Energy Efficient Clustering Research Articles

Energy-efficient clustering and path planning for UAV-assisted D2D cellular networks

Energy-efficient clustering and path planning for UAV-assisted D2D cellular networks

Quadratic Opposition Learning with Water Strider Algorithm Based Energy Efficient Clustering and Routing for Wireless Sensor Network

Quadratic Opposition Learning with Water Strider Algorithm Based Energy Efficient Clustering and Routing for Wireless Sensor Network

An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance. Through mimicking the natural scavenging behavior of African vultures, AVOACS continuously balances energy consumption on nodes resulting in an increase in network stability and lifetime. For CH selection, we use AVOACS, which considers the following parameters: communication mode decider, the distance between sink and node, residual energy, and intra-cluster distance. In comparison to the OE2-LB protocol, simulation findings demonstrate that AVOACS enhances stability, network lifetime, and throughput by 21.5%, 31.4%, and 16.9%, respectively. The results show that AVOACS is an effective clustering algorithm for energy-efficient operation in heterogeneous WSN environments as it contributes to a large increase of network lifetime and significant enhancement of performance.

Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks

Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks

Energy-efficient clustering in wireless sensor networks using metaheuristic algorithms

Energy management in Wireless Sensor Networks (WSNs) remains a critical challenge, particularly in clustering processes. This article compares three optimization algorithms—Grasshopper Optimization Algorithm (GOA), Bat Algorithm (BA), and Whale Optimization Algorithm (WOA)—to achieve energy-efficient clustering and extend network lifetime. Initial cluster head placement is performed using K-means clustering, and a novel cost function is introduced that considers energy consumption and node distribution, enhancing the network’s efficiency and resilience. The algorithms are evaluated across three scenarios with varying base station (BS) placements. In the simplest scenario, with the BS centrally located, GOA slightly outperforms WOA in extending network lifetime, although WOA remains competitive. BA, while energy-efficient, lags behind GOA and WOA. As complexity increases with BS placement at the edge, WOA demonstrates superior energy management, delaying node death and extending network lifetime more effectively than GOA and BA. In the most challenging scenario, where the BS is placed in a remote corner, WOA emerges as the most effective algorithm, maintaining network performance and balancing energy consumption for the longest duration. GOA, while relatively strong, shows faster network lifetime decline, particularly in later stages, whereas BA faces significant challenges, leading to quicker node failures. Overall, this study highlights the importance of efficient clustering and optimization for prolonging WSN lifetimes. WOA excels in complex scenarios, while GOA leads in simpler environments. Integrating K-means clustering with the novel cost function enhances algorithm performance, contributing to the development of resource-efficient WSNs, especially in resource-constrained settings.

Secure Cluster Based Routing Scheme for Neuro‐Fuzzy Assisted MANET in 5G

ABSTRACTThe Fifth Generation (5G) of wireless networks has increasingly focused significantly on energy‐saving methods for mobile communication systems. The objective of combining MANET with 5G communication is to achieve a high data throughout while simultaneously reducing latency and cost. The main issues with MANET are elevated energy use, security, and reduced longevity. The self‐configuring characteristics and dynamic architecture of MANET may lead to frequent changes in topology and resource allocation. This mobility results in frequent connection failures, which increase routing overheads and decrease data transmission dependability. A secured clustering technique is essential to enhance the routing performance and establish reliable routing. In MANET, the cluster mechanism is the prominent way to improve the network lifetime and routing mechanism. The proposed secure cluster routing‐based neuro‐fuzzy logic (SCR‐NFL) establishes a secure cluster mechanism via the use of the Trust value method. It guarantees cluster stability by taking into account node mobility and energy while selecting cluster heads. The important components in the proposed system are Mobility (M), Residual Energy Level (REL), and Trust Value (TV). The neural fuzzy logic system is implemented to handle the data uncertainty and make effective decisions during routing path selections. The proposed SCR‐NFL performance is measured in an NS‐2 simulation environment. The result is compared with the existing Improved Particle Swarm Optimization Algorithm, Trust‐Based Secure Neuro Fuzzy Clustering mechanism and Mobility Aware Energy Efficient Clustering system using Particle Swarm Optimization mechanism. The performance parameters for comparison are packet delivery ratio, average number of clusters, end‐to‐end delay, average number of cluster instances, mobility speed and mobility distance.

A Novel Topology-Scale-Adaptive and Energy-Efficient Clustering Scheme for Energy Sustainable Large-Scale SWIPT-Enabled WSNs

A Novel Topology-Scale-Adaptive and Energy-Efficient Clustering Scheme for Energy Sustainable Large-Scale SWIPT-Enabled WSNs

Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system

Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system

Archimedes Chicken Swarm Optimization‐Based Routing for Internet of Underwater Wireless Sensor Networks

ABSTRACTUnderwater Wireless Sensor Networks (UWSNs) are employed in various applications ranging from observing the underwater environment to military applications, which leads to attaining interest for many investigators in the UWSNs area. They experience more problematic issues because they are deployed underwater. Some of the common difficulties involve the assessment of battery, installation, energy consumption, and long delay. To overcome this bridge, an effectual scheme is developed for the internet of UWSNs named Archimedes Chicken Swarm Optimization (ACSO). Here, ACSO is obtained by the fusion of Archimedes Optimization Algorithm (AOA) and Chicken Swarm Optimization (CSO). The underwater sensor network IoT module (IoUT) nodes are simulated and the energy model and mobility model are considered for the following progress. The energy prediction is achieved by employing a Deep Recurrent Neural Network (DRNN), which is trained by the proposed ACSO. The Cluster Head (CH) selection is performed utilizing the Energy Efficient Clustering Protocol. Lastly, the routing is carried out by the ACSO algorithm, where the best route is determined based on various parameters namely, energy, distance, delay, throughput, and Packet Delivery Ratio (PDR).

Secured and energy efficient cluster based routing in WSN via hybrid optimization model, TICOA

There are two main design issues in Wireless Sensor Network (WSN) routing including energy optimization and security provision. Due to the energy limitations of wireless sensor devices, the problem of high usage of energy must be properly addressed to enhance the network efficiency. Several research works have been addressed to solve the routing issue in WSN with security concerns and network life time enhancement. However, the network overhead and routing traffic are some of the obstacles still not tackled by the existing models. Hence, to enhance the routing performance, a new cluster-based routing model is introduced in this work that includes two phases like Cluster Head (CH) selection and Routing. In the first phase, the hybrid optimization model, Tasmanian Integrated Coot Optimization Algorithm (TICOA) is proposed for selecting the optimal CH under the consideration of constraints like security, Energy, Trust, Delay and Distance. Subsequently, the routing process takes place under the constraints of Trust and Link Quality that ensures the enhancement of the network lifetime of WSN. Finally, simulation results show the performance of the proposed work on cluster-based routing in terms of different performance measures. The conventional systems received lower trust ratings, specifically BOA=0.489, BSA=0.475, GA=0.493, TDO=0.418, COOT=0.439, TSGWO=0.427, and P-WWO=0.408, whereas the trust value of the TICOA technique is 0.683.

Energy-efficient clustering and routing using fuzzy k-medoids and adaptive ranking-based wireless sensor network

<p>The wireless sensor network (WSN) is a vital component of infrastructure that is seeing tremendous demand and quick expansion in a variety of industries, including forestry, airports, healthcare, and the military. Increasing network lifetime and reducing power consumption (PC) are now major goals in WSN research. This research proposes a unique energy-efficient cross-layer WSN design that aims to maximize network lifetime while maintaining quality of service (QoS) criteria to address these challenges. The research initially utilizes the fuzzy k-medoids (FKMeds) clustering technique to group sensor nodes (SN) to improve resilience, scalability, and minimize network traffic. Following that, the hybrid improved grey wolf and ant colony (HIGWAC) optimization approach is applied to choose cluster heads (CH), minimizing distances, reducing latency, and optimizing energy stability. Finally, data is transmitted through the shortest pathways using the adaptive ranking-based energy-efficient opportunistic routing (ARanEOR) protocol, which ensures effective and energy-conserving routing in WSN while dynamically lowering network overhead. Compared to existing approaches, the proposed method in this study outperforms them in terms of energy efficiency, latency, and network longevity.</p>

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

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.

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Large-scale molecular dynamics simulation and aggregate behavior research on asphalt

In this comprehensive study, a molecular dynamics (MD) model consisting of approximately 150,000 atoms was developed within the Strategic Highway Research Program (SHRP) framework to investigate the behavior of AAA asphalt, focusing on asphaltene aggregate dynamics behavior over a simulation time span of 0.282 microseconds. The findings reveal that the model achieves a stable state in terms of both energy and microstructure without significant changes or the formation of densely clustered molecules, despite the extensive simulation period. Notably, the patterns of asphaltene aggregation were found to significantly influence the MD system energy, with variations ranging from loosely organized structures due to dispersed asphaltenes to more structured, energy-efficient clusters formed by encapsulated asphaltene pentamers. The size of asphaltene aggregates emerged as a pivotal factor, affecting their encapsulation and leading to distinct formation patterns. Furthermore, the research highlighted that increasing shear rates prompt the depolymerization of asphaltene aggregates, shifting from large-scale structures to smaller ones and adapting dynamically to the flow conditions by aligning parallel to the shear direction. This study underscores the critical impact of asphaltene aggregate behavior and shear rates on the structural integrity and distribution within the asphalt model, offering profound insights into the MD of asphalt materials.

BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

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.

Performance Comparison of CC AODV and Optimized AODV K-Means Clustering Using NS3

Nowadays, many studies have been interested in Ad Hoc Wireless Networks because of their low cost, when there is a need to communicate using a network without any infrastructure. Ad Hoc On-Demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANET) and other wireless ad hoc networks. AODV was co-developed in July 2003 at the NOKIA Research Center, University of California, Santa Barbara and University of Cincinnati by C. Perkins, E. Belding-Rover an S.Das. AODV is a routing protocol used by ZigBee - a low power wireless ad hoc network with low data rates. AODV is included in the proactive routing protocol. The advantage of using this routing protocol is that it has economical resources, because resources are not needed to store information about routes to other nodes. However, this AODV has a drawback, namely the packet that will be sent has a long delay in anti-node origin waiting for the protocol to find the path to be passed. There are many papers that optimize AODV, including CC AODV and K Means Clustering AODV. Based on the paper “CC-AODV: An effective multiple paths congestion control AODV”, a control scheme called CC AODV has been created, namely managing routing conditions by significantly increasing packet sending rates while reducing packet drop rates, and based on the paper “An Energy Efficient Clustering Using K-Means and AODV Routing Protocol in Ad hoc Networks”. Based on both paper, in this study the objectives to be achieved are: a) Analyze the performance of using CC AODV and K Means Clustering AODV based on the number of nodes. b) how much influence the number of nodes has on the performance of CC AODV and K Means clustering AODV c) Performance analysis is performed on throughput, end to end delay, packet loss, packet delivery ratio. This paper begins by looking at the literature covering MANET, AODV, CC AODV, and K-Means Clustering AODV. On the result section the test results such as end to end delay, throughput, packet delivery ratio, and packet loss ratio are documented. The conclusion summarizes key points and is followed by the references.