that HHO outperforms competitors by extending network lifetime up to 30%, with an average reduction in energy consumption by 15%. Specifically, numerical values show an increase in network lifetime from 3000 hours to 3900 hours, while reducing energy consumption from 2000 J/bit to 1700 J/bit. This research underscores the efficacy of HHO in enhancing WSN efficiency through optimized cluster head selection, promising sustainable operation in resource-constrained environments.

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

Energy aware and secure routing for hierarchical cluster through trust evaluation

Energy aware and secure routing for hierarchical cluster through trust evaluation

GTFR: A Game Theory-Based Fuzzy Routing Protocol for WSNs

Clustering is an approach exploited in Wireless Sensor Networks (WSNs) to enhance their performance and energy efficiency. Therefore, cluster formation and proper cluster head (CH) selection are of utmost importance in such scenarios. The proposed method, game theory-based fuzzy routing (GTFR) protocol, optimizes both of these phases of network operation in WSN. In the proposed work, fuzzy clustering is utilized for forming clusters with the desired quality of having low intra-cluster communication distance (IACD). In addition, CH selection is based on game theory and a fitness function that ensures optimal CH selection during two sub-phases of CH selection (tentative CH selection and final CH selection). In conjunction with using a penalty function for the clustering game, the proposed work also considers the nodes’ history of acting as a CH as a fitness criterion to enhance performance by impartially rotating the role among all the nodes. The proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL protocols in terms of stability by 238.27%, 183.94%, 432.04%, and 47.31%, respectively. Rigorous simulations prove that the proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL in all three considered network scenarios.

DBlock-RLB: An energy efficient framework for intelligent routing and trading based load balancing in SDWSN environment

DBlock-RLB: An energy efficient framework for intelligent routing and trading based load balancing in SDWSN environment

Energy and Security-Aware Data Transmission in IoT: An Optimal Cluster Head Selection Approach with Spiral Chip Dynamics Optimizer (SCDO)

Energy and Security-Aware Data Transmission in IoT: An Optimal Cluster Head Selection Approach with Spiral Chip Dynamics Optimizer (SCDO)