Energy Efficient Clustering Algorithm Research Articles

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization

GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization

Retracted: Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks

Retracted: Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks

Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Improving Efficiency of IOT in Agriculture by using Energy Efficient Clustering Algorithm

Improving Efficiency of IOT in Agriculture by using Energy Efficient Clustering Algorithm

Spread Spectrum based QoS aware Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of small, resource-constrained sensor nodes that form self-organizing, infrastructure-less, and ad-hoc networks. Many energy-efficient protocols have been developed in the network layer to extend the lifetime and scalability of these networks, but they often do not consider the Quality of Service (QoS) requirements of the data flow, such as delay, data rate, reliability, and throughput. In clustering, the probabilistic and randomized approach for cluster head selection can lead to varying numbers of cluster heads in different rounds of data gathering. This paper presents a new algorithm called "Spread Spectrum based QoS aware Energy Efficient Clustering for Wireless sensor Networks" that uses spread spectrum to limit the formation of clusters and optimize the number of cluster heads in WSNs, improving energy efficiency and QoS for diverse data flows. Simulation results show that the proposed algorithm outperforms classical algorithms in terms of energy efficiency and QoS.

Improving WSNs execution using energy-efficient clustering algorithms with consumed energy and lifetime maximization

<span lang="EN-US">Wireless sensor networks (WSNs) has a major designing feature representing by energy. Specifically, the sensor nodes have limited battery energy and are deployed remote from base station (BS); therefore, the actual enhancement dealing with energy turns into the Clustering routing protocols fundamentals which concerned in network lifetime improvement. Though, unexpected and energy insensible of the clusters head (CH) selection is not the best of WSN for greatly lowering lifetime network. A presentation article of an WSNs incoming routing approach using a mix of the fuzzy approach besides hybrid energy-efficient distributing (HEED) algorithm for increasing the lifetime and node’s energy. The FLH-P proposal algorithm is split into two parts. The stable election protocol HEED approach is used to arrange WSNs into clusters. Then, using a combination of fuzzy inference and the low energy adaptive clustering hierarchy (LEACH) algorithm, metrics like residual energy, minimal hops, with node traffic counts are taken into account. A comparison of FLH-P proposal algorithm with LEACH algorithm, fuzzy approach, and HEED utilizing identical guiding standards was used for demonstrating the performance of the suggested technique from where corresponding consumed energy as well as lifetime maximization. The suggested routing strategy considerably increases the network lifetime and transmitted packet throughput, according to simulation findings.</span>

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network’s lifespan. In the proposed system, a hybrid Energy Efficient and Reliable Ant Colony Optimization (ACO) based on the Routing protocol (E-RARP) and game theory-based energy-efficient clustering algorithm (GEC) were used. E-RARP is a new Energy Efficient, and Reliable ACO-based Routing Protocol for Wireless Sensor Networks. The suggested protocol provides communications dependability and high-quality channels of communication to improve energy. For wireless sensor networks, a game theory-based energy-efficient clustering technique (GEC) is used, in which each sensor node is treated as a player on the team. The sensor node can choose beneficial methods for itself, determined by the length of idle playback time in the active phase, and then decide whether or not to rest. The proposed E-RARP-GEC improves the network’s lifetime and data transmission; it also takes a minimum amount of energy compared with the existing algorithms.

Fuzzy Rule-Based Clustering Algorithm for Energy Efficiency in WSN

Abstract: Wireless Sensor Networks (WSNs) have gained much attention in public and research communities due to their incredible capabilities and ever-growing range of applications. The WSN is equipped with a specialized transducer that adds sensing services to IoT. This equipment is limited to battery and resource capacity, which introduces many challenges to academia and industry. Many recent wireless sensor networks (WSN) routing protocols are enhancements to address specific issues with the low-energy adaptive clustering hierarchy (LEACH) protocol. Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm compensates for the energy flow in CH by probabilistically rotating the position between nodes with energy above the specified threshold. The selection of CH in WSN is NP-Hard as the optimal data aggregation with efficient energy efficiency cannot be resolved in polynomial time. Since the performance of LEACH deteriorates sharply with increasing network size, the challenge for new WSN protocols is to extend the network lifespan while maintaining high scalability. This paper introduces a fuzzy logic-based energy-efficient scalable routing algorithm (F-EESRA). The goal of the proposed algorithm is to extend the network lifespan despite an increase in network size. This paper compares F-EESRA against EESRA WSN routing protocol in terms of network performance. EESRA uses multi-hop transmissions for intra-cluster communications to implement the proposed work. The simulation results show that F-EESRA outperforms the fuzzy protocol in terms of energy efficiency on large-scale WSNs.

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

An advancement in energy efficient clustering algorithm using cluster coordinator-based CH election mechanism (CCCH)

A mobile ad hoc network comprises several nodes, and each node can send messages (data) on demand without any established infrastructure. Mobile nodes dynamically enter and exit the network often, resulting in unstable network topology in MANET. As a result, maintaining a stable network becomes a challenging task. Path preservation, battery life, safety, dependability, and unexpected connection characteristics are the major issues in MANET. The network's quality of service (QoS) would be impaired as a result (QoS). The routing protocol is vital for MANETs' pathway detection and maintenance. The multicast routing technology may significantly increase the reliability of the MANET network. Clustering methods, which enable the structuring of networks into groups known as entities or clusters, are invariably employed to address such problems. This paper presents a cluster coordinator-based CH election mechanism (CCCH). The cluster coordinator (CC) is in charge of carrying out the entire operation and is the most important entity in this scenario. The cluster coordinator (CC), cluster head (CH), and cluster members (CM) are the three entities that make up the proposed model. Next to the cluster coordinator, the Cluster Head (CH) is the important serving node in each cluster. Each node in a cluster has a unique value for factors such as node mobility, degree, identity, and energy, among others. With the least amount of time and energy use, the combined proposed architecture successfully constructed reliable routing. A comparison study is done with the existing Optimal Route Selection for Mobile Ad Hoc Networks (ORSMAN) [13] and ticket ID cluster manager protocol (TID-CMGR) [14] to assess the performance of the proposed CCCH. The comparison result proves that the proposed algorithms' performance is far better than the others in all evaluation metrics, such as energy consumption, packet delivery ratio (PDR), and the number of CH changes.

Performance-Aware Green Algorithm for Clustering of Underwater Wireless Sensor Network Based on Optical Signal-to-Noise Ratio

Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.

A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy Efficiency

Wireless sensor networks’ energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node’s time of death has been delayed by 3% in total.

Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks

Wireless networking is popular due to the “3 any” concept: anyone, anytime, anywhere. Wireless communication technology advancements have covered the opportunities for sustainable development of low-power, low-cost, multipurpose sensor nodes in wireless sensor networks. In sensor networks, the network layer handles routing problems. Since radio transmission requires a significant amount of energy, it is essential to investigate power efficiency and optimization. As a result, the conservation of energy is a critical concern in wireless sensor networks. Recent research is focused on developing routing algorithms that use less amount of energy during communication, thereby prolonging the network’s life. Wireless sensor networks with energy recovery nodes use nodes that can extract energy from their environment. The fuzzy-GWO method and the energy-saving routing algorithm are proposed and analyzed in this research work. For simulation, the MATLAB 2021b working environment is used. The LEACH, HEED, MBC, FRLDG protocols, along with the proposed protocol F-GWO, are compared. From the obtained results, it is found that the network lifetime is increased by 20%, 14.8%, 12.5%, and 3.8%, respectively. In addition, the proposed method has a 37.5%, 33.3%, 16.6%, and 6.25% reduction in average energy consumption when compared with the conventional algorithms. According to the experimental data obtained through simulation, the proposed F-GWO algorithm outperforms the LEACH, HEED, MBC, and FRLDG in network lifetime, packet delivery ratio, throughput, bit error rate (BER), buffer occupancy, time analysis, and end-to-end delay.

Uneven clustering and fuzzy logic based energy-efficient wireless sensor networks

Clustering is the fundamental issue in terms of ensuring long-term operation of wireless sensor networks (WSNs). The problem of hot spots remains the most prominent research challenge relating to the design of energy-efficient clustering algorithm. This paper proposed a protocol, namely an uneven clustering and fuzzy logic-based energy-efficient (UCFLEE), for prolonging network lifetime. Depending on the communication distance, the UCFLEE protocol divides the network into uneven clusters for suppressing the hot spot problem. The fuzzy logic selects the optimal cluster head in accordance with certain parameters. The advocated method adopts a dynamic energy threshold to chnage the cluster head. The UCFLEE protocol is dependent on the iterative deepening A (IDA) star algorithm for identifying the routing path from the cluster heads to the base station. The IDA-star method is reliant upon a cost bounded method to select the optimal solution for the base station. The UCFLEE protocol is tested and subsequently contrasted with other protocols. The results obtained from the UCFLEE protocol enable an energy consumption equilibrium, eradicates the hot spot challenge, while also attaining maximum network lifetime.

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.