Cluster Head Selection Research Articles

𝑃c𝜀𝜅max-Means++: Adapt-𝑃 Driven by Energy and Distance Quality Probabilities Based on 𝜅-Means++ for the Stable Election Protocol (SEP)

Attaining a prolonged network lifetime, maximized coverage, and high performance are vital design factors that have to be maintained in a Wireless Sensor Networks (WSN). Such factors are dependent on the stability and optimality of the protocol employed to formulate Sensor Nodes (SNs) into mutual clusters that effectively work around fulfilling specific performance goals. SEP is a heterogeneity-aware protocol implemented based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, designed to prolong the stability period of the network defined by the time interval before the death of the first SN. Nevertheless, adaptability in design is a scheme that has not been extensively applied in the formation of WSN election protocols. Adapt-𝑃 is a probabilistic model for adaptivity designed to evolve the probability of selecting a cluster-head based on the active status of the WSN, represented by the residual energy of and distances between SNs. However, the adaptive probability 𝑃adp formalized in Adapt-𝑃 is developed based on the remaining number of SNs 𝜁 and optimal clustering κmax, yet 𝑃adp does not implement the probabilistic ratios of energy and distance factors in the network. Furthermore, Adapt-𝑃 does not localize cluster-heads in the first round properly because of its reliance on distance computations defined in LEACH, that might result in uneven distribution of cluster-heads in the WSN area and hence might at some rounds yield inefficient consumption of energy. This paper utilizes 𝑘-means++ and Adapt-𝑃 to propose 𝑃c𝜅max-means++ clustering algorithm that better manages the distribution of cluster-heads and produces an enhanced performance. The algorithm employs an optimized cluster-head election probability 𝑃c developed based on energy-based 𝑃𝜂(𝑗,𝑖) and distance-based 𝑃𝜓(𝑗,𝑖) quality probabilities along with the adaptive probability 𝑃adp, utilizing the energy 𝜀 and distance optimality 𝑑opt factors. Furthermore, the algorithm utilizes the optimal clustering 𝜅max derived in Adapt-𝑃 to perform adaptive clustering through 𝜅max-means++. The proposed 𝑃c𝜅max-means++ is compared with the energy-based algorithm 𝑃𝜂𝜀𝜅max-means++ and distance-based 𝑃𝜓𝑑opt𝜅max-means++ algorithm, and has shown an optimized performance in term of residual energy and stability period of the network.

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.

A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads. A fuzzy-based cluster head election strategy is proposed here to improve energy efficiency in wireless sensor networks. The input parameters of the fuzzy inference system are chosen as the residual energy, the node centrality, and the mobility factor. The system generates an output of the chance of a node being selected as a cluster head based on the combination of the values of the given inputs. The simulation results show that the proposed model reduces the network’s overall energy consumption and extends the sensor nodes’ lifetime.

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.

Clustering Based Energy Efficient Routing for Wireless Sensor Network Using Particle Swarm Optimization

The clustering strategy is the most effective and efficient way to preserve energy in the Wireless Sensor Network (WSN). However, the cluster heads in the hierarchical clustering approach use the majority of the energy that is required to carry out the operations. These operations include receiving the data from the sensor nodes, aggregating it, and then eventually transmitting it to the base station. When choosing the appropriate cluster head, you can play a significant part in reducing the amount of energy that is consumed by the WSN and, as a result, extending its lifespan. A technique for the selection of energy-efficient cluster heads that is based on the particle swarm optimization method is proposed in this study (PSO-EECH). For the method that has been proposed to measure the amount of energy used, we need to take into account the cluster distance, the distance between each sensor node and the nodes that are nearby, and the amount of residual energy that is left in sensor nodes. The aforementioned structure is also capable of doing cluster building, in which the non-cluster head node can follow its CH based on the determined weight function. The proposed PSO-EECH approach has been put through extensive testing, and the results have shown that it possesses a high degree of accuracy in every scenario. The outputs of the proposed algorithm are compared with those of other clustering-based algorithms already in existence, and the conclusions of this comparison have reported that our method outperforms the other existing methods.

Cluster Head Selection and Secured Routing Using Glowworm Swarm Algorithm and Hybrid Security Algorithm for Over IoT-WSNs

WSNs (Wireless Sensor Networks) have received a lot of attention in a number of industries due to its self-configurability, simplicity in maintenance, and scaling capacities. WSN is configured with extra nodes in order to transport data around the network. The usual characteristics of sensor networks are low bandwidth, low energy, constrained power supply, and little memory space, which makes security particularly difficult in these networks. CH (Cluster heads) in a cluster experience heavier traffic loads than other cluster nodes. This results in the hotspot problems. Therefore, in a cluster-oriented routing scheme, picking the right CH is crucial. The two-level security is recommended in this research to safeguard data transmission via the WSN. A novel CH selection methodology is presented in order to increase the network's lifespan and energy effectiveness. This work also combines the fruit fly algorithm (FGF) with the GSA (firefly swarm algorithm) based on fitness to choose the optimal CH. To defend the network from assaults, it employs a hybrid security method that combines the Diffie-Hellman key exchange mechanism and the ECC (elliptic curve cryptography) algorithm. The suggested GSA-HSA protocol consistently produces good performance results in terms of MSR (Message Success Rate), E2E (End-to-End) Latency, NT (Network Throughput), and network throughput. average AEE (energy efficiency). The suggested approach outperforms all prior research by giving the wireless sensor network combination security, optimised coverage, and energy savings.

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.

Developing a novel energy efficient routing protocol in WSN using adaptive remora optimization algorithm

Energy efficiency is a complicated process in designing Wireless Sensor Networks (WSNs). Effective routing is a precise solution for the energy efficiency constraint in WSN owing to the higher sharing of energy consumption during communication. Thus, routing is an alternative way out to this issue along with hierarchy-based clustering techniques. The clustering in WSN is carried out by picking the Cluster Head (CH), which is assumed as the essential or leader node. They perform more tasks, and thus, more energy is consumed. The clustering classifies the nodes into groups to avoid the huge communication range that is deputized to the CH. Hence, this paper establishes a routing algorithm for saving energy using a new optimization strategy via solving the multi-function formulation of the developed protocol in WSN. Here, the cluster head selection is performed with the Adaptive Remora Optimization Algorithm (AROA) to derive the multi-function derivation. The multi-function formulation undergoes with known factors such as energy consumption, distance, throughput, Packet Delivery Ratio (PDR), and path loss. The experimental result indicates an enhanced network lifetime and saving the energy of the developed heuristic-based routing protocol in WSN.

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.

SMEOR: Sink mobility‐based energy‐optimized routing in energy harvesting‐enabled wireless sensor network

SummaryFor accurate environmental monitoring, a wireless sensor network (WSN) uses a collection of autonomous sensor nodes (SNs) that are dispersed at random across a given area. WSNs are used for various applications that require real‐time data. While the SNs in a WSN are limited in their ability to generate energy, the routing process is seen as a vital means of improving the network's ability to conserve resources and prolong its useful life. In this research work, we present sink mobility‐based energy‐optimized routing (SMEOR) in energy harvesting (EH)‐enabled WSN. While following the cluster‐based routing, the election of cluster head (CH) is executed using our proposed extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy‐hole problem, four data‐collection energy‐unlimited nodes are used around the periphery of the network. The sink moves from one EH‐enabled node to another to collect data. Further, security in SMEOR is ensured by encrypting the data using a stream cipher and a stochastically generated security key. It is evident from the simulation analysis that SMEOR delivers network longevity and supreme performance.

A Service-Driven Routing Algorithm for Ad Hoc Networks in Urban Rail Transit

Due to increased traffic pressure, traditional urban rail vehicle–ground communication systems are no longer able to meet the increasing communication requirements. In this paper, ad hoc networks are applied to urban rail transit vehicle–ground communication systems to improve link reliability and reduce transmission delay. In the proposed network, a service-driven routing algorithm is proposed, which considers the distance factor for cluster head selection and optimizes the routing transmission delay by service priority and congestion level. An auxiliary node-based routing maintenance mechanism is also proposed to avoid the problem of frequent breakage of communication links due to the high-speed movement of trains. Through the simulation, the proposed algorithm can effectively reduce the packet loss rate, end-to-end delay, and routing overhead of vehicle–ground communication compared with the traditional routing algorithm, which is more conducive to meeting the next generation of urban rail transit vehicle–ground communication requirements.

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 secure multifactor-based clustering scheme for internet of vehicles

The development of the Internet of Vehicles (IoV) has been made possible through a variety of communication technologies and advanced AI techniques. In this context, ensuring stable and efficient communication for IoV is extremely important. It addresses several challenges related to security issues, high dynamism, constant connection outages, and the expected high traffic density. To overcome these challenges, vehicle clustering is a viable strategy for a reliable communication environment. The majority of current research focuses on solving the problem of cluster stability and efficiency by utilizing one or multiple factors, particularly vehicle location, mobility, and behavior. This article introduces an efficient Multifactor Clustering Scheme for IoV (MFCS-IoV). MFCS-IoV includes two stages: cluster formation and cluster head selection. The cluster formation is based on the improved K-means algorithm, considering both the vehicle mobility and final destination within the driving zone. While, a weighted cluster fitness function that includes mobility, behavior, dynamic location, and security is used to optimally select the Cluster Head (CH). Blockchain technology has been integrated into the model to safeguard the privacy of information like destination and other vehicle parameters. Simulation results demonstrate the success of MFCS-IoV in partitioning the vehicles into stable clusters and selecting the optimal cluster heads based on the proposed parameters. The effectiveness of MFCS-IoV is demonstrated by simulating different scenarios of 50 to 300 vehicles in the driving area. A comparison with related works shows that MFCS-IoV outperforms other schemes regarding average node-to-node delay, packet delivery rate, and throughput. Additionally, the proposed MFCS-IoV increases communication reliability by providing stable clusters while maintaining security measures.

A WSN clustering routing method based on an improved WOA-optimized K-means clustering algorithm

To address the problem of transmission energy consumption and premature node death in wireless sensor networks (WSN), a novel routing strategy by IWOA that optimizes the K-means (KM) is proposed. WOA is improved by using it to optimize the KM algorithm, and more evenly distributed and suitable cluster centers are selected as the initial cluster heads (CH). In the CH election process, a CH election function is established, and the optimal CH under the influence of multiple factors is obtained. The simulation results have verified the feasibility of the proposed scheme in this paper.

Dragon fly algorithm based approach for escalating the security among the nodes in wireless sensor network based system

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.

Enhancing Firefly Algorithm for better Network lifetime optimization in Healthcare Monitoring System - Cloud Computing Environment

The Internet of Things (IoT), a new phenomenon in the technology industry, is mostly responsible for updating healthcare systems by gathering and analyzing patient physiological data through wearable technology and sensor networks. It is difficult to process so much data from so many IoT devices in such a short amount of time. Maximizing the network lifetime is one of the most significant tasks faced by any wireless sensor network. The objective of the study described in this paper was to apply swarm intelligence metaheuristics to optimize the cluster head selection. In order to extend the lifetime of the WSN, we have implemented both the original firefly algorithm (FA) and the proposal for the revised FA. Additionally, According to the proposed study, sensitive data is created and stored by IoT devices, which are vulnerable to attack, and data processing is handled by the edge server. Standard security algorithms like AES, DES, and RSA make it difficult for the majority of IoT devices to function successfully because of their restricted resources. For real-time processing, visualization, and diagnosis, the real-time data is subsequently sent to a distant cloud server.

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

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

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.

A Novel Approach to Reduce Video Traffic Based on Understanding User Demand and D2D Communication in 5G Networks

Mobile network customers today prefer video news updates than text. However, the conventional cellular base stations may have challenges in simultaneously managing video transmission for all users, particularly in extensive network deployments. To address the aforementioned challenge discussed in this article, a proposed solution is presented. To lessen the burden of video traffic, we intend to improve the efficiency of D2D multicast network transmission in cellular D2D communications. A scalable video multicast strategy that accounts for user demand and makes use of device-to device (D2D) communication is offered as a means to this end. In the first step, we analyze the users' interests by looking at things like their video viewing habits and criteria like video popularity. The purpose of this research is to ascertain whether or not users are inclined to share movies, which would increase the number of viewers who receive multicast clusters. In the next stage, an algorithm for selecting cluster heads will be developed, taking into account criteria including channel quality, social metrics, and the requirements for video quality. In comparison to similar systems without multiplexing capacity, the proposed method exhibits superiority in three distinct aspects. The initial modification exhibits a surge ranging from 70% to 375 %. Furthermore, the overall growth rate of the entire system ranges from 32% to 64%. Furthermore, it is worth noting that there has been a significant rise of 90% in the number of network recipients, indicating a substantial boost in customer satisfaction.

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.