Energy Efficient Clustering Research Articles

Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

Priority based energy efficient hybrid cluster routing protocol for underwater wireless sensor network

<p class="Affiliation"><span lang="EN-US">A little change in the environment that goes unnoticed in an underwater communication network might lead to calamity. A little alteration in the environment must also be adequately analyzed in order to deal with a potential crisis. A priority-based routing protocol is required to ensure that the vital data perceived by the sensor about the environment changes. The priority-based routing system guarantees that vital data packets are delivered at a quicker pace to the destination or base station for further processing. In this work, we present a priority-based routing protocol based on the energy efficient hybrid cluster routing protocol (EEHRCP) algorithm. The suggested approach keeps two distinct queues for lower and higher priority data packets. In order to ensure that these packets get at their destination without any information loss and at a quicker rate, all of the crucial sensed data is passed through a higher priority queue. Test findings show that the suggested technique increases throughput, delivery percentage, and reduces latency for the crucial data packets.</span></p>

Energy Efficient Clustering and Resource Allocation Strategy for Ultra-Dense Networks: A Machine Learning Framework

The ultra-dense network structure of 5G with dense femto-cells deployment, is identified as a prospective way out for the problem of growing demand for cellular services. However, optimum resource allocation among dense and random deployed femto-cell base stations (FBSs) is a challenging task since there are severe interferences. To overcome these interferences and to obtain efficient resource allocation, a two-stage cluster-based resource allocation scheme has been proposed. In the first step, an efficient dynamic clustering algorithm based on unsupervised learning is proposed which group the FBSs in an optimal number of clusters while balancing the traffic load in each cluster. Afterward, we focus on the green resource allocation problem by using a cooperative methodology. A cloud-based multi-agent reinforcement learning algorithm has been proposed in a decentralized way. The cloud server provides fast-flexible access, large storage space, and turns down operational expenditure. Further, the reinforcement model exploits a brief representation of Q-value to resolve the problem of large stateaction space, diminish the computational complexity and hasten the convergence. The proposed scheme is evaluated in real time experimental setup. The experimental results and the numerical results verify that the proposed scheme significantly improves energy efficiency with a QoS guarantee.

Design of the energy-balanced wireless sensor networks for 3D seismic exploration

Wireless Sensor Networks for 3D Seismic Exploration are large-scale and long-term networks to ensure high resolution. Energy balance is essential to avoid interruption of the whole network. In this paper, the hybrid wireless network architecture is designed for low-power monitoring system. An energy-balanced clustering method is developed to prolong network lifetime. The partial energy factor is introduced to optimize scheduling of cluster head nodes. An improved ant colony algorithm for energy-efficient clustering and routing network (IACA-EECR) is proposed to find optimal path. The results show the proposed architecture outperforms the existing platform. Extensive tests validate energy efficiency and network performance.

Detection of intrusions in clustered vehicle networks using invasive weed optimization using a deep wavelet neural networks

A VANET is a kind of IMS, or intelligent transportation system, that connects moving vehicles. Guaranteeing timely and accurate information sharing between vehicles and infrastructure helps make roads safer and more efficient. Wireless connections are used for data transmission in VANET, making security a critical design challenge. At the same time, the energy constrained ability of the vehicles in VANET creates energy efficiency as a challenging problem. In this aspect, this study presents an effective invasive weed optimization algorithm based energy efficient clustering with deep wavelet neural network based intrusion detection (IWOEEC-DWNN) technique for VANET. The clustered VANET is the target of the IWOEEC-DWNN approach, which seeks to detect any intrusions that may occur. In addition, the IWOEEC-DWNN method builds clusters through an IWO-based cluster technique's optimum cluster head (CH) selection and cluster design. In addition, the DWNN model is employed as a classification model to determine the occurrence of intrusions in the VANET. The design of IWOEEC-DWNN technique considerably helps to accomplish security and energy efficiency. Extensive computations were performed to test the efficacy of the IWOEEC-DWNN method, and the findings showed that it was superior to competing methods on a number of different metrics.

A Review Paper on Energy Efficient Clustering Schemes for WSN Assisted IOT Networks

“Abstract”:WSNs have seen a large improvement in their applications in recent years. WSNs are self-organized, non- wired networks that monitor ambient conditions such as temperature, tension, motion, audio, vibration, or contaminants and transmit data. Because of its sensor-based working standards, the WSN is becoming a common topic among numerous investigators. Clustering is a method used in WSNs to extend network lifecycle & provide more effective operating processes. Clustering is a topology management method that can gather nodes to boost efficiency of the network by controlling resources and distributing duties between many nodes in an adequate way. It divides a sensing field into clusters where cluster's sensor node can be appointed as cluster head. The cluster-dependent routing protocol is a very energy-efficient technique of choosing and relaying information to the CH. Optimized clustering could save a significant amount of energy in the system. This review examines a variety of energy-efficient clustering methods utilized in the wireless sensor area. Keywords: WSN, Energy Efficiency, Clustering, Cluster Head, Clustering protocols.

Energy efficient clustering using modified PROMETHEE-II and AHP approach in wireless sensor networks

Energy efficient clustering using modified PROMETHEE-II and AHP approach in wireless sensor networks

Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods.

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)

Improving Smart Irrigation Systems through Energy Efficient Cluster-Head Selection in Heterogeneous WSNs

Introduction— Smart irrigation systems require reliable and energy-efficient communication between the sensors in a Wireless Sensor Network (WSN) and the control system. Objective— To address this challenge, this paper presents several cluster-based selection protocols, based on the Stable Election Protocol (SEP) and Distributed Energy-Efficient Clustering (DEEC). Methodology— The presented protocols divide the agriculture field into sub-fields to reduce energy consumption between far sensors and the Base Station (BS). Results— Comparison with traditional protocols using evaluation metrics such as network throughput, stability, instability period, and lifetime, shows that the presented protocols outperform in terms of all metrics. Conclusions— The results indicate the effectiveness of the proposed protocols in prolonging the network’s lifetime and improving energy efficiency in heterogeneous WSNs, thus supporting the performance of smart irrigation systems. Numerically, using the proposed protocols, the network lifetime increased by 23% compared to conventional SEP.

A Novel Energy Efficient Cluster Based Routing in Wireless Sensor Network

Objective: Network lifetime of WSN can be extended through assigning nodes with higher energy levels as cluster heads. Cluster heads selection improves the packet transmission rate and conserves energy by reducing re-transmission. The proposed algorithm assigns nodes with high energy as clusters heads and finds optimal path which improves network coverage by avoiding dead nodes. Methods: To improve network lifetime and conserve node energy, a novel approach which involves optimum cluster head selection using Firefly algorithm (FF) and Glow Swarm Optimization (GSO) algorithm for routing in WSN (FFGSO) was developed. The cluster head selection is designed to be multi-objective function which meant for not only minimizing the energy consumption but also to minimize the distance and to improve coverage. Findings: The proposed model reduces energy consumption to about 16.21% which avoids dead nodes effectively and finds the shortest path to reach sink which eventually improves network coverage and improves the throughput by 9.52%. Novelty: To overcome the limitations of dead nodes and to improve the network lifetime, the proposed method introduces cluster head selection as a multi-objective function. To improve energy consumption, network lifetime and network coverage and to overcome the limitations of PSO-GSO, Leach and FF-PSO cluster head selection is designed as multi-objective function. The proposed method improves network stability and network lifetime through reducing energy consumption and finding the shortest path. Keywords: Wireless Sensor Network; Cluster head selection; Firefly algorithm (FF); Glow Swarm Optimization (GSO); Energy efficient clustering

GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs

GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs

Fault Tolerance and Energy Efficient Multi-Hop Clustering with Dual Base Stations in Large Scale Wireless Sensor Network

Fault Tolerance and Energy Efficient Multi-Hop Clustering with Dual Base Stations in Large Scale Wireless Sensor Network

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Energy efficient and multi-hop routing for constrained wireless sensor networks

Wireless sensor network (WSN) technique has advanced in several applications, however, the environment has significant energy and security challenges. Therefore, this research proposes a Multi-Hop, Trivial, as well as Protected Routing Solution for Constrained Wireless Sensor Networks. The Chaotic Particle-swarm Krill Herd (CPKH) method, which includes chaotic theory with accelerated PSO (APSO) as a mutation operator into the krill herd method, is used to first choose the energy-efficient cluster heads. Then, to select the optimal path this research proposed a novel Self-Adaptive Step Glow Worm Swarm Optimization (SASGWS) Algorithm. Following that, the Hamming Residue Technique (HRT) is utilized to counteract the malicious attacks in the WSN. The proposed technology, which could result in increased power efficiency as well as high bandwidth, analyses system status on demand as well as dynamically identifies system hazards.

Energy Efficient Multi-Hop Multipath Sub Clustering Routing Protocol for Wireless Sensor Network

The aim of this paper is to design Energy Efficient Multi-Hop Multipath Sub Clustering Routing Protocol for Wireless Sensor Networks. This paper presented a routing protocol for heterogenous WSN for the selection of cluster head based on the highest remaining residual energy in a node by using the optimal probabilistic equation. The heterogenous technique contains the multi-hoping of inter clustering communication and connectivity of remaining sensor nodes by using sub clustering technique. The sensor nodes with high residual energy will become cluster head and sends data packets to the base station. After successive simulations of this routing protocol on to MATLAB 2022b software, it was consequently found out that it improves the number of alive nodes, number of data packets transmitted to the sink in a network and also improves the network lifetime by 75%-80% in comparison with other protocols. The simulation results were carried out by comparing the proposed method with O LEACH, EEE LEACH, Z-SEP, LEACH. The result shows that, our proposed method is more stable and energy efficient than the other routing protocols.

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).

Performance Analysis by Improving Energy Efficiency in IoT Based Wireless Sensor Network for Routing Algorithm

Objectives: To improve the energy efficiency in IoT-based wireless sensor networks. To increase the sensor node lifetime in any wireless sensor network (WSN) based on the Energy-efficient cluster and routing in WSN. The advancement in the electronic industry has to develop the low powerconsuming and low-cost devices for multiple purposes. Methods: A new algorithm is proposed to improve the energy efficiency in IoT-based WSN for routing algorithms. The low power consuming devices are wisely chosen for the respected application and their power management. This study is intended to provide efficient routing algorithms with less energy consumption in wireless sensor networks. Findings: A wireless sensor network deployed with a number of non-static nodes in a communication network are beneficial for numerous fields such as the military, medical sector, structural health monitoring, transportation industries, etc. In this paper, the performance parameters like average throughput, average end-to-end delay, packet delivery ratio, average node energy, and normalized routing load are analyzed. Novelty: We designed and simulated the Distributed IoT-based wireless network system to analyze the information of nodes roaming in the vicinity of the zone area. In proposed technique energy efficiency is increased compared to the conventional algorithm. In node centric routing protocol, energy efficiency is increased from 55% to 68%. In data centric routing protocol, energy efficiency is increased from 65% to 75%. In source-initiated routing protocol, energy efficiency is increased from 68% to 80%. In destination-initiated routing protocol, energy efficiency is increased from 72% to 85%. Keywords: Ad Hoc OnDemand stance Vector (AODV); DestinationSequenced DistanceVector Routing (DSDV); Dynamic source routing (DSR); MQ Telemetry Transport (MQTT); Quality of Services (QoS); Wireless Sensor Network (WSN)

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT

In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.