Optimal Cluster Head Selection Research Articles

Secured Osprey-Based Energy Efficient Routing and Congestion Control in WSN

An adaptive metaheuristic optimization-based QoS-aware, energy-balancing, secure routing protocol (AQoS-ESRP) is proposed in this article. The network is modelled as a biconcentric hexagon (BiCon-HexA), and the clusters are formed within the BiCon-HexA network. The BiCon-HexA is divided into six sectors to support effective data aggregation, and then clusters are formed within all sectors. The optimal cluster head (CH) selection mechanism is modelled by an Adaptive Hunter-Prey Optimization (AdapH-PO) algorithm considering QoS parameters. Data aggregation is then done securely with an enhanced encryption approach. Here, upgraded elliptic curve cryptography (UEllip-CC) is used to encode data in CH. This UEllip-CC approach provides security improvements in data transmission. Furthermore, in this study, CHs are combined in the multi-hop routing of data packets to reduce the power consumption problems of wireless sensor networks (WSN). To determine the optimal route for data transmission, an energy-balanced multi-path routing algorithm called improved convolutional osprey network (ICON) is presented. Nevertheless, the data transmission nodes can be overloaded in the data routing phase. Here, the congestion problem can be solved by applying an improved version of the Random Early Detection (RED) congestion control model to discard the data packets more noticeably. The simulation of AQoS-ESRP is done with Matlab, and the performance is evaluated using different metrics. When compared to existing systems, the simulation results clearly indicate a significantly higher throughput and delay. Thus, the AQoS ESRP model is employed to maximize the overall data transfer in the 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

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

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

With the rapid growth of technology advancement in wireless communication have resulted in interconnection of large number of devices covering wide range of application services. Wireless sensor network (WSN) have become popular and provide various benefits, including low-power smart computation, compact size, self organizing and ability to route via routing protocols. WSN covers a wide range of application services, including home automation, smart cities, fire detection, unmanned aerial vehicle (UAV), and object tacking. Sensor nodes are tiny, battery operated, and suffer from fundamental restrictions such as available bandwidth, memory space, power restrictions, and limited resources. Due to their communication characteristics and working environment, WSN faces energy optimization and secure data communication challenges to increase network lifetime and defend against malicious nodes. To overcome energy scarcity and consumption issues, clustering methods have proven smart and effective approaches to reduce energy consumption and extend network life. Low energy adaptive clustering hierarchy (LEACH), well-known clustering method, enhances energy efficiency by selecting cluster head (CH) on a rotation basis by employing random integer technique. To extend network stability period, extensions of LEACH was proposed. LEACH and its extensions mainly focus on optimal selection of CH and fail to concentrate on secure communication. Integration of blockchain and real-time message content validation (RMVC) in LEACH was modelled to detect malicious node through secure hash algorithm. However, existing scheme had complex cryptographic functions and higher computation overhead. In this paper, we present energy aware and secure routing (EASR) for hierarchical cluster based WSN. Existing LEACH is modified and an improved threshold value is adapted to select secure optimal CH and trust based routing model which combines direct and indirect trust opinion values to resist routing attacks for establishing trustworthy and reliable routes towards base station.

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

Wireless Sensor Networks (WSNs) is an emerging field that enables them to work in complex scenarios with enough flexibility. The WSN systems are improved in terms of network flexibility by integrating Software Defined Network (SDN) to obtain SDWSN environment. However, security and energy efficiency are the major issues in the SDWSN environment which leads to low network lifetime. To resolve the existing issues, we proposed DAG-based BLOCKchain-Routing and Load Balancing Model (DBLOCK-RLB). The proposed approach performs routing and load balancing in the SDWSN environment in an energy efficient manner with high security. The SDWSN environment is composed of four major planes as data plane, switch plane, control plane, and application plane. The sensor nodes in the data plane are authenticated priory to ensure legitimacy and reduce unwanted network traffics by adopting Camellia Encryption Algorithm (CAE) which provides secret key to the user by considering several metrics. The authenticated sensor nodes are clustered to reduce energy consumption using Adaptive Threshold based Network Partitioning in which the circular SDWSN environment is split into equal parts and optimal Cluster Head selection is done by considering clustering metrics. From the clustered network, the CH is responsible for intelligent routing. For intelligent routing, we utilize Dual Agent-Twin Delayed Deep Deterministic Policy Gradient (DA-TD3) in which one agent for optimal forwarder selection and another agent for optimal route selection. After routing, the load balancing is done to reduce the network overhead and scalability issues. The proposed work performs trading based load balancing by utilizing Stackelberg Game Model in which all the local controllers are considered as followers and global controller is considered as leaders. All the transactions are stored in the DAG blockchain to reduce the scalability and Proof-of Authentication (PoAH) used to verify the transactions. The proposed work is simulated using NS-3.26 simulation tool and compared with several existing works by validation metrics like throughput at 2.55(mbps), latency at 27.1(ms), packet delivery ratio at 91.5(%), network Lifetime at 546.5(s), and energy consumption at 14.5 (J). The results show that the proposed work outperforms better than the existing works.

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)

Energy Optimized Cluster Head Selection Based on Multi-Objective Sand Cat Swarm Optimization in Under Water Wireless Sensor Networks

Energy Optimized Cluster Head Selection Based on Multi-Objective Sand Cat Swarm Optimization in Under Water Wireless Sensor Networks

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Heterogeneous Wireless Sensor Network Design with Optimal Energy Conservation and Security through Efficient Routing Algorithm

A heterogeneous wireless sensor network (H-WSN) comprises multiple sensor nodes having varied abilities, like diverse processing power and sensing range. H-WSN deployment and topology control seem to be more difficult than homogeneous WSNs. Research on H-WSNs has increased in the last few years to improve real-time sensor networks' reliability and deliver better networking services than a homogenous WSN does. When it comes to H-WSN's energy consumption and security, the major problem remains the efficient routing process. To that end, this research aims at demonstrating how an efficient routing algorithm of hierarchical H-WSN can greatly enhance the network's performance. It is important to note that the nodes' capabilities mostly determine the suitability of a given routing algorithm. Hence, the H-WSN design issues for routing in a heterogeneous environment are discussed in this paper. This research designs an Optimal Energy Conservation and Security-aware Routing Algorithm (OECS-RA) for H-WSN using clustering and a secure-hop selection scheme. In this proposed model, the optimal cluster head selection and routing have been found through various computational stages based on the energy conservation of each sensor node. It further secures the transmission by selecting the secured node with credential factor computation and comparing each hop of the optimal route. The MATLAB simulation scenario finds the significant performance of the routing mechanism with security compared to existing models. The proposed OECS-RA gives highly recognizable throughput, lifetime, energy efficiency, and reliability. With these results, this proposed algorithm is suggested for real-time implementation in the medical industry, transportation, education, business, etc.

Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique

Disaster prediction systems enable authorities and communities to identify and understand the risks associated with various natural and man‐made disasters. Disaster prediction systems are essential tools for enhancing public safety, reducing the impact of disasters, and enabling more informed and strategic decision‐making across various sectors. Their development and implementation represent a crucial aspect of modern disaster risk management and resilience systems. This novel technique introduces a modern approach to forest fire prediction by integrating a deep learning model with an optimized cluster head selection technique. The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. The proposed system comprises two core components: a deep learning model for predictive analysis and an optimized selection process for cluster heads in sensor networks. The deep learning model utilizes various environmental data parameters such as humidity, wind speed, temperature and former fire incidents. These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. The model is trained on historical data to improve its predictive accuracy, and its performance is continuously evaluated against new data. Simultaneously, the optimized cluster head selection using the cat‐mouse optimization technique plays a crucial role in efficiently managing sensor networks deployed in forests. The integration of these two components results in a robust system capable of predicting forest fires with high precision. The system not only assists in early detection and timely alerts but also contributes to the strategic planning of firefighting and resource allocation efforts. This approach has the prospective to significantly lessen the impact of forest fires, thereby protecting ecosystems and communities.

An Optimized Cluster Head Selection and Secured Wireless Sensor Network Using MRSA

Currently, Wireless Sensor Networks (WSNs) play a vital role in remote monitoring and sharing of information in an infrastructure. Even though WSNs have numerous advantages, their usage is reduced by node's lifetime and data loss. The aforementioned problems in WSNs were overcome by using energy efficient protocols by various researchers. But most of the authors focused on either trust model or energy efficient model, or analyzed both models using separate algorithms. However, this leads to repeated process and to high energy consumption. Here, the cluster head is selected by using the swarm intelligence algorithm and LEACH protocol. The hybrid ant colony and modified Particle swarm algorithm are used to select the cluster heads. The CH selection depends on the two parameters, namely residual energy and number of successful transmissions of node. The node's trust is assessed using these two parameters (transmission nodes and residual energy), which also assist in ensuring the safety of the data and energy. The proposed system uses a digital signature algorithm known as the modified RSA algorithm to achieve network security when performing cryptography on data. The performance obtained by the proposed method was compared to other existing techniques in terms of throughput, energy consumption, packet delivery ratio, and network lifetime.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

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.

Hybrid Multi-Objective-Derived Horse Herd and Dragonfly Algorithm-Based Energy-Efficient Secured Routing in WSN

Energy efficiency and security have become prominent aspects of Wireless Sensor Networks (WSNs) for transmitting data. The major challenge is to increase the “Quality of Service” (QoS) since it is restricted by fewer constrained resources. Although the existing routing protocols acquire efficient transmission, they are still subsisting with the challenges of improving security and energy conservation. It will be helpful in practical implications like the military, weather forecasting and healthcare industry. As the nodes are operated by the energy preservation of the battery, developing an energy-efficient protocol is challenging. Since WSN possesses massive numbers of sensor nodes, it also has to avoid long-term communication, and the clustering mechanism is a prerequisite. Considering the diverse objectives like delay, energy and distance, there are still remarkable challenges to develop the routing protocol. Moreover, security enhancement becomes another challenging issue over the network for data transmission through the sensor nodes. To overcome these issues, a novel energy-efficient routing model is designed in this paper by using a hybrid algorithm and the multi-objective derivatives. Initially, the clustering approach is employed to reduce the complexity and improve the security in an energy-efficient manner. Therefore, the Cluster Head (CH) is selected significantly using the novel Hybrid Horse Herd-Dragonfly Optimisation (HHHDO). The hybrid algorithm is developed by integrating Horse Herd Optimisation (HHO) and Dragonfly Algorithm (DA). Here, the optimal CH selection is achieved by deriving the multi-objective function regarding “distance, delay, energy, load and trust of nodes”. Trust is measured with the Neural Network (NN)-based HHHDO (NN-HHHDO) model. Depending on the trust evaluation and energy efficiency, the proposed model obtains better communication between the nodes. In the end, the experimentation of the recommended model is made using various measures and it’s ensured that the suggested technique enhances trust among the nodes and energy conservation in comparison to the existing algorithms.