Energy Efficient Clustering Protocol Research Articles

Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN.

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A COMPREHENSIVE ANALYSIS OF ENERGY EFFICIENT WIRELESS SENSOR NETWORK (WSN) PROTOCOLS

Wireless Sensor Networks (WSNs) include many tiny and low energy and cheaper nodes in different areas for measuring the environmental parameters like temperature and humidity. Hence, the usage of WSNs has become an adaptable means of data transfer across different fields due to the advancement of wireless technology. It is noted that these networks,which use nodes for event detection and identification, gather information by means of simple calculations and send it to a particular Base Station (BS) via gateway nodes. The applications of WSNs can be classified into monitoring and tracking,which are used in areas such as environmental monitoring, health diagnostics, military surveillance, and many others.nergy consumption is still a major issue of concern, sensor nodes in particular, use energy in sensing data and transmitting it. Energy savings techniques like clustering and routing protocols are important for increasing the life time of the network.hus, this research aims at analysing the different aspects of improving energy efficiency and network performance,especially in the context of the clustering algorithms, such as the Multi-level Stable and Energy Efficient Clustering (MSEEC) protocol. The paper discusses major issues and suggests some new combined routing approaches for WSNs with the problems connected with fault tolerance, topology, scalability, and power consumption.

Energy efficient clustering and sink mobility protocol using Improved Dingo and Boosted Beluga Whale Optimization Algorithm for extending network lifetime in WSNs

In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In this context, swarm intelligence-based algorithms are identified to be suitable and ideal for determining near-optimal and optimal solutions in the search space. On the other hand, APTEEN routing protocol possesses the issues that are related to unnecessary energy drain, ineffective overall network coverage and premature death of certain nodes. To address these issues, an attempt to optimize the APTEEN routing protocol using Dingo Optimization Algorithm (DOA) and Beluga Whale Optimization Algorithm (BWOA) is made in this proposed clustering protocol. With this motivation, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimized the energy consumption and maximize the lifetime of the network. It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination. It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime. The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation.

Enhanced centroid-based energy-efficient clustering routing protocol for serverless based wireless sensor networks

Serverless computing is a new concept as cloud computing, which dynamically manages the networks and is applied in Serverless Wireless Sensor Networks (SWSN) to help the networks. These networks are becoming famous for monitoring various physical and environmental factors. Serverless computing also facilitates the networks by offering an extensive range of applications. Different applications have been designed for monitoring purposes where the sensor nodes sense the data and transmit it to the base station through single or multi-hop routing. However, existing routing protocols cannot manage the sensor nodes’ energy issues because of the complex routing processes and depleted their power before their time. Because of these limitations, the nodes close to BS continuously rely on the network for data forwarding. As a result, these nodes cause energy consumption and lead to a useless state. This paper proposes a serverless architecture and designs an Enhanced Centroid-based Energy Efficient Clustering (ECEEC) protocol for SWSN networks. The proposed serverless architecture provides automated scalability, cost-effective services, and stateless execution. In addition, the proposed protocol offers the cluster head selection and its rotation to maximize the energy efficiency in the network. Furthermore, gateway nodes are chosen in every cluster to overcome the load on the cluster head. Simulation results indicated the excellent performance of the proposed protocol as compared to the existing routing protocols concerning network lifetime and energy consumption. The proposed protocol shows better reliability with nodes failing at 650 rounds compared to 600 rounds, especially with 5 % and 10 % Cluster Heads. The proposed protocol exhibits superior energy efficiency consumption of SNs under varying CH percentages, indicating the protocol’s consistent performance across different scenarios.

Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs

Energy efficient clustering protocol using hybrid bald eagle search optimization algorithm for improving network longevity in WSNs

Distance-Distributed Energy Efficient Clustering (D-DEEC) Routing Protocol for Wireless Sensor Network

A wireless sensor network (WSN) composed of many tiny devices that rely on energy efficient routing protocols for extension of their lifetime. Many cluster-based routing protocols have been proposed based on heterogeneity in recent times. Indeed, these protocols are aiming at achieving energy efficiency, throughputs and better lifetime of the networks. However, two important factors that could have helped some of these protocols to achieve the above-mentioned aims are really missing. Factors such as the distance and average distance between nodes and the Base Station (BS) in selecting a cluster head needed to be considered. These were the major challenges that were identified in the Distributed Energy Efficient Clustering (DEEC) protocol after careful study. As a result, the throughputs and the lifetime of the scheme were affected. In this paper, a reviewed hierarchy-based heterogeneous routing protocol called Distance-DEEC (D-DEEC) is proposed to enhance the DEEC protocol. The new algorithm took into account the residual energy, distance of the individual nodes and average distance of all the nodes from the BS in selecting the Cluster Heads (CHs). This has allowed the protocol to select a cluster head that has high residual energy, is closer to the BS and at the same time not too far from its neighbours. The scheme also employed the sleep and awake approach to reduce energy dissipation. The technique allows the Base BS to calculate the maximum energy of distant nodes and determines when such nodes can transmit their report based on a given threshold energy, Eth . The performance of the proposed algorithm was evaluated using MatLab R2018a and the outcomes showed that, D-DEEC protocol outperformed TDEEC in terms of energy consumption, throughputs and the network lifetime.

A Detailed Review and Comparative Analysis of Various Energyefficient Clustering Protocols in Wireless Sensor Networks

A Detailed Review and Comparative Analysis of Various Energyefficient Clustering Protocols in Wireless Sensor Networks

Modified glowworm swarm optimisation-based cluster head selection and enhanced energy-efficient clustering protocol for IoT-WSN

Modified glowworm swarm optimisation-based cluster head selection and enhanced energy-efficient clustering protocol for IoT-WSN

Retracted: EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

Retracted: EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

Energy-Efficient Improved Optimal K-Means: Dynamic Cluster Head Selection based on Delaying the First Node Death in MWSN-IoT

The Internet of Things (IoT), which attaches dynamic devices that can access the Internet to create a smart environment, is a tempting research area. IoT-based mobile wireless sensor networks (WSN-IoT) are one of the major databases from which the IoT collects data for analysis and interpretation. However, one of the critical constraints is network lifetime. Routing-based clustered protocols and cluster head (CH) selection are crucial in load balancing and sensor longevity. Yet, with clustering, sensor node mobility requires more overhead because the nodes close to the center may get far and thus become unsuitable to be a CH, whereas those far from the center may get close and become good CH candidates, influencing energy consumption. This paper suggests an energy-efficient clustering protocol with a dynamic cluster head selection considering the distance to the cluster center, remaining energy, and each node's mobility degree implementing a rotation mechanism that allows cluster members to be equally elected while prioritizing those with the minimum weight. The advised algorithm aims to delay the first node death (FND) and thus prolong the stability period and minimize energy consumption by avoiding re-clustering. The performance of the proposed protocol exceeds that achieved in our previous work, Improved OKmeans, by 11%, in first dead node lifetime maximization, 43% in throughput, and 44% in energy efficiency.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

Energy-efficient clustering and cooperative routing protocol for wireless body area networks (WBAN)

Energy-efficient clustering and cooperative routing protocol for wireless body area networks (WBAN)

A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks

Energy saving is one of the hot research topics in wireless sensor networks (WSNs), and a number of solutions have been proposed. Clustering protocols have become the widely used method to reduce energy consumption. However, the energy efficiency in existing clustering protocols is limited because of the excessive energy consumption in periodic reclustering. In this article, we propose a centralized energy-efficient clustering (CEEC) protocol, which includes two aspects of node energy balancing schemes, to enhance energy efficiency in WSNs. As for intracluster energy balancing, we employ the cluster head (CH)-rotation approach to balance the energy load among nodes in each cluster. With respect to intercluster energy balancing, we suggest four sequential algorithms to balance the energy consumption among clusters, and the main contributions are as follows: 1) a size-balanced cluster formation (SBCF) algorithm aims to partition the whole network into several small clusters of the same size; 2) a low-cost forecast-based maintenance mechanism is raised to help the base station (BS) maintain the total nodes’ energy information of each cluster in the network, and then, BS utilizes our introduced intercluster energy adjustment (ICEA) algorithm to reallocate a few cluster members in related clusters with low overhead; and 3) an energy-aware data forwarding (EDF) scheme is to maintain the nodes’ energy balance among clusters in intercluster communication. Simulation results show that our CEEC protocol outperforms the classical clustering mechanisms in terms of network lifetime, energy consumption, and network throughput.

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

Recent research predicts many internet-connected technologies, including mobile phones and devices that collect data and operate in exterior settings. WSNs provide flexibility, reliability, and intelligence to the internet of things (IoT). WSNs often use sensors with limited power and memory. The biggest issue in these systems is the high energy depletion during data transmission from the origin node to the destination nodes, which reduces node longevity. Because node batteries cannot be replaced, WSN-IoT energy usage is a big design challenge. Thus, WSN-IoT clustering protocol (CP) improves energy efficiency and network lifetime. Many researchers have created energy-efficient CPs (EECPs) for WSN-IoT energy conservation. Such protocols cluster nodes by residual energy, distance, etc. to maximise energy production. This study covers various WSN-IoT EECPs. It also tabulates protocol benefits, drawbacks, and simulation efficiency. Several protocols were simulated to evaluate network longevity, residual energy, throughput, packet delivery ratio (PDR), and stability. Finally, WSN-IoT systems can be made more energy efficient and last longer.

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

A comparative study of energy-efficient clustering protocols for WSN-internet-of-things

FEEC: fuzzy based energy efficient clustering protocol for WSN

FEEC: fuzzy based energy efficient clustering protocol for WSN

Modified distributed energy efficient clustering protocol for lifetime maximization in wireless sensor networks

A wireless sensor network is a combination of tiny sensing nodes with minimal energy capabilities. The sensors are installed across field to monitor parameters such as noise, temperature etc for long time. WSN has wide range of applications such as military, health care industry etc. It has advantages such as easy deploying, lower cost and fault tolerance. Since the energy capabilities are minimal, we can take some measures to improve network capabilities. To overcome the issue, we need to concentrate on the performance of the routing protocol while designing the architecture. The aim of WSN is to transfer data by increasing the lifetime of the network, throughput, and number of Cluster Heads (CH). We take DEEC protocol in comparison with the proposed design of M-DEEC which helps in minimizing the energy consumption of the nodes in network and maximize the network lifetime. In M-DEEC is a modified energy distribution algorithm which uses energy aware adaptive clustering protocol in heterogeneous WSN. Each node will carry over a remaining energy over the network which can be further used as reference to increase the lifetime.

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

In recent years, Internet of Things (IoT)-based constrained wireless sensor networks (WSN) have been observed with radical developments. The aligned phenomenon such as maximizing resource utilization and the delivery of efficient services is the need of the hour to suffice for the purpose of seamless connectivity. Typically, IoT devices have limited battery life and are deployed in remote areas. As a result, limited battery life limits the use of networks in some situations. To develop energy-efficient techniques for the IoT requires innovative ideas where the current techniques of using WSNs cannot directly apply to the IoT due to protocol issues, SLAs (service level agreements), scalability issues, and complexity levels. To overcome these issues, this article proposes an Enhanced Multitier Energy-Efficient Clustering Protocol integrated with the Internet of Things (EMEECP-IOT) based on the heterogeneous wireless sensor networks (HWSN) technique. The proposed approach has been compared with the current and standard protocols with respect to the parameters such as network stability, packets received, average throughput, standard deviation (SD), and residual energy. The simulation results show significantly better performance of the proposed EMEECP-IoT-based HWSN technique than the existing technique. Extensive performance factors show that the proposed EMEECP-IOT extends the network lifetime by 35% and decreases the network’s energy consumption by 21%. This proposed approach can be applied to save energy and enhance network lifetime for WSN and IoT-oriented applications.

An Energy Efficient Clustering Protocol Using CM-YSGA Optimization Approach in WSN

Abstract: While dealing with the wireless sensor networks (WSNs), one of the greatest factors that must be taken into consideration is energy consumption of nodes. One of the most effective common way of preserving energy in sensor nodes is clustering technique in which CH selection is of great importance. In this manuscript, an improved energy efficient clustering protocol is proposed in which chaotic mapping algorithm is clubbed along with the advanced variant of Yellow Saddle Goatfish Algorithm (YSGA). The main objective of the proposed model is to reduce the energy consumption of nodes which ultimately prolongs the lifespan of wireless network. Initially, cluster are formed in the network by selecting the nodes randomly. After this, four important parameters which included, residual energy, distance to sink, average distance to CH neighbor node and delay are taken into consideration and on the basis of these factors’ fitness value is calculated by the chaotic-YSGA technique. The node whose fitness value came out to be highest of all other nodes, is selected as the CH in that particular cluster. In addition, the Huffman data compression technique is used in the proposed system to reduce energy consumption in nodes. Finally, the performance of the proposed Chaotic-YSGA model is analyzed and compared with conventional energy efficient models in MATLAB software in terms of alive node, dead nodes, throughput, residual energy, FND, HND and LND. Keywords: WSN, Energy Efficiency, CH selection, Optimization Algorithms.