Energy Aware Cluster Head Research Articles

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks

The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery powered, and may be completely drained, resulting in deterioration in network lifetime. Energy efficient protocols aim to maximize network lifetime while minimizing overall energy consumption to avoid interruptions to the wireless sensor nodes deployed for monitoring and recording physical information from the environment. Moreover, hybrid metaheuristic-optimization based clustering and routing protocols are widely used for attaining energy stability and network lifetime. In this study, the golden eagle optimization algorithm (GEOA) and improved grasshopper optimization algorithm (IGHOA) based on the energy efficient cluster-based routing protocol (GEIGOA) are proposed for sustaining energy stability and augmenting network lifetime longevity by overcoming challenges in the cluster head (CH) selection process. In particular, by optimizing the node centrality, node degree, distance to the base station, distance to the neighbors, and residual node energy, GEOA selects an optimal CH from the deployed group of sensor nodes in the network. Furthermore, IGHOA is utilized to determine a reliable and optimal route between the CH and base station (BS) by assessing node degree, residual energy, and distance parameters. Moreover, the proposed GEIGOA was confirmed to be sufficiently capable in improving the probability of preventing the worst nodes from being selected as CHs with different number of sensor nodes, which was predominantly enhanced by 12.64%, 15.82%, 18.96%, and 20.98% compared with the competitive CH selection schemes. In addition, the computational cost incurred by the proposed GEIGOA with different number of sensor nodes was also confirmed to be minimized by 14.98%, 17.21%, 19.76%, and 21.62% compared with the competitive CH selection schemes.

Rate-Aware Fuzzy Clustering and Stable Sensor Association for Load Balancing in WSNs

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale–Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

Energy Aware Cluster Head Selection and Multipath Routing Using Whale-based Tunicate Swarm Algorithm (WTSA) for Wireless Sensor Network

ABSTRACT The wireless sensor network (WSN) plays a significant role in home automation, energy consumption monitoring, medical field, computational field, and so on. The major challenges associated with the WSN are time delay during the data transmission and network lifetime. Moreover, the cluster head selection (CHS) is also a challenging task. Hence, this research article introduces a novel multi-objective whale-based tunicate swarm algorithm (WTSA) for energy aware CHS process for multipath routing in WSN. The newly developed WTSA integrates the tunicate swarm algorithm (TSA) and whale optimization algorithm (WOA). The CHS procedure can be initiated by stimulating the sensor nodes in a network. The cluster heads (CHs) are selected by estimating these stimulated sensor nodes for delay, intercluster distance, node degree, energy and intracluster distance individually. Finally, the multipath routing is carried out through the proposed WTSA. The performance of the developed WTSA model is evaluated using metrics such as delay, number of nodes that are alive, throughput, and residual energy. The introduced WTSA attained better performance with respect to delay of 0.140 seconds for 100 nodes and throughput of 69,641 kbps for 100 nodes.

Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

A new energy aware cluster head selection for LEACH in wireless sensor networks

Internet of Things (IoTs) as a new network pattern for the intelligent world usually uses wireless sensor networks (WSNs) as a perception layer which consisted of numerous number of sensor nodes scattered in the environment to gather intended information. The selected information then is sent to a base station (BS) to be sent to cloud server for further processing. Since the energy of sensor nodes is limited, the most significant challenge in these networks is reducing the energy consumption of the network. It is proved that dividing the network to clusters can significantly reduce the energy consumption. One of the most popular clustering protocols in WSNs is the Low-Energy Adaptive Clustering Hierarchy (LEACH). In this protocol, cluster heads (CHs) are selected randomly which results in poor performance in real scenarios. In this article, a new energy aware CH selection algorithm is proposed which selects CHs based on the residual energy, the position and centrality of nodes. It uses a variable range upon which the centrality and the number of neighbours of each node are calculated. Simulation results show that the proposed algorithm outperforms LEACH, Multi-hope Routing with LEACH (MR-LEACH) and Enhanced Multi-hop LEACH (EM-LEACH) in terms of reducing energy consumption, increasing network lifetime and improving network reliability.

An Energy-Aware Cluster Head Selection Protocol based on Multi-Objective Dolphin Swarm Optimization in Wireless Sensor Network

The Wireless Sensor Network (WSN) is spread widely in many applications due to its portable, cost-effective, and user-friendly approach. This needs to adopt routing schemes to forward the sensed information to the sink nodes or base station through minimum-cost links. The frequently utilized data accumulation approach is clustering which organizes the nodes into clutches to minimize the consumption of energy. Likewise, in clustering protocols, the additional load will be bear by the Cluster Head (CH) for organizing various deeds inside the cluster. As a result, appropriate CH selection and their power balance using efficient clustering protocol are significant features for the long-term operation of the Wireless Sensor Network (WSN). In this paper, An Energy-Aware Cluster Head Selection Protocol based on Multi-Objective Dolphin Swarm Optimization (EACHP-MODSO) is proposed to enrich the lifetime of Wireless Sensor Network (WSN). This proposed method considers six criteria to select effective Cluster Head like residual energy, inter cluster distance, intra-cluster distance, mobility, node density, and delay. The results of the proposed system show better enhancement of network lifetime compared to existing protocols.

Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Wireless sensor networks depend on the effective functioning of the nodes in the network, which is concerned regarding the energy that is essential for the extended network life-time. Clustering plays a major role in enabling energy efficiency, which extends the life-time of the network. Thus, the paper introduces a cluster head (CH) selection phenomenon based on the algorithm, Taylor kernel fuzzy C-means (Taylor KFCM), which is the modification of the kernel-based fuzzy c-means (KFCM) algorithm in the Taylor series. The developed algorithm chooses the cluster head using the selection phenomenon, acceptability factor, which is computed using the energy, distance, and trust. In other words, a node acts as a CH when the fitness constraints of minimal distance, maximal trust, and maximal energy are attained. The simulation environment is established using 50, 100, and 200 nodes with 5 and 10 CHs and the effectiveness of the proposed CH selection is revealed through the analysis depending on the metrics, throughput, energy, delay, and the number of alive nodes. The proposed Taylor kernel fuzzy C-means acquired a maximal throughput, energy, and alive nodes of 0.2857, 0.0947, and 31, and minimal delay and routing overhead of 0.1219, 0.0418 respectively.

A survey of energy-aware cluster head selection techniques in wireless sensor network

Recently, wireless sensor networks (WSNs) are becoming very famous as they are inexpensive and easy to maintain and manage. The network contains a group of sensor nodes, which are capable of sensing, computing, and transmitting. Energy efficiency is one of the most important challenging problems in WSN. Sensor nodes have inadequate energy and installed in remote areas. Hence, it is difficult to restore the batteries in WSN. Therefore, to maximize the network lifetime, appropriate clustering techniques and cluster head (CH) selection methods should be implemented. The main idea behind the clustering technique is that it clusters the sensor nodes and reduces the composed data simultaneously and then, it broadcasts the data. In this process, CH selection is an essential part. Therefore, this survey paper provides an overview of the clustering techniques for reducing energy consumption by reviewing several CH selection techniques in WSN that provide high energy efficiency. Several techniques have been employed for CH selection based on partitional clustering, optimization, low-energy adaptive clustering hierarchy, hierarchical, distributed, and other classification methods. Finally, an analysis is done based on the implementation tools, metrics employed, accuracy, and achievements of the considered CH selection techniques.

Multi-objective HSDE Algorithm for Energy-Aware Cluster Head Selection in WSN

Multi-objective HSDE Algorithm for Energy-Aware Cluster Head Selection in WSN

Energy-aware Cluster Head Selection in WSN using HPSOCS Algorithm

Energy-aware Cluster Head Selection in WSN using HPSOCS Algorithm

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.

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.

Energy Aware Cluster Head Selection for Maximizing Lifetime Improvement in Internet of Things

Energy efficiency is a major concern in Internet of Things (IoT) networks as the IoT devices are battery operated devices. One of the traditional approaches to improve the energy efficiency is through clustering. The authors propose a hybrid method of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. The performance of the hybrid algorithm is evaluated using energy, delay, load, distance, and temperature of the IoT devices. Performance of the proposed method is analyzed by comparing with the conventional methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSO algorithms. The performance of the hybrid algorithm is evaluated using of number of alive nodes, convergence estimation, normalized energy, load and temperature. The proposed algorithm exhibits high energy efficiency that improves the life time of IoT nodes. Analysis of the authors' implementation reveals the superior performance of the proposed method.

A Dual Ring Connectivity Model for VANET Under Heterogeneous Traffic Flow

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET’s overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system.

Water rippling shaped clustering strategy for efficient performance of software define wireless sensor networks

The routing protocols are the hot areas to manage the network quality-of-service (QoS), viz., energy consumption, lifetime, network design and packet overhead. Network optimization relies on different calibers of decision: to discuss the network parameters meticulously for overall network improvement. Thus several criteria are proposed which fixate on energy conservation, architecture design, etc. to implicitly or explicitly amend the network performance. We propose a novel strategy named as Water-Rippling Shaped Clustering (WARIS) is a hybrid approach applies to cluster the large-scale software define wireless sensor network, which resembles the shape of water rippling. Major achievements are improved cluster design, energy aware cluster head (CH) selection method and reducing re-clustering overhead. The centrally controlled layer design locally restricted clustered design, and then cluster member selection in WARIS gives better performance as compared to the other two state of the art competitors MCDA and EELBCRP. The to-and-fro message communication between the deployed nodes and BS for exchanging parametric values and making decisions makes this cluster design process lengthy. Load management is done during the process cluster size formation which improves the network performance. Performance simulations illustrate that WARIS is a better choice to implement over wireless sensor networks, predicated on energy consumption and set-up completion time.

Energy-Efficient Multicast Transmission for Underlay Device-to-Device Communications: A Social-Aware Perspective

In this paper, by utilizing the social relationships among mobile users, we present a framework of energy-efficient cluster formation and resource allocation for multicast D2D transmission. In particular, we first deal with D2D multicast cluster/group formation strategy from both physical distance and social trust level. Then we aim to maximize the overall energy-efficiency of D2D multicast groups through resource allocation and power control scheme, which considers the quality-of-service (QoS) requirements of both cellular user equipment and D2D groups. A heuristic algorithm is proposed to solve above energy-efficiency problem with less complexity. After that, considering the limited battery capacity of mobile users, we propose an energy and social aware cluster head update algorithm, which incorporates both the energy constraint and social centrality measurement. Numerical results indicate that the proposed social-tie based D2D multicast group formation and update algorithm form a multicast group in an energy efficient way. Moreover, the proposed resource and power allocation scheme achieves better energy efficiency in terms of throughput per energy consumption. These results show that, by exploiting social domain information, underlay D2D multicast transmission has high practical potential in saving the source on wireless links and in the backhaul.

Optimal load balanced clustering in homogeneous wireless sensor networks

SummaryBalancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.

High-Leach Energy Efficient Routing Protocol for Wireless Sensor Networks

Background/Objectives: In this paper, an enhanced Low Energy Aware Cluster Head (LEACH) Protocol is proposed. Methods/Statistical Analysis: We study an extension to a Grid-Based Network to identify an efficient block length for the regions. Simulations show that a higher transmission range and cluster head percentages were not efficient and a low cluster head percentage with a high transmission range is preferred. Findings: In this exploration, we concentrate on amplifying the life of the system, which has turned into a basic issue in sensor systems. Bunching of hubs with mixing of information to the group head gets to be one of the imperative intends to augment the future of the system. In the proposed work, we take a gander at the correspondence conventions that can significantly affect the general force dissemination of these systems. The simulation clarifies the effectiveness of our proposed work over its comparatives in phrases of network lifetime, average packet transmissions, cluster head choice rounds supported by means of average power consumption. In this kind of association, the hubs are organized in bunches where Cluster Heads (CHS) pass messages between your part hubs and the base station. Systems group pecking order might prompt harming assaults, particularly when these assaults are gone for CHS. Application/Improvements: The Low Energy Aware Cluster Head (LEACH) Protocol works in the lower energies. In high energies without reducing the network lifetime the data is transmitted in the nodes. We can further improve the transmission of data in different nodes.