Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks

Recently, wireless sensor networks (WSNs) have had many real-world applications; they have thus become one of the most interesting areas of research. The network lifetime is a major challenge researched on this topic with clustering protocols being the most popular method used to deal with this problem. Determination of the cluster heads is the main issue in this method. Cognitively inspired swarm intelligence algorithms have attracted wide attention in the researh area of clustering since it can give machines the ability to self-learn and achieve better performance. This paper presents a novel nature-inspired optimization algorithm based on the gravitational search algorithm (GSA) and uses this algorithm to determine the best cluster heads. First, the authors propose a rank-based definition for mass calculation in GSA. They also introduce a fuzzy logic controller (FLC) to compute the parameter of this method automatically. Accordingly, this algorithm is user independent. Then, the proposed algorithm is used in an energy efficient clustering protocol for WSNs. The proposed search algorithm is evaluated in terms of some standard test functions. The results suggest that this method has a better performance than other state-of-the-art optimization algorithms. In addition, simulation results indicate that the proposed clustering method outperforms other popular clustering method for WSNs. The proposed method is a novel way to control the exploration and exploitation abilities of the algorithm with simplicity in implementation; therefore, it has a good performance in some real-world applications such as energy efficient clustering in WSNs.

Energy efficiency is critical in Wireless Sensor Networks (WSNs), pivotal for Internet of Things (IoT) applications like environmental monitoring and smart cities. Clustering protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH) extend network lifetime through Cluster Head (CH) rotation, but random CH selection often leads to suboptimal energy use. This study evaluates eight hybrid Particle Swarm Optimization-based LEACH (PSO-LEACH) variants to optimize CH selection and clustering: FA-PSO-LEACH (Firefly Algorithm), CSA-PSO-LEACH (Crow Search Algorithm), PSO-LEACH with CBTEERA, Adaptive PSO-LEACH, ABC-PSO-LEACH (Artificial Bee Colony), ACO-PSO-LEACH (Ant Colony Optimization), WOA-PSO-LEACH (Whale Optimization Algorithm), and Hybrid GA-PSO-LEACH (Genetic Algorithm). The primary research objective is to compare these variants in terms of energy efficiency, network lifetime, and clustering quality to identify the most effective strategies for prolonging WSN operation in IoT environments. Specifically, this paper addresses the research question: Which hybrid PSO-LEACH variant provides the optimal balance between early-stage stability and long-term energy conservation in a simulated WSN with 50 nodes? Each variant uses a unique metaheuristic for CH selection based on residual energy and distance, with PSO refining clustering. A Python simulation modelled a 100m × 100m WSN with 50 nodes, assessing network lifetime (First Node Died, FND; Half Nodes Died, HND; Last Node Died, LND), energy consumption, and clustering quality. Results show ABC-PSO-LEACH leading with FND at 57 rounds and LND at 462 rounds, followed by WOA-PSO-LEACH (HND 148, LND 406), surpassing others (e.g., PSO-LEACH with CBTEERA: FND 10, LND 280). WOA-PSO-LEACH’s balanced energy distribution excels in mid-to-late stages, despite computational overhead. These bio-inspired hybrids outperform traditional LEACH, with ABC-PSO-LEACH and WOA-PSO-LEACH ideal for prolonged WSN operation. Future work aims to enhance early-stage performance and explore dynamic topologies.

Classical clustering protocols in wireless sensor networks (WSNs) assume that all nodes are equipped with the same amount of energy. As a result, they cannot take full advantage of the presence of node heterogeneity. In this study, a stable and energy‐efficient clustering (SEEC) protocol for heterogeneous WSNs is proposed. In addition, the extension to multi‐level of SEEC is presented. It depends on the network structure that is divided into clusters. Each cluster has a powerful advanced node and some normal nodes deployed randomly in this cluster. In the multi‐level architectures, more powerful supper nodes are assigned to cover distant sensing areas. Each type of nodes has its role in the sensing, aggregation or transmission to the base station. At each level of heterogeneity, the optimum number of powerful nodes that achieves the minimum energy consumption of the network is obtained. The proposed protocol is a heterogeneous awareness to prolong the stability period, which is crucial for many applications. The performance of the proposed protocol is compared by existing homogeneous and heterogeneous protocols. Simulation results show that the proposed protocol provides a longer stability period, more energy efficiency and higher average throughput than the existing protocols.

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. The network characteristics of existing classical clustering protocols for wireless sensor network are homogeneous. Clustering protocols fail to maintain the stability of the system, especially when nodes are heterogeneous. We have seen that the behavior of Heterogeneous-Hierarchical Energy Aware Routing Protocol (H-HEARP) becomes very unstable once the first node dies, especially in the presence of node heterogeneity. In this paper we assume a new clustering protocol whose network characteristics is heterogeneous for prolonging of network lifetime. The computer simulation results demonstrate that the proposed clustering algorithm outperforms than other clustering algorithms in terms of the time interval before the death of the first node (we refer to as stability period). The simulation results also show the high performance of the proposed clustering algorithm for higher values of extra energy brought by more powerful nodes.

The Comparative Study of three main energy efficient wireless sensor networks (WSNs) protocols are studied according to their working. The WSN protocols are Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS) and Threshold Sensitive Energy Efficient Sensor Network (TEEN). These three protocols work on hierarchical structure to succeed their own functioning and improvement energy efficiency. Here include four quality of services (QOS) parameter performance namely “Average End to End Delay”, “Average Energy Consumption”, “Packet Delivery Ratio” and “Throughput” which are developed and compare to their QOS parameter performance. At the end of the analysis, design an overall study for these protocols with respect to their QOS parameter and further generalized the future scope of this study.

Wireless sensor network (WSN) consists of thousands of sensor nodes with low power, low memory, and low computational capabilities. Therefore, energy issues have to be taken into considerations while designing WSN applications and protocols. Routing protocols such as LEACH and LEACH-C have been proposed for energy conservation in WSN. However, these typical protocols have drawback of unequal energy depletion in cluster heads (CHs) due to the different transmission distance from each CH to the base station (BS). In order to minimize the energy consumption caused by large distance of CHs from the BS, we propose dynamic clustering and distance aware routing protocol (DDAR) for WSN which takes into account of node distance for selecting CH, dynamic approach of selecting the CH nodes, and two level hierarchy of clustering with super cluster head (SCH) node near to the BS. Simulation results show that the proposed DDAR protocol consumes about 15.5% less energy than conventional LEACH protocol.

—Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way, since their energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor networks. In this paper, we introduce a clustering protocol with mode selection (CPMS) for wireless sensor networks. Our scheme improves the performance of BCDCP (Base Station Controlled Dynamic Clustering Protocol) and BIDRP (Base Station Initiated Dynamic Routing Protocol) routing protocol. In CPMS, the base station constructs clusters and makes the head node with the highest residual energy send data to the base station. Furthermore, we can save the energy of head nodes by using the modes selection method. The simulation results show that CPMS achieves longer lifetime and more data message transmissions than current important clustering protocols in wireless sensor networks.

Wireless Sensor Networks (WSNs) are a component of various applications, including environmental monitoring and the Internet of Things (IoT). Energy efficiency is one of the significant issues in WSNs, since sensor nodes are usually battery-powered and have limited energy resources. The Enhanced Distributed Energy-Efficient Clustering (DEEC) protocol is one of the most common methods for improving energy efficiency and network lifespan by selecting cluster heads according to residual energy. Nevertheless, standard DEEC methods are limited in dynamic environments because of their fixed nature. This paper presents a novel modular implementation of the DEEC protocol for Wireless Sensor Networks, addressing the limitations of the standard DEEC in dynamic and heterogeneous environments. Unlike the typical DEEC protocol, the proposed approach incorporates realistic energy models, supports heterogeneous nodes, implements load balancing, and enables dynamic cluster head selection Numerical simulations in MATLAB® demonstrate that the improved DEEC protocol achieves a 133% longer stability period (first node death at 1166 rounds vs. 472 rounds), nearly doubles the network lifetime (4000 rounds vs. 2111 rounds), and significantly enhances energy efficiency compared to the standard DEEC. These results verify the effectiveness of the proposed enhancements, making the protocol a robust solution for modern WSN and IoT applications.

Energy is one of the most important factors in the design of routing protocols in wireless sensor networks. This paper first analyzes the energy consumption in typical clustering protocols and finds that the consumption is not evenly distributed among nodes. Thus some nodes die quickly with the reduction of the network lifetime. Then a new energy-balanced strategy is introduced in clustering protocols. The strategy assigns the head communication load to base station by detecting the energy consumption in the cluster heads. The evenly distributed energy among the nodes is realized by controlling the head consumption. Simulation results show that the lifetime of the network is significantly prolonged with the new energy-balanced strategy.

A difficult constraint in the design of wireless sensor networks (WSNs) is the limited energy resource of the batteries of the sensors. This limited resource restricts the operating time that WSNs can function in their applications. Routing protocols play a major part in the energy efficiency of WSNs because data communication dissipates most of the energy resource of the networks. There are many energy-efficient cluster-based routing protocols to deliver data from sensors to a base station. All of these cluster-based algorithms are heuristic. The significant benefit of heuristic algorithms is that they are usually very simple and can be utilized for the optimization of large sensor networks. However, heuristic algorithms do not guarantee optimal solutions. This article presents an analytical model to achieve the optimal solutions for the cluster-based routing protocols in WSNs.

The unique nature of wireless sensor networks (WSNs) poses new design challenges for development of applications, protocols and algorithms. In recent years, a substantial amount of research effort has been directed towards overcoming the underlying challenges to minimize energy consumption by the sensor nodes. The exchange of routing control packets has a significant impact on energy consumption from the small irreplaceable and non rechargeable batteries of the sensor nodes. In this paper, we propose a cross-layer adaptive routing (CLAR) protocol for WSNs, which aims to minimize the use of routing control packets in WSNs; thereby minimizing energy consumption which in turn prolongs network-lifetime. We also develop analytical model to derive energy consumption for route establishment and present simulation results, which reveal better performance and improved energy efficiency in WSNs when using the proposed cross-layer adaptive routing protocol.

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.

Wireless sensor networks (WSNs) are quickly becoming an essential part of today's modern infrastructure, and it is becoming crucial to consider time synchronisation problems in WSNs. Furthermore, one of the main design issues in WSN time synchronisation is the efficient use of energy at each sensor node to prolong the lifespan of the network. This paper describes a new energy-efficient time protocol based on the spanning tree of the network model graph in a WSN. Three major cases are discussed: 1) time synchronisation using the energy-efficient spanning tree (EST) protocol in a WSN with approximately 300 sensors; 2) an improved EST protocol in large-scale WSNs; 3) an improved EST protocol under weighted graph. Additionally, a series of practical algorithms are proposed in this paper, where both energy and time efficient can be clearly shown through a number of simulated examples along with their comparative results.

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Studies of clustering in Wireless Sensor Networks (WSNs) usually tackle the problems of designing new algorithms and compare them based on a set of properties (e.g. energy efficiency, scalability), lacking the understanding of the underlying mechanisms and communication patterns that lead to these properties. Our approach tackles this lack of understanding by applying techniques developed by complex systems scientists. Functional topology graphs, which describe the interactions between system parts, are used to represent different implementations of clustering in WSNs. We employ a complexity metric - functional complexity (C F ) - to quantify the potential of the functional topology to transport information. Our analysis highlights the trade-off between scalability and energy efficiency, showing that higher values of C F indicate higher scalability and lower energy efficiency.