scholarly journals Efficient Cluster Head Selection in WSN Using Secure Mobility Cluster Based Algorithm

2020 ◽  
Author(s):  
Sampoornam K P ◽  
Hemavikasini S ◽  
Vidhya S ◽  
Vakula V ◽  
Dharani S
Keyword(s):  
Sensor Node ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Battery Lifetime ◽  
Military Applications ◽  
Major Application ◽  
Simulation Results

Wireless Sensor Networks (WSNs) are widely adopted by various civilian/military applications for implementing real-time monitoring or long-term surveillance task. Considering sensor nodes with mobility has emerged as a major application in environmental monitoring or surveillance. Due to the limited battery lifetime, the network in the deployed region is divided into clusters and the clusters are controlled by their Cluster Heads (CH). But selecting CH in WSNs (considering the network with both static and mobility nodes) is a challenging task because security is significant. To prevent the malicious sensor node from becoming CH, Secure Mobility Cluster Based Algorithm (SMCBA) is proposed. This algorithm considers only static node among mobility node for selecting CH with efficient parameters such as trust criteria, selection time and mobility. The simulation results show that the proposed algorithm works effectively compared with the existing clustering algorithms.

Clustering Algorithms for Wireless Sensor Networks Survey

2020 ◽  
Vol 18 (2) ◽  
pp. 143-149
Author(s):  
Sathyapriya Loganathan ◽  
Jawahar Arumugam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Sensor Node ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Head Node

This paper aims to discuss a comprehensive survey on clustering algorithms for wireless sensor networks (WSN). The several real-time applications adopted the WSN with the advance features. But the capacity and size of the battery used in the sensor nodes are limited. Battery replacement or recharging is very difficult in most outdoor applications. Hence handling this kind of network is one of the issues. One of the best solutions to the energy issue is Clustering. Clustering is to balance the energy consumption of the whole network by cluster-based architecture to prolong the network lifetime. Sensor nodes grouped into clusters; one sensor node selects as the cluster head for each cluster. The cluster head sensor node collects the data from their sensor member nodes and forwards them to the sink node. In cluster-based architecture, cluster formation and the selection of the cluster head node decides the network lifetime. The paper discusses the for and against various clustering algorithms. It suggests the vital parameters for developing energy-efficient clustering algorithms and steps to overcome the limitations.

A Node Authentication Model in Wireless Sensor Networks With Locked Cluster Generation

2021 ◽  
pp. 236-250
Author(s):  
C. R. Bharathi ◽  
Alapati Naresh ◽  
Arepalli Peda Gopi ◽  
Lakshman Narayana Vejendla
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Cluster Formation ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Generation ◽  
Secure Network

In wireless sensor networks (WSN), the majority of the inquiries are issued at the base station. WSN applications frequently require collaboration among countless sensor nodes in a network. One precedent is to persistently screen a region and report occasions. A sensor node in a WSN is initially allocated with an energy level, and based on the tasks of that sensor node, energy will be reduced. In this chapter, two proposed methods for secure network cluster formation and authentication are discussed. When a network is established then all the nodes in it must register with cluster head and then authentication is performed. The selection of cluster head is done using a novel selection algorithm and for authenticating the nodes. Also, a novel algorithm for authentication is used in this chapter. The validation and authorization of nodes are carried over by managing the keys in WSN. The results have been analyzed using NS2 simulator with an aid of list of relevant parameters.

Super Strongly Perfect Graphs in the Selection of Cluster Heads in Wireless Sensor Networks

2014 ◽  
Vol 591 ◽  
pp. 206-210
Author(s):  
R. Mary Jeya Jothi ◽  
S. Emalda Roslin ◽  
N.M. Nandhitha
Keyword(s):  
Energy Consumption ◽  
Topology Control ◽  
Dominating Set ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Battery Lifetime ◽  
Hierarchical Topology ◽  
Computationally Intensive

Wireless sensor network comprises of dense sensor nodes which are randomly deployed. Major challenges in WSN are limited battery source and computation capacity. Considerable research has been carried out in the area of maximizing battery lifetime by reducing the energy consumption. Once such proposed technique involves hierarchical topology control. Conventionally proposed algorithm for hierarchical topology control involves computationally intensive soft computing tools. It leads to higher energy consumption in the sink node. Hence it necessitates computationally less intensive technique for cluster head selection. In this paper, an efficient cluster head selection is proposed using minimal dominating set in Super Strongly Perfect (SSP) graph.

scholarly journals Qualification of Cluster Header and Cluster Member in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S6) ◽  
pp. 101-105
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Sensor Node ◽  
Data Transfer ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sink Node

Many researches have been proposed for efficiency of data transmission from sensor nodes to sink node for energy efficiency in wireless sensor networks. Among them, cluster-based methods have been preferred In this study, we used the angle formed with the sink node and the distance of the cluster members to calculate the probability of cluster head. Each sensor node sends measurement values to header candidates, and the header candidate node measures the probability value of the header with the value received from its candidate member nodes. To construct the cluster members, the data transfer direction is considered. We consider angle, distance, and direction as cluster header possibility value. Experimental results show that data transmission is proceeding in the direction of going to the sink node. We calculated and displayed the header possibility value of the neighbor nodes of the sensor node and confirmed the candidates of the cluster header for data transfer as the value. In this study, residual energy amount of each sensor node is not considered. In the next study, we calculate the value considering the residual energy amount of the node when measuring the header possibility value of the cluster.

scholarly journals Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

2021 ◽  
Vol 11 (5) ◽  
pp. 4183
Author(s):  
Veerabadrappa Veerabadrappa ◽  
Booma Poolan Marikannan
Keyword(s):  
Wireless Sensor Network ◽  
Load Balancing ◽  
Sensor Network ◽  
Clustering Algorithms ◽  
Research Work ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Physical Parameters

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.

Mutual Exclusive Sleep Awake Energy Efficient Distributive (MESAEED) Clustering: An Approach for Enhancing Lifetime of Wireless Sensor Network

2019 ◽  
Vol 16 (9) ◽  
pp. 3925-3931
Author(s):  
Bhupesh Gupta ◽  
Sanjeev Rana
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Sensor Node ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
A Algorithm ◽  
Extension Work ◽  
Simulation Purpose

For resource constraint network, one uses wireless sensor network in which limited resources are there for sensor nodes. Basic aim of sensor node is to sense something, monitor it and explain it. The issue arises for sensor node is its battery endurance. The battery endurance of sensor node is consuming in communication instead of sensing. In this regard clustering is using now a day’s which reduces endurance consumption. This paper comes with a new clustering protocol MESAEED (Mutual Exclusive Sleep Awake Energy Efficient Distributed clustering), which helps in saving endurance of sensor nodes so that network lifetime will prolong. It is an extension work of previous work MESADC. In previous work cluster head is chooses on the basis of sleep awake mode in mutual exclusive way under communication range and the results were obtained with the help of comparison graph between HEED and MESADC. The proposed MESAEED protocol provides benefit of A* algorithm of heuristic search, HEED and MESADC. MATLAB 8.3 is use for simulation purpose. The comparison graph between HEED, MESADC and proposed MESAEED were shown. Parameters for comparison include alive nodes versus number of rounds taken and number of nodes dead versus number of rounds taken. The graph shows improvement in performance over HEED and MESADC, which results in enhancing lifetime of WSN.

Level and Cluster Based Routing for Wireless Sensor Network

2013 ◽  
Vol 321-324 ◽  
pp. 515-522 ◽  
Author(s):  
Kou Lin Yuan ◽  
Lin Qiao ◽  
Lei Han
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Node ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Method ◽  
Sink Node ◽  
Head Node ◽  
Cluster Based Routing

This paper proposes a level and cluster based routing approach for a wireless sensor network. Nodes in the network are divided into several levels according to their hops to sink node. Every sensor node has a level number. Using level information, a sensor node can send messages to a sink node in a more efficient way, and a sink node can easily locate other sensor nodes. To make network more balanced, the paper introduces a cluster method, which splits nodes in the same level into different clusters, and chooses a cluster head for every cluster, to switch nodes in the cluster to work in turn. Unlike all other cluster routing methods, a cluster head node takes schedule jobs of sensor nodes in the cluster according to their energy left, instead of sensing. The paper also presents several algorithms for constructing a wireless sensor network, querying and scheduling. The simulation experiment shows that the scalability of our method is approximately linear.

scholarly journals LOCATION AWARE CLUSTER BASED ROUTING IN WIRELESS SENSOR NETWORKS

2015 ◽  
pp. 36-41
Author(s):  
S. JERUSHA ◽  
K. KULOTHUNGAN ◽  
A Kannan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Minimum Distance ◽  
Sensor Node ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor

Wireless sensor nodes are usually embedded in the physical environment and report sensed data to a central base station. Clustering is one of the most challenging issues in wireless sensor networks. This paper proposes a new cluster scheme for wireless sensor network by modified the K means clustering algorithm. Sensor nodes are deployed in a harsh environment and randomly scattered in the region of interest and are deployed in a flat architecture. The transmission of packet will reduce the network lifetime. Thus, clustering scheme is required to avoid network traffic and increase overall network lifetime. In order to cluster the sensor nodes that are deployed in the sensor network, the location information of each sensor node should be known. By knowing the location of the each sensor node in the wireless sensor network, clustering is formed based on the highest residual energy and minimum distance from the base station. Among the group of nodes, one node is elected as a cluster head using centroid method. The minimum distance between the cluster node’s and the centroid point is elected as a cluster head. Clustering of nodes can minimize the residual energy and maximize the network performance. This improves the overall network lifetime and reduces network traffic.

scholarly journals Survey on Multi Agent Energy Efficient Clustering Algorithms in Wireless Sensor Networks

2014 ◽  
Vol 3 (3) ◽  
pp. 172-184
Author(s):  
Nazli Bagherzadeh karimi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Awareness ◽  
Multi Agent ◽  
Energy Efficient Clustering

In the last few years, there are many applications for Wireless Sensor Networks (WSNs). One of the main drawbacks of these networks is the limited battery power of sensor nodes. There are many cases to reduce energy consumption in WSNs. One of them is clustering. Sensor nodes partitioned into the clusters so that one is chosen as Cluster Head (CH). Clustering and selection of the proper node as CH is very significant in reducing energy consumption and increasing network lifetime. In this paper, we have surveyed a multi agent clustering algorithms and compared on various parameters like cluster size, cluster count, clusters equality, parameters used in CHs selection, algorithm complexity, types of algorithm used in clustering, nodes location awareness, inter-cluster and intra-cluster topologies, nodes homogeneity and MAC layer communications.

Thermoelectric Powered Autonomous Wireless Sensor Module for Temperature Monitoring

2011 ◽  
Vol 63-64 ◽  
pp. 978-982 ◽  
Author(s):  
Wen Si Wang ◽  
Ning Ning Wang ◽  
Michael Hayes ◽  
Brendan O'Flynn ◽  
Cian O'Mathuna
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Energy Harvester ◽  
Electrical Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Battery Life ◽  
Wireless Sensor Node ◽  
Battery Lifetime ◽  
Sensor Module

Wireless sensor networks are frequently used to monitor temperature and other manufacturing parameters in recent years. However, the limited battery life posts a constraint for large sensor networks. In this work, thermoelectric energy harvester is designed to effectively convert the heat into electrical energy to power the wireless sensor node. Bismuth telluride thermoelectric modules are optimized for low temperature conditions. Charge pump and switching regulator based power management module is designed to efficiently step up the 500mV thermoelectric voltage to 3.0V level for wireless sensor nodes. This design employs electric double-layer capacitor based energy storage with considerations on practical wireless sensor node operation. The implemented energy harvester prototype is proposed for Tyndall wireless sensor system to monitor temperature and relative humidity in manufacturing process. The prototype was tested in various conditions to discover the issues in this practical design. The proposed prototype can expect a 15 years operative lifetime instead of the 3-6 months battery lifetime.

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