An Energy Efficient Clustering Approach Using Spectral Graph Theory in Wireless Sensor Networks

For network graphs, numerous graph features are intimately linked to eigenvalues of the Laplacian matrix, such as connectivity and diameter. Thus, it is very important to solve eigenvalues of the Laplacian matrix for graphs. Similarly, for higher-order networks, eigenvalues of combinatorial Laplacian matrices are also important for invariants of graphs. However, for large-scale networks, it is difficult to calculate eigenvalues of the Laplacian matrix directly because it is either very difficult to obtain the whole network structure or requires a lot of computing resources. Therefore, this article makes the following contributions. Firstly, this paper proposes a random walk approach for estimating the bounds of the greatest eigenvalues of Laplacian matrices for large-scale networks. Considering the relationship between the spectral moments of the adjacency matrix and the closed paths in the network, we utilize the relationship between the adjacency matrix and the Laplacian matrix to establish the relationship between the Laplacian matrix and the closed paths. Then, we employ equiprobable random walks to sample the large graph to obtain the small graph. Through algebraic topology knowledge, we obtain the bounds of the largest eigenvalue of the Laplacian matrix of the large graph by using Laplacian spectral moments of the small graph. Secondly, for high-order networks, this paper proposes a method based on random walks and graph transformations. The graph transformation we propose mainly converts graphs with second-order simplices into ordinary weighted graphs, thereby transforming the problem of solving the spectral moments of the second-order combined Laplacian matrix into solving the spectral moments of the adjacency matrix. Then, we use the aforementioned random walk method to solve bounds of the greatest eigenvalue of the second-order combinatorial Laplacian matrix. Finally, by comparing the proposed method with existing algorithms in synthetic and real networks, its accuracy and superiority are demonstrated.

In a wireless sensor network (WSN), there are sets of autonomous sensor nodes distributed spatially that use wireless communication to track and document physical or environmental aspects. The series of sensor nodes (SN) cannot be changed when it is installed in an isolated or unattended region due to their wireless nature. Because of the high energy restrictions of wireless sensor devices, it is crucial to carefully manage extreme energy consumption by malevolent nodes in order to enhance network performance. To overcome this challenge fuzzy based adaptive cat swarm optimization for routing (FAR) has been proposed to decrease latency, increase network lives, and minimize energy consumption by reducing the network's energy consumption. There are two stages to the proposed FAR approach. In the first stage, the choice of the cluster head is made depending on things like energy, distance, and transmission cost using fuzzy logic. In the second stage, an adaptive cat swarm optimization method is used to choose the most efficient route for packet routing to maximize node lifetime and to ensure efficient packet routing. The effectiveness of the proposed FAR strategy has been established using metrics like packet delivery, lifetime, and energy efficiency for evaluation. According to the experimental findings, the suggested FAR model consumes less energy than the current FCEEC (fully connected energy-efficient clustering), HSA-CSO (harmony search algorithm and competitive swarm optimisation), and EECHIGWO (energy-efficient cluster head selection using a grey wolf optimization algorithm) models by 43.4%, 32.5%, and 24.1% respectively.

Clustering is a widely discussed as an important architecture for data centric wireless sensor networks. Low energy adaptive clustering hierarchy (LEACH), proposed in past, is used as benchmark scheme by many proposals and its performance is widely studied in the literature, over last few years. The probabilistic and randomized clusterhead selection strategy results in variable number of clusterheads during different rounds of data gathering. To avoid communication interference in different clusters the signals need to be spread which results in increased data size. The variable number of clusterheads requires spreading factor to be set corresponding to the probable maximum number of clusterheads, to avoid collisions. This increases the data size and correspondingly the amount of energy required to transmit it to the base station. This directly affect the network lifetime and energy consumption of the sensor nodes, which are treated as important performance metrics for evaluation of many of the schemes proposed, in the literature, for data gathering in such networks. To study the effect of spreading factor on these performance metrics, LEACH scheme was simulated in NS-2. With the results obtained 8 is observed as an optimum value of the spreading factor for the simulated scenario, to achieve the desired performance in terms of network lifetime and energy consumption rate which demands development of a distributed clustering scheme with exactly equal number of clusterheads, during different rounds of data gathering.

Wireless sensor network is widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring. Coverage hole detection and recovery is a major problem in WSN. It causes energy holes and increases the number of dead nodes. In this paper, we propose a bi-layered WSN architecture for dynamic clustering based routing and coverage hole detection and recovery. Our proposed work has four steps cluster formation, cluster head (CH) selection, coverage hole detection, and recovery and routing. Clusters are formed by the K-means algorithm. CH is elected by Determined Weight (DW). This DW is calculated by residual energy, distance from cluster and center to the base station. Based on the weight CH is selected. When the size of the cluster which is nearer to the base station is large then we explore cluster maintenance (cluster splitting and merging) for effective clustering by Efficient Entropy Function. After cluster formation, we perform coverage hole detection and recovery for successful packet transmission. Coverage holes occur in different locations. In this paper, we detect the coverage hole in three locations: (1) within the cluster, (2).among the cluster, (3).edge of the network. We use the hole manager (HM) at four different locations, it uses fuzzy logic (energy, stability) to find which node can recover coverage hole. The best multi-hop route is selected for transmission by Multi-objective Emperor Penguin Optimization Algorithm (MO-EPO). We implement our proposed approach for Agriculture Applications in WSN assisted with IoT. Finally, we analyse the performance of our proposed approach with respect to following metrics: Energy consumption, Network lifetime, Number of alive nodes and Packet delivery ratio. Our simulation results are implemented on the NS3.26 simulator.

RETRACTED: Parallel distributed computing based wireless sensor network anomaly data detection in IoT framework

SummaryIn wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.

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.

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.

In wireless sensor networks, power is the most essential resource because each sensor node has limited batteries. Many kinds of existing clustering protocols have been developed to balance and maximize lifetime of the sensor nodes in wireless sensor networks. These protocols select cluster heads periodically, and they considered only `How can we select cluster heads energy-efficiently?' or `What is the best selection of cluster heads?' without considering energy-efficient period of the cluster heads replacement. Unnecessary head selection may dissipate limited battery power of the entire sensor networks. In this paper, we present T-LEACH, which is a threshold-based cluster head replacement scheme for clustering protocols of wireless sensor networks. T-LEACH minimizes the number of cluster head selection by using threshold of residual energy. Reducing the amount of head selection and replacement cost, the lifetime of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that T-LEACH outperformed LEACH in terms of balancing energy consumption and network lifetime.

Energy conservation is a major challenging issue in the field of Wireless Sensor Networks (WSNs) since the sensor nodes are battery operated. Among the various topology control techniques, clustering is best and popular technique which provides both energy conservation and scalability. A cluster based routing protocols consists of mainly three phases: cluster head selection, cluster formation and data communication phase. In this paper, a fuzzy logic based cluster head selection method is proposed. In this method, energy of cluster head, node centrality and distance of node from the base station act as fuzzy descriptors to the Mamdani fuzzy inference system. The proposed Fuzzy Logic based Cluster Head Selection (FLCHS) method helps in balancing the load of nodes and leads to energy conservation. The proposed approach FLCHS is compared with the well known Stable Election Protocol (SEP) for heterogeneous networks. The FLCHS protocol performs better than SEP protocol in terms of network lifetime, energy consumption and throughput.

Wireless Sensor Networks (WSNs) allow new applications and necessitate non traditional routing design paradigms due to several constraints. A long network lifetime requires minimal device complexity and low energy consumption, which requires a proper balance between communication and signal/data processing capabilities. The objective of this paper is to increase the network lifetime while minimizing overall energy. Currently, clustering and routing algorithms are widely used in WSNs to increase the network lifetime. The Butterfly Optimization Algorithm (BOA) is used in this work to select the best cluster head from a set of nodes. The cluster head is chosen based on the nodes residual energy, node degree, node centrality and space to the base station and neighbours. Comparing the existing methodology to current algorithms BOA, BOA exposures that it outperformed LEACH PRO and EECHIGWO in terms of network lifetime, throughput, delay and energy consumption.

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

Reducing energy consumption in IoT networks remains a critical challenge in energy-efficient clustering routing protocols (EECR-IoT). Several routing protocols have been developed to address power consumption, with cluster- based protocols emerging as particularly effective. In these protocols, cluster heads are selected to aggregate data from root nodes and transmit it to the base station, optimizing energy usage. Efficient selection of cluster heads is essential to prolong network lifetime. Our proposed protocol employs static clustering for optimal cluster head selection, ensuring effective performance in both large and small areas. To enhance communication efficiency, we divide large sensor fields into rectangular clusters, which are then grouped into zones to facilitate communication between cluster heads and the base station. EECR-IoT involves a vast network of tiny sensor nodes capable of sensing, processing, and transmitting environmental data to the base station. Energy efficiency is crucial for sustaining these networks, and our study introduces an energy-efficient clustering protocol based on the Divide and Conquer Quad Tree dynamic multi-hop LEACH algorithm. This approach optimizes energy efficiency through balanced cluster creation, thereby distributing the load among cluster heads and extending network lifetime. In addition to Euclidean distance, the protocol considers residual energy for cluster head selection. Multi-hop communication between cluster heads and the base station depends on their distance, further improving energy efficiency. Simulations demonstrate that the proposed method outperforms existing heterogeneous protocols, including LEACH-B, BPK-means, Park’s approach, and Mk-means, by up to 50% in energy savings. The protocol’s effectiveness is evaluated using performance metrics such as throughput, End-to-End delay (EED), Packet Drop Rate (PDR), and node lifespan, showcasing its superiority in enhancing network longevity and reducing energy consumption across various IoT applications.

In many modern situations, wireless sensor networks play a crucial role, used for monitoring things like environmental conditions, transactions, and various statuses. These networks gather a large amount of data, sending it to a central hub for analysis. However, a significant problem is that traditional wireless sensor networks heavily rely on energy, and this limits how long they can operate. This chapter develops an optimization approach for a variable clustering routing protocol to address the issue. The objective is to enhance the cluster structure in wireless sensor networks while reducing energy loss in cluster heads. The first step involves employing a dynamic estimation method for clustering to determine cluster heads, utilizing core concentration to establish the head within the cluster radius. The authors also introduce a fuzzy logic algorithm to handle uncertainties in selecting cluster heads. The residual energy of the cluster's head nodes, maintaining a balanced distribution of cluster heads, and optimizing node use of energy are all taken into account by this fuzzy logic approach. An ant colony algorithm-based technique for optimizing inter-cluster transport is presented in this outline. The primary objective is to optimize energy consumption while simultaneously reducing the data communication overhead among cluster heads. Chaotic mapping is utilized by this technique to update and perturb pheromones, ensuring optimal performance. Energy utilization among cluster heads is optimized by selecting the optimal path based on considerations of energy dispersion parameters and distance coefficients. Our experiments show that compared to traditional algorithms, Researchers provide a non-uniform clustering approach for route optimization that dramatically extends the network lifespan by 75% and lowers overall energy consumption by about 20%. This effectively optimizes network energy utilization and significantly extends the network lifetime, demonstrating the practical effectiveness of our method.

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.