Dynamic Cluster Head Selection Research Articles

Dynamic Cluster Head Selection in WSN

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Energy-Efficient Improved Optimal K-Means: Dynamic Cluster Head Selection based on Delaying the First Node Death in MWSN-IoT

The Internet of Things (IoT), which attaches dynamic devices that can access the Internet to create a smart environment, is a tempting research area. IoT-based mobile wireless sensor networks (WSN-IoT) are one of the major databases from which the IoT collects data for analysis and interpretation. However, one of the critical constraints is network lifetime. Routing-based clustered protocols and cluster head (CH) selection are crucial in load balancing and sensor longevity. Yet, with clustering, sensor node mobility requires more overhead because the nodes close to the center may get far and thus become unsuitable to be a CH, whereas those far from the center may get close and become good CH candidates, influencing energy consumption. This paper suggests an energy-efficient clustering protocol with a dynamic cluster head selection considering the distance to the cluster center, remaining energy, and each node's mobility degree implementing a rotation mechanism that allows cluster members to be equally elected while prioritizing those with the minimum weight. The advised algorithm aims to delay the first node death (FND) and thus prolong the stability period and minimize energy consumption by avoiding re-clustering. The performance of the proposed protocol exceeds that achieved in our previous work, Improved OKmeans, by 11%, in first dead node lifetime maximization, 43% in throughput, and 44% in energy efficiency.

MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs

In Wireless Sensor Networks (WSNs), sensors are deployed in a specific region to sense the environment’s physical parameters. After sensing, data is processed and sent to the base station through a given route. Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively, efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be considered an important decision approach for optimal path selection and saving energy. This paper proposes a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two sub-processes: the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head (CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency.

Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

A joint optimized data collection algorithm based on dynamic cluster-head selection and value of information in UWSNs

Underwater wireless sensor networks (UWSNs) have become one of the enabling technologies for the development of future ocean monitoring systems, where autonomous underwater vehicles (AUVs) provide a very attractive way for the data collection of sensor nodes. The data obtained by nodes may have different importance and time sensitivity, which is recorded as the value of information (VoI). The VoI often decays with time. The obtained VoI based on AUV is closely related to the path length of AUV. A longer AUV path lead to more time to collect data, along which less VoI could be obtained finally. Moreover, as sensor nodes are battery-powered and therefore energy-constrained, selecting the nodes with larger residual energy as the AUV path nodes helps to prolong the network lifetime. Therefore, we focus on AUV path planing with the objective of maximizing the lifetime of network while satisfying the constraint on VoI. In this paper, considering the node energy and VoI, we provide an Integer Linear Programming (ILP) model to find an optimal path of AUV. Then, a joint optimized data collection algorithm (JODA) that taking in account the influence of residual energy of nodes and VoI is proposed, which reduces the computational complexity. In our experiments the effectiveness of JODA has been proved by comparing its performance with the theoretical value determined by the ILP model. We also compare the performance of JODA with that of other data collection algorithms, namely TSP, GAAP and EEDA. The simulation results demonstrate that the JODA always outperforms all other algorithms in terms of the total VoI and network lifetime.

Adoption features and approach for UWB Wireless Sensor Network based on Pilot Signal assisted MAC

In the field of wireless sensor network (WSN), Pilot Assisted Transmission (PAT) is a new concept. In our previous research, a mac layer algorithm called, “PA-MAC” was designed, which exclusively uses PAT technique for the medium access control. The performance of PA-MAC was evaluated under multi-hop and single hop WSNs. It was excellent under a single hop structure (i.e. network with a few nodes). But when it was evaluated for the dense network topology, the performance seriously declined. It was noted that the two main reasons of performance degradation are interference and transmission range. Technically there are two ways to tackle a dense network traffic problem. One is to use the multi-hop structure and the other one is clustering. In this research paper, clustering based adoption strategy is examined, and beside dynamic clustering approach multiple optimization features (i.e., clustering formation based on transmission range, dynamic cluster head selection and use of the Volterra code for the mitigation of interference) are added and tested. Collectively these adoption features have not only improved the media access performance but also optimized the network lifetime.In the field of wireless sensor network (WSN), Pilot Assisted Transmission (PAT) is a new concept. In our previous research, a mac layer algorithm called, “PA-MAC” was designed, which exclusively uses PAT technique for the medium access control. The performance of PA-MAC was evaluated under multi-hop and single hop WSNs. It was excellent under a single hop structure (i.e. network with a few nodes). But when it was evaluated for the dense network topology, the performance seriously declined. It was noted that the two main reasons of performance degradation are interference and transmission range. Technically there are two ways to tackle a dense network traffic problem. One is to use the multi-hop structure and the other one is clustering. In this research paper, clustering based adoption strategy is examined, and beside dynamic clustering approach multiple optimization features (i.e., clustering formation based on transmission range, dynamic cluster head selection and use of the Volterra code for the mitigation of interference) are added and tested. Collectively these adoption features have not only improved the media access performance but also optimized the network lifetime.

Efficient and Dynamic Cluster Head Selection for Improving Network Lifetime in WSN using Whale Optimization Algorithm

Efficient and Dynamic Cluster Head Selection for Improving Network Lifetime in WSN using Whale Optimization Algorithm

Enhancing Network lifetime and Throughput in Heterogeneous Wireless Sensor Networks

In the modern era, WSNs broadly used in many research areas. Mainly researchers are focusing on rising the network lifetime, throughput and decreasing the energy utilization to make the network more reliable, robust and more responsive for a longer period of time. In this paper, two key aspects are taken into account; (i) network lifetime (ii) throughput of the network. The proposed approach is based on multilevel heterogeneity inspired by SEP (Stable Election Protocol). First node dead in the network plays a vital role in network lifetime because if the first node dead after a long period then definitely network lifetime becomes better. To get better the network life time, the proposed approach is another effort to make the network more responsive. Proposed approach compared with NEECP (Novel Energy-Efficient clustering protocol), ICACO (Inter Cluster Ant Colony optimization) and DCHSM (Dynamic Cluster Head Selection Method) gives the improved outcome in conditions of Network lifetime and throughput. In addition to this, comparison with existing approaches is carried out by considering the research papers from the year 2000 to 2017for 16 approaches.

Particle-Water Wave Optimization for Secure Routing in Wireless Sensor Network Using Cluster Head Selection

Wireless sensor network (WSN) has some great advantages, such as flexible communication, low power consumption, and low cost. In view of the WSN clustering algorithm, the dynamic cluster head selection methods are put forward in order to solve the problem of the unreasonable cluster head selection that may lead to the overlapping coverage and unbalanced energy consumption in the cluster communication. Also, security is an important aspect in the WSN. Various security and routing protocols are developed for increasing the efficiency of packet transmission, but discovering the optimal path without degrading transmission reliability poses a challenging task in the sensor network. Hence, an effective and optimal secure routing algorithm named Particle-Water Wave Optimization (P-WWO) is developed in this research for routing the data packets in secure path. The proposed P-WWO algorithm is designed by integrating the Particle Swam Optimization (PSO) with the Water Wave Optimization (WWO). The secure route needed to broadcast the data packets is determined through the selection of Cluster Head using the PSO-based cellular automata with fitness measure. However, the fitness measure is computed by considering the factors, like energy, delay, trust, consistency factor, and maintainability factor. Accordingly, the routing path with the minimal distance and less delay is accepted as the optimal path using the proposed P-WWO based on the fitness value. The route maintenance process enables the proposed optimization to decide whether the packets can be transmitted in the selected route or need to re-route the data. Moreover, the proposed P-WWO obtained better performance using the metrics, such as energy balancing index, coverage, number of alive-nodes, and average energy left with the values of 0.9246 99.9%, 144, and 0.666 J, respectively.

Simulation research of nodes target tracking algorithm based on clustering in wireless sensor network

Since sensor nodes are constrained in cost and volume, they have limited ability in data storage and processing. Therefore, the routing algorithm needs lower time and space complexity for energy conservation. This article design a dynamic cluster head selection mechanism based on back-off time of residual energy of nodes, aiming at energy conservation and low complexity for the algorithm. At the phase of cluster head selection, we reduce the back-off time of nodes with more energy, so it can win the competition in cluster head selection and control the number of cluster heads, by controlling the radius of cluster lead broadcasting. Then the cluster member nodes are chosen according to the hop range to cluster head, whose weights are computed by the distance to cluster head and the residual energy. At the premise of saving self-energy, it can choose different cluster head and save the energy of cluster head to achieve global energy balance and prolong the network lifetime. The simulations verify that the improved algorithm can make reasonable distribution location of cluster head. The membership has close number and the reasonable topology ensure the global consumption of network.

Energy Efficient and High PDR Wireless Sensor Networks using DCHS and FIS

Wireless Sensor Network (WSN) is forthcoming technology that afford assorted applications in hearth care, military, industrial and object tracking. WSN is a wireless device consisting of dispersed autonomous apparatus using sensors to collectively adviser physical or environmental conditions. This paper is presenting WSN using dynamic cluster head selection (DCHS) and fuzzy inference system (FIS) technique. The DCHS methods for WSN are put leading in procedure to solve the problem of the arbitrary cluster head selection that may lead to the extending description and irregular energy consumption in the cluster communication. FIS is a scheme that uses fuzzy inclination theory to atlas inputs to outputs. FIS is depend on mamdani rules and Fuzzy rules are a compilation of linguistic statements that characterize how the FIS should make a judgement regarding classifying an input or regulating an output. The proposed technique is implemented MATLAB 2012R and calculated average energy consumption and success rate packet delivery ratio.

Fuzzy-Logic-Inspired Zone-Based Clustering Algorithm for Wireless Sensor Networks

With the increasing use of wireless terminals, wireless sensor networks (WSNs) have received significant attention owing to their wide usage in monitoring harsh environments, crucial surveillance and security applications, and other real-life applications. Sensor nodes in operation are equipped with batteries that are not rechargeable in most of the cases. Attaining the lifetime maximization of these nodes has drawn the attention of researchers in recent years. The clustering mechanism is highly successful in conserving energy resources for network activities and has become a promising field for researches to overcome the issues of battery-constrained WSNs. This research work implements zone-based clustering with the fuzzy-logic approach for dynamic cluster head (CH) selection. It aims to resolve the problem of unbalanced energy dissipation among the CHs in the network. The experimental results show that the proposed protocol outperforms the existing protocols in terms of maximizing the network lifetime.

Optimized and Dynamic Selection of Cluster Head Using Energy Efficient Routing Protocol in WSN

Wireless Sensor Networks plays an outstanding role in providing dynamic cluster head (CH) selection. However, the selection of CH is a major challenge due to erroneous CH selection and can lead to unbalanced energy consumption. This paper addresses this challenge by proposing a hybrid optimization algorithm for CH selection. The proposed CH selection comprises three phases, which includes the setup phase, transmission phase, and measurement phase. At first, the energy and the node’s mobility in the network are initialized. The setup phase is processed by choosing CH using the Optimized Sleep-awake Energy-Efficient Distributed clustering, which is designed by determining the optimal threshold and CH using proposed Rider-Cat Swarm Optimization (RCSO) algorithm. The proposed RCSO is designed by integrating Rider Optimization Algorithm into Cat Swarm Optimization. Here, the threshold and CH are chosen using multi-objective constraints, which involves distance, energy, and delay. After determining the CHs, the data transmission begins from CHs to the base station. At last, in the measurement phase, the residual energies produced from the nodes are being updated. The proposed RCSO method shows superior performance by providing maximal energy, throughput, and the number of alive nodes with values 0.0351 J, 74.715%, and 18 respectively.

Elliptic Curve Cryptography Based Security Protocol of MANET under Dynamic Cluster Head Selection Environment

Elliptic Curve Cryptography Based Security Protocol of MANET under Dynamic Cluster Head Selection Environment

Energy efficient dynamic clustering routing protocol in underwater wireless sensor networks

In underwater wireless sensor networks (UWSN) energy control is a vital issue. Few UWSN applications include both dynamic and static sensor nodes within the same networks. The mobility of dynamic nodes can lead to performance related issues as these nodes include small memory, battery power, short range communication, and low speed calculation ability. Hence, an energy efficient routing protocol is important to direct the incoming packet. Dynamic cluster technology utilising cluster based network arrangement is mainly learned in routing protocol. In this paper, proposed a unique routing algorithm called as energy efficient and mobility-based dynamic cluster head selection routing protocol (EE-MDCHSRP) is suggested for UWSN for successful dynamic cluster head choices. The dynamic cluster-head nodes are determined considering the factors including node density, remaining energy and least mobility factor. Simulation test and data analysis results prove that the overall performance of proposed or projected routing method is 96.98% more effective when compared to existing routing protocols of the UWSN, and it was simulated in Linux-based Network Simulator 2 (NS2) simulation software environment. This research work helps us to reduce power consumption, response time and avoid overload, improve the throughput and network lifetime.

Energy‐efficient source location privacy protection for network lifetime maximization against local eavesdropper in wireless sensor network (EeSP)

AbstractWireless sensor networks can be deployed in harsh environments for monitoring purposes. In such environments where there is a lack of human presence, the network may face severe security and privacy issues due to unauthorized access of a powerful attacker. The attacker may exploit the contextual information to locate the position of the asset. We propose a scheme for protecting the location of the source node, which consumes less energy hence maximizing network lifetime. The proposed scheme has the inspiration of black and white regions of the chessboard, which represent sleep and active regions in the wireless sensor network. This alternation plays its role in reducing the total energy consumption along misguiding the eavesdropper from finding the source node location. Our scheme uses dynamic cluster head selection to further add to location privacy. The results show that we have improved the safety period by a maximum of 131% and a minimum of 17% than that of the compared techniques. Similarly, a maximum of 60% reduction in energy consumption and a 20% reduction in latency is recorded by our proposed scheme. These results make our scheme suitable for application requiring high‐safety period along maximizing network lifetime.