Energy Aware Clustering Research Articles

Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks

Routing in Wireless Sensor Networks (WSNs) is the most significant and the challenging issue for the researchers in terms of enhancing its performance in terms of network lifetime, energy efficiency, scalability, connectivity, throughput, etc. They have the incredible capability to interact and gather data from any physical environment with help of routing protocols. Many routing protocols based solutions have been proposed in the recent years for accomplishing the preferred level of performance in WSNs for these issues. The hierarchical heterogeneous cluster based energy efficient routing protocols are more efficient as compared to flat and location based routing protocols due to the presence of nodes heterogeneity in terms of energy level of sensor nodes which enhances the lifetime of the network. The most recent trend that extensively enhances the functionality and the performance of WSNs is the use of mobile sensor nodes. In this paper, the authors proposed a novel concept regarding mobile sensor nodes is proposed called Mobile Energy Aware Cluster Based Multi-hop (MEACBM) routing protocol for hierarchical heterogeneous WSNs which selects CHs on the basis of newly proposed probability equation which selects only that sensor node as Cluster Head (CH) which has the highest energy among other sensor nodes by introducing a new term S(i).E in the equation. It considers hierarchical heterogeneous clustering considering three levels of sensor nodes; multi-hoping for inter-cluster communication and connectivity of sensor nodes within the whole network area. In MEACBM, after the deployment of sensor nodes and formation of clusters, the whole network area is divided into sectors and inside each sector a mobile sensor node is placed which act as Mobile Data Collector (MDC) for collecting data from CHs. This technique helps in significantly reducing the energy consumption of sensor nodes for transferring information to the Base Station (BS). Simulation performance based results indicates the effectiveness of MEACBM routing protocols by comparing it with other contemporary cluster based routing protocols in terms of network lifetime, stability, throughput, number of CHs and number of dead nodes.

Residual Energy and Time Aware Cluster Based Data Gathering in Wireless Sensor Network

Residual Energy and Time Aware Cluster Based Data Gathering in Wireless Sensor Network

Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT

Wireless Sensor Networks (WSNs) are used in the design of Internet of Things (IoT) for sensing the environment, collecting the data and to send them to the base station and the locations used for analysis. In WSNs for IoT, intelligent routing is an important phenomena that is necessary to enhance the Quality of Service (QoS) in the network. Moreover, the energy required for communication in the IoT based sensor networks is an important challenge to avoid immense packet loss or packet drop, fast energy depletion and unfairness across the network leading to reduction in node performance and increase in delay with respect to packet delivery. Hence, there is an extreme need to check energy usage by the nodes in order to enhance the overall network performance through the application of intelligent machine learning techniques for making effective routing decisions. Many approaches are already available in the literature on energy efficient routing for WSNs. However, they must be enhanced to suite the WSN in IoT environment. Therefore, a new Neuro-Fuzzy Rule Based Cluster Formation and Routing Protocol for performing efficient routing in IoT based WSNs. From the experiments conducted in this research work using the proposed model, it is proved that the proposed routing algorithm provided better network performance in terms of the metrics namely energy utilization, packet delivery ratio, delay and network lifetime.

Energy-aware Cluster Head Selection in WSN using HPSOCS Algorithm

Energy-aware Cluster Head Selection in WSN using HPSOCS Algorithm

Firefly algorithm inspired energy aware clustering protocol for wireless sensor network

Most of the existing cluster-based routing protocols focus either on cluster head election schemes or on refinement of routing process ignoring process of cluster formation. In this paper, firefly algorithm inspired energy aware clustering protocol for WSN is presented. The idea of the proposed protocol is to elect the cluster head nodes by assigning them intensity value defined through firefly algorithm. Another contribution of this paper is energy efficient cluster formation where cluster members join to cluster heads based on intensity parameter associated with cluster head. The presented protocol ensures that the elected cluster heads are usually located in densely populated area of the network resulting in low distance intra-cluster data transmissions and ultimately leading to low energy consumption. The firefly-based protocol is compared with popular routing protocols such as LEACH, LEACH-C and EOICHD and comparative results indicate that proposed protocol is highly energy efficient than other protocols.

Swarm dynamics for enhanced energy aware clustering

Energy can be efficiently conserved in WSN through clustering of nodes. As in all shared-medium networks, medium access control (MAC) protocol enables the smooth functioning of the network. An important function of MAC is to prevent the bottle-neck between two nodes sending data simultaneously. Many MAC protocols have been developed for smooth functioning of WSN which includes Berkeley medium access control (BMAC) which utilises minimal power listening as well as a proper preamble for minimal power communication. The main challenge of BMAC is overhearing and power wasting in long preambles. The aim of this work is to cluster BMA protocol using heuristic methods based on river formation dynamics (RFD) and particle swarm optimisation (PSO). The suggested protocol's performance is evaluated for packet delivery ratio (PDR), end to end delay, hop as well as jitter. The outcome shows that the proposed river PSO cluster BMAC performs better than BMAC with flooding and BMAC with cluster-based routing when compared with static and varying node mobility.

BICSF: Bio-Inspired Clustering Scheme for FANETs

Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and krill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms.

Firefly algorithm inspired energy aware clustering protocol for wireless sensor network

Most of the existing cluster-based routing protocols focus either on cluster head election schemes or on refinement of routing process ignoring process of cluster formation. In this paper, firefly algorithm inspired energy aware clustering protocol for WSN is presented. The idea of the proposed protocol is to elect the cluster head nodes by assigning them intensity value defined through firefly algorithm. Another contribution of this paper is energy efficient cluster formation where cluster members join to cluster heads based on intensity parameter associated with cluster head. The presented protocol ensures that the elected cluster heads are usually located in densely populated area of the network resulting in low distance intra-cluster data transmissions and ultimately leading to low energy consumption. The firefly-based protocol is compared with popular routing protocols such as LEACH, LEACH-C and EOICHD and comparative results indicate that proposed protocol is highly energy efficient than other protocols.

Swarm dynamics for enhanced energy aware clustering

Energy can be efficiently conserved in WSN through clustering of nodes. As in all shared-medium networks, medium access control (MAC) protocol enables the smooth functioning of the network. An important function of MAC is to prevent the bottle-neck between two nodes sending data simultaneously. Many MAC protocols have been developed for smooth functioning of WSN which includes Berkeley medium access control (BMAC) which utilises minimal power listening as well as a proper preamble for minimal power communication. The main challenge of BMAC is overhearing and power wasting in long preambles. The aim of this work is to cluster BMA protocol using heuristic methods based on river formation dynamics (RFD) and particle swarm optimisation (PSO). The suggested protocol's performance is evaluated for packet delivery ratio (PDR), end to end delay, hop as well as jitter. The outcome shows that the proposed river PSO cluster BMAC performs better than BMAC with flooding and BMAC with cluster-based routing when compared with static and varying node mobility.

Energy-aware clustering of CoMP-DPS transmission points

The main idea behind CoMP operation is to efficiently coordinate multiple transmission points to provide better transmission quality and QoS, while improving spectral efficiency. This concept can be extended to the heterogeneous networking environment of 5G systems and is expected to be one of the key technologies of the future. In this work, we focus on the CoMP transmission with Dynamic Point Selection (DPS) due to its low complexity and the gains it can offer to the reduction of the size of the clusters and to the network’s power consumption. Therefore, in this paper, we present an Energy-Aware Clustering (EnAC) scheme to be utilized with CoMP-DPS in LTE-A. The goal of the proposed scheme is to reduce, as much as possible, the power consumption at each BS of a cluster without compromising the CoMP operation. In other words, we incorporate energy-related parameters into the CoMP clustering procedure aiming to raise the energy awareness of the overall system. These parameters are thoroughly studied and fine-tuned through simulation results which prove the energy efficiency of the proposed scheme.

A Reliability Improved Routing as a Qos Measure for Mobile Ad-Hoc Networks

In the recent time, the huge expansion in the application of the real-time applications for smartphones resulted in new difficulties when it is time to develop new protocols for mobile ad-hoc networks (MANETS). Most important within these difficulties is to activate real-time applications for mobile ad-hoc networks which includes quality of service (QoS) support, like bandwidth constraints and stability issues. Adding to this of course the reliability of the nodes as an important factor that has a direct effect on the network performance and data integrity.In this paper we will discussed the QoS main issue that affect several factors in the mobile ad hoc networks, which is the reliability. Because of the fact that the reliability and the availability of the nodes can be interacted made me use both the terms throughout the paper. we will suggest the efficient multi-path Quality of service routing (EMQR), (EMQR) as a QoS guarantee for the stability and reliability issues in the network. Next, we will discuss the availability issues as a QoS guarantee and for this we will suggest the best suitable protocol which is “an efficient warning energy aware Cluster head” WEAC as a solution to the availability issue.The two protocols will guarantee the availability and the reliability in MANETs as it is the new direction for routing algorithms design and it is a way to enhance the availability by controlling the Cluster head based on its power level as a metric for availability as in WEAC protocol and the reliability by increasing the reserved bandwidth for the route as mentioned in EMQR protocol.

Energy aware cluster based routing protocol over distributed cognitive radio sensor network

Cognitive radio sensor network (CRSN) is a combination of wireless sensor network (WSN) and opportunistic spectrum access technology. It involves the issues related to energy and dynamic spectrum inherited by WSN and cognitive radio network (CRN) respectively. CRSN poses research challenges in designing an efficient topology control for communication in network. In this paper, we propose an Energy Aware Cluster based Routing Protocol (EACRP) for CRSN that jointly considers both the energy and dynamic spectrum challenges. Reported schemes for CRSN suffer from high frequency of re-clustering due to PU activities and are energy inefficient. The proposed scheme employs a self-organized distributed clustering to obtain less average node power by producing the optimal number of clusters. To mitigate the effect of PUs’ activities, our proposed scheme forms clusters having more number of common channels and employs the concept of cooperative sensing in selecting the data channels for intra-cluster communication. To enhance inter-cluster connectivity, EACRP selects gateway nodes having more energy, closer to sink node and more number of common channels with neighboring nodes. Furthermore, a cluster-head (CH) rotation mechanism is employed that picks the CH based on nodes’ residual energy values, available channels, neighbors and distance to the sink in order to have longer network lifetime. Upon detection of an event, the EACRP aims to route the event data through an energy efficient and stable path from source node to the sink node. On performance evaluation, we found that EACRP outperforms in terms of end-to-end packet delay, node energy consumption, packet delivery ratio and stability of selected gateway nodes than considered competitive approaches.

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In theresearch, Reinforcement Learning (RL) based energy-aware clustering algorithm is proposed by which the neighboring nodes in the cluster selects an appropriate Cluster Head ( CH) by observing the environmental conditions like as energy consumptionand coverage that is computed as distance from the CH to the Base Station (BS). An optimal cluster is selected by each neighboring node, which minimized the energy consumption and network lifetime. The problem of selecting an optimal CH is resolved using the RL approach. Using the RL approach, the CH having the highest reward point is selected for data communication. The results show that energy saving of 7.41%, 3.27%, 4.03%, and 2.79 % is achieved for 100, 200, 300, and 400 deployed nodes, respectively.

Hybrid energy aware clustered protocol for IoT heterogeneous network

IoT diverse applications face many challenges. The main challenge is to have efficient energy aware communication protocols that utilize the diversity and heterogeneity of the connected things through Internet. Saving energy is a vital requirement in the limited battery energy nodes and also for the outsourced energy nodes for green computing. IoT milieu has many diverse devices that are heterogeneous in their energies, their Internet availability, etc. These devices are usually distributed into regions with different heterogeneity levels; ranging from homogeneous to near homogenous, till reaching to the high heterogeneous regions. Many existed protocols efficiently treated either the homogenous devices or heterogeneous devices. This paper defeats the gap between the physical wireless sensor network environment and the real heterogeneous Cyber IoT milieu. This paper targets not only providing an efficient hybrid energy aware clustering communication protocol for green IoT network computing; Hy-IoT, but also provides a real IoT network architecture for examining the proposed protocol compared to commonly existed protocols. Efficient cluster-head selection boosts the utilization of the nodes energy contents and consequently increases the network life time as well as the packets transmission rate to the base station. Hy-IoT uses different weighted election probabilities for selecting a Cluster-head based on heterogeneity level of the region. Simulation shows that Hy-IoT prolongs the network life time and increases the throughput compared to the SEP, LEACH and Z-SEP. Hy-IoT provides prolonging for the network life time ranging from 47.8% to 92.5% based on the heterogeneity level and also the average throughput was boosted ranging from 11.5% to 70.1% based on the heterogeneity level.

Performance Evaluation of new Energy Aware Cluster Based Multi-hop (EACBM) Routing Protocol in Wireless Sensor Networks

Performance Evaluation of new Energy Aware Cluster Based Multi-hop (EACBM) Routing Protocol in Wireless Sensor Networks

Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things

With ample technological developments and flourishing demand for digital assistance in daily life and work environments that goes along with it, technologies are required to derive this application domain to the subsequent level. Internet of Things (IoT) is considered as one of vision for such technologies. This paper intends to develop self adaptive whale optimization algorithm (SAWOA) for the accomplishment of energy-aware cluster head selection and clustering protocols under wireless sensor network (WSN)—based IoT. Along with the parameters like energy, distance, and delay of sensor nodes in WSN, this simulation considers both load and temperature of IloT devices. After modeling the simulation, it carries out a valuable performance analysis in terms of network efficiency, normalized energy and load and temperature of the selected cluster head. The performance analysis compares the effectiveness of proposed SAWOA over traditional artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), adaptive GSA (AGSA) and WOA-based cluster head selection models. The outcome from simulation model proves the successful performance of SAWOA in cluster head selection so that the lifetime of network is prolonged.

An energy-aware clustering algorithm for wireless sensor networks: GA-based approach

An energy-aware clustering algorithm for wireless sensor networks: GA-based approach

An energy-aware clustering algorithm for wireless sensor networks: GA-based approach

Energy conservation is the predominant requirement of wireless sensor networks. Clustering is a technique which helps in achieving the goal of energy efficiency and scalability. Several clustering approaches using genetic algorithm (GA) as an optimisation tool are proposed in the literature. Most of these clustering approaches lead to multi-objective optimisation. In this paper, we propose a GA-based clustering algorithm (GACA) which considers major factors responsible for effective clustering. The proposed approach has been compared with existing approaches for the best fit and optimal fit case. Simulation results show that the proposed GACA approach is more energy efficient than existing approaches and optimal fit results are better than the best fit results.

Energy-efficient hadoop for big data analytics and computing: A systematic review and research insights

As the demands for big data analytics keep growing rapidly in scientific applications and online services, MapReduce and its open-source implementation Hadoop gained popularity in both academia and enterprises. Hadoop provides a highly feasible solution for building big data analytics platforms. However, defects of Hadoop are also exposed in many aspects including data management, resource management, scheduling policies, etc. These issues usually cause high energy consumption when running MapReduce jobs in Hadoop clusters. In this paper, we review the studies on improving energy efficiency of Hadoop clusters and summarize them in five categories including the energy-aware cluster node management, energy-aware data management, energy-aware resource allocation, energy-aware task scheduling and other energy-saving schemes. For each category, we briefly illustrate its rationale and comparatively analyze the relevant works regarding their advantages and limitations. Moreover, we present our insights and figure out possible research directions including energy-efficient cluster partitioning, data-oriented resource classification and provisioning, resource provisioning based on optimal utilization, EE and locality aware task scheduling, optimizing job profiling with machine learning, elastic power-saving Hadoop with containerization and efficient big data analytics on Hadoop. On one hand, the summary of studies on energy-efficient Hadoop presented in this paper provides useful guidance for the developers and users to better utilize Hadoop. On the other hand, the insights and research trends discussed in this work may inspire the relevant research on improving the energy efficiency of Hadoop in big data analytics.

EA-CRP: A Novel Energy-aware Clustering and Routing Protocol in Wireless Sensor Networks

This paper proposes a novel energy-aware and layering-based clustering and routing protocol (EA-CRP) for gathering data in WSNs. The main goal of EA-CRP is to reduce the energy consumption among all sensor nodes in the network. To achieve this goal, EA-CRP employs an innovative multilayered architecture. More specifically, we divide the field into a number of layers where the width of a layer not only decreases towards the base station, but also is composed of a certain number of clusters (sub layers). The proposed design not only shortens the communication distance between nodes, but also decreases the amount of communication overhead required for setting up clusters. Furthermore, a multi-hop routing algorithm for inter-cluster communications is proposed which basically considers the tradeoff between distance and the energy of relaying nodes. The effectiveness of EA-CRP is verified through comparing its results with those obtained from previous relevant protocols. Interestingly, our experimental results show that EA-CRP has superior performance in term of network lifetime, energy efficiency, and scalability.