Energy-balanced clustering routing protocol based on task separation in wireless sensor networks

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

One of the major design goals in Wireless Sensor Networks (WSN) is to improve network lifetime. In this paper, an efficient clustering protocol with multi-hop routing is proposed which aims at increasing network lifetime. In the initialization phase, the base station elects cluster heads (CHs) based on the residual energy of the sensor nodes. The clusters formed by the CHs initially are maintained till all its member nodes die, while the cluster heads are rotated within the formed clusters based on the residual energy levels of the nodes at the beginning of each round. The cluster heads aggregate the data received from cluster members and send it to the base station in either one or two hops depending on whether the node is a level 1 CH or a level 2 CH, which is decided by the CH's distance from the base station. The proposed clustering protocol has shown a significant 300 percent improvement in network lifetime as measured by the time till the first death of a node and a 10 percent increase in the total number of packets delivered to the base station, when compared to conventional LEACH. The MATLAB simulation performed considers the network area to be rectangular (100m x 100m) with the base station outside the network area.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

Wireless sensor networks (WSNs) face challenges in maintaining network lifetime due to energy limitations. To optimize energy usage, techniques such as node clustering and data transmission through the shortest path are employed. However, the selected Cluster Head (CH) node eventually becomes inactive as its energy is depleted during continuous transmission to the sink. To overcome this issue, we propose the utilization of Energy-Efficient Bat-Moth Flame Optimization (EEBMFO) in WSNs, aiming to enhance network lifetime. Our strategy makes use of the echolocation signal pattern that bats use to identify prey within a certain range. In a similar manner, nodes fall into clusters within the range of the CH, and the CH is chosen by looking at the highest residual energy. In addition, we use spiral path data transmission and Moth Flame optimization to route data from the source node to the CH. By combining bat and moth flame optimizations and considering each node’s residual energy, we aim to improve the network’s lifespan. With the use of simulations and performance measures such network lifetime, throughput, latency, dependability, and network stabilization, this research provides a thorough analysis of the suggested EEBMFO approach in WSNs. When compared to current techniques, the results show a noteworthy 11–16% increase in network longevity. These findings validate the efficacy of EEBMFO in prolonging the lifespan of WSNs, offering a promising solution for energy-efficient and sustainable wireless sensor networks.

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

Most of the sensor nodes in a Wireless Sensor Network (WSN) have limited energy. In order to increase the network lifetime, some energy efficient algorithms were proposed earlier. It has been a challenge to design wireless sensor networks to enable applications for oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The main objective of this work is to find out the data routing method such that the battery life of sensor nodes can be prolonged. In this paper, A Two Cluster Head Energy efficient Wireless Sensor Network (TCHE-WSN) Algorithm was put forward. WSN consists of sensor nodes which sense the physical parameters such as temperature, humidity, pressure and light etc and send them to a fusion center namely Base Station (BS) from where one can get the value of physical parameters at any time. Requirement of monitoring the environment might be anywhere, like middle of the sea or under the earth where man cannot go often to recharge the batteries which supplies the sensing device, transceiver and memory unit in the sensor node. So the usage of the battery power must be judicious in WSN. The present study demonstrates the data routing method from node to BS (Base Station) using two cluster heads to prolong the battery life of sensor nodes. The two cluster head analogy reduces the overhead of single cluster head, avoids packet collision, and improves reliable data transmission with little compromise in energy consumption and hop count compare to single cluster head method.

In this paper we present a low complexity cooperative diversity clustering protocol for heterogeneous wireless sensor networks. Here we analyze the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are clustered in a co-operative fashion to prolong the effective network lifetime. In contrast to these approaches, we propose HACC, a heterogeneous-aware cooperative clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to its residual energy. We then propose a simple modification in the clustering algorithm to exploit virtual MIMO based cooperative transmission. In place of selecting a single cluster head at network layer, we proposed multiple cluster heads in each cluster to obtain a full diversity gain over long distance communication without sacrificing transmission data rate, and established virtual MIMO based cooperative transmission. We treated the cooperative cluster head selection method as a Facility Location Problem (FLP) to gain better energy utilization. Finally, the simulation results demonstrate that our cooperative clustering approach is more effective in prolonging the time interval before the death of the first node (we refer to as stability period) compared to the one obtained using current clustering protocols, and observe that this proposal can achieve higher order diversity gain with improved spectral efficiency when the distance to Base Station is over a given threshold.

Wireless Sensor Networks (WSN) is an ever growing field owing to their extensive variety of applications. WSN have limited battery energy, consequently, an energy efficient routing protocol is of major concern. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the fundamental clustering protocols in WSN that provides high energy efficiency. Many protocols have been derived from LEACH which mostly address a single inherent challenge. Instead of focusing on a single parameter, combining independent techniques can compound gains in energy efficiency. This paper proposes Yet Another LEACH (YA-LEACH) which uses centralised cluster formation to ensure optimal clusters and allow Cluster Heads (CH) to extend operation into multiple rounds to achieve energy savings. Also, the proposed protocol will have an alternative (vice) CH that takes over when CH residual energy reaches a critically low level. The extended protocol was simulated on NS2 with results showing an improvement in network lifetime and overall data throughput.

Discrete power allocation strategies for amplify- and-forward cooperative networks are proposed based on selective relaying methods. The goal of power allocation is to maximize the network lifetime, which is defined as the duration of time for which the outage probability at the destination can be maintained above a certain level. The discrete power levels enable a low cost implementation and a close integration with high speed digital circuits. We propose three power allocation strategies that take into consideration both the channel state information (CSI) and the residual energy information (REI) at each node. By modeling the residual energy of each node as the states of a Markov Chain, we are able to derive the network lifetime analytically by computing the expected number of transitions to the absorbing states, i.e., the energy states for which the outage probability is no longer achievable. The performance of the three strategies are compared through numerical simulations and a significant improvement in network lifetime is shown, when compared with the case considering only the local CSI.

The sensor network performs gathering, monitoring, and tracking of objects in the given area. The sensor nodes are normally distributed randomly in the network area for collecting the information. The major issues in Wireless Sensor Networks (WSN) are coverage, energy, and limited resources. Sensor Nodes’ (SN) performance depends on so many parameters but normally depends on Residual Energy (RE) and Distance from the base station. The Cluster Head (CH) cooperatively communicates with Base Station (BS) via routing protocols. The proposed Energy Efficient Multilevel Region Based (EEMRB) protocol performs the task by partitioning the entire network area into multiple levels and sub-levels. The sub-levels are partitioned to perform clusters to communicate the sensor via CH (s) using single/multi-hop communication to BS. The proposed protocol is compared with the Stable Election Protocol and shows improvement in network lifetime. Based on the proposed protocol data set, a Multivariate Polynomial Regression (MPR) Model is proposed to predict network lifetime. The model uses packet size and node density as network design parameters. The simulation results show that the size of the packet and network area play a major role in network lifetime. Therefore, the lifetime of the predicted model and EEMRB protocol are close to each other. This prediction model is suitable for the prediction of any network area's lifetime.

Wireless Sensor Networks (WSNs) deployed over Gaussian grids face critical challenges in energy efficiency and network longevity due to constrained node resources and spatial limitations. Traditional cluster head (CH) selection methods, such as LEGN, TEGN, and FOA-CH, individually address aspects of energy optimization, load balancing, and spatial coverage but often fall short in dynamic and heterogeneous network environments. This paper proposes a novel hybrid machine learning (ML) framework that combines features from these existing approaches to enable adaptive, energy-efficient, and fair CH selection. The framework constructs comprehensive feature vectors for each node, capturing residual energy, energy consumption ratio, historical CH frequency, spatial coordinates, and FOA-based optimization metrics. A predictive ML model dynamically selects candidate CHs, followed by localized Firefly Optimization Algorithm (FOA) refinement to improve spatial distribution and load balancing. Performance evaluation demonstrates improvements in network lifetime, average residual energy, load fairness, and data reliability, validating the effectiveness of the proposed approach in enhancing energy utilization and extending operational longevity of Gaussian grid-based WSNs.

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

Clustering is an efficient topology control approach for maximizing the lifetime and scalability of Wireless Sensor Networks (WSNs). Many cluster-based routing techniques have been proposed in the literature. However, in most of the proposed protocols, the communication between a sensor node and its designated cluster head (CH) is assumed to be single-hop. Multi-hop communication can be used when the communication range of the sensor nodes is limited or the number of sensor nodes is very large in a network. Moreover, they used a predetermined percentage of CHs regardless of the network density or the number of live nodes. Clustering is known to be non-deterministic polynomial (NP)-hard problems for a WSN. Particle Swarm Optimization (PSO) is a swarm intelligent approach that can be applied for finding fast and efficient solutions of such problems. In this paper, we propose a novel centralized PSO protocol for Hierarchical Clustering (PSO-HC) in WSNs. Our objective is to maximize the network lifetime by minimizing the number of active CHs and to maximize the network scalability by using two-hop communication between the sensor nodes and their respective CHs. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN was investigated. Extensive simulations show that PSO-HC outperforms the well-known cluster-based sensor network protocols in terms of average consumed energy and throughput.

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.

Energy minimization in sensor nodes is the problem in wireless sensor networks (WSNs). The most widely accepted method to preserve energy of sensor nodes is clustering. In cluster based networks, energy consumption is higher in the nodes which are closer to the sink as compared to the nodes which are located farther away from base station (BS). Various unequal clustering algorithms were proposed in the past to correct this issue. The major drawbacks in the findings are that the nodes, which join a specific cluster head, cause overburdening of the cluster head. In this paper, we present an algorithm namely energy efficient unequal sector clustering (EUSC) using multi-sector and unequal clustering approaches which improve the network’s energy efficiency. EUSC divides the network into multiple sectors based on nodes distances from BS. Selection of cluster heads in each sector is based on nodes distances to BS, residual energy and neighbor nodes proximities in that sector. Selection of a relay cluster head for data transmission is also based on residual energy, nodes distances to BS and queue length of nodes. In this paper, we have carried out some theoretical analysis of the nodes energy consumption in each sector and derived an expression for optimal number of clusters in each sector to minimize nodes energy consumption. Various simulations were carried out with MATLAB package to differentiate the competency of the proposed EUSC algorithm with that of the existing protocols ECHA and ‘PSO based protocol’. Simulation results in different network scenarios indicate that EUSC has given much improved performance than ECHA and ‘PSO based protocol’ in terms of number of clusters formed during each round, network lifetime and energy efficiency.