Research on Clustering Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 380-384 ◽  
pp. 1231-1234
Author(s):  
Cheng Xu Feng ◽  
Zhong Liu ◽  
Zhi Kun Liu
Keyword(s):  
Clustering Algorithm ◽  
Cluster Head ◽  
Signal Strength ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sink Node ◽  
The Novel ◽  
Selection Mechanism ◽  
Energy Constrained

According to the problem that the sensor nodes are highly energy-constrained, a novel dynamic clustering algorithm for WSN, which is in application to object tracking, is proposed. The cluster head selection mechanism takes not only the distance between the cluster head and the sink node but also the received signal strength and the residual energy of nodes into consideration. The analysis result indicates that, the energy consumption of network communication is reduced and the cluster size is optimized. The simulation results show that the novel algorithm can efficiently prolong the lifetime of WSN, especially when the sink node is far from the network.

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 An Energy-Balanced Mechanism for Hierarchical Routing in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Yanan Wang ◽  
Fuxing Song ◽  
Wenyu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Balance ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sink Node ◽  
Hierarchical Routing ◽  
Cluster Heads

In wireless sensor networks (WSNs), energy-constrained sensor nodes are always deployed in hazardous and inaccessible environments, making energy management a key problem for network design. The mechanism of RNTA (redundant node transmission agents) lacks an updating mechanism for the redundant nodes, causing an unbalanced energy distribution among sensor nodes. This paper presents an energy-balanced mechanism for hierarchical routing (EBM-HR), in which the residual energy of redundant nodes is quantified and made hierarchic, so that the cluster head can dynamically select the redundant node with the highest residual energy grade as a relay to complete the information transmission to the sink node and achieve an intracluster energy balance. In addition, the network is divided into several layers according to the distances between cluster heads and the sink node. Based on the energy consumption of the cluster heads, the sink node will decide to recluster only in a certain layer so as to achieve an intercluster energy balance. Our approach is evaluated by a simulation comparing the LEACH algorithm to the HEED algorithm. The results demonstrate that the BEM-HR mechanism can significantly boost the performance of a network in terms of network lifetime, data transmission quality, and energy balance.

scholarly journals Comparison of Residual Energy-Based Clustering Algorithms for Wireless Sensor Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Asis Kumar Tripathy ◽  
Suchismita Chinara
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Architecture ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Awareness

Wireless sensor network swears an exceptional fine-grained interface between the virtual and physical worlds. The clustering algorithm is a kind of key technique used to reduce energy consumption. Many clustering, power management, and data dissemination protocols have been specifically designed for wireless sensor network (WSN) where energy awareness is an essential design issue. Each clustering algorithm is composed of three phases cluster head (CH) selection, the setup phase, and steady state phase. The hot point in these algorithms is the cluster head selection. The focus, however, has been given to the residual energy-based clustering protocols which might differ depending on the application and network architecture. In this paper, a survey of the state-of-the-art clustering techniques in WSNs has been compared to find the merits and demerits among themselves. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.

scholarly journals Data Clustering Method in Wireless Sensor Networks Based on Residual Energy Perception

2018 ◽  
Vol 14 (06) ◽  
pp. 85 ◽  
Author(s):  
Xudong Yang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Data Gathering ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Forwarding ◽  
Cluster Data ◽  
Head Node

<p class="0abstract"><span lang="EN-US">To prolong the survival time of wireless sensor network, an iterative scheme was proposed. First of all, spectrum clustering algorithm iteratively segmented the network into clusters, and cluster head nodes in each sub cluster were determined depending on the size of residual energy of sensor nodes. Then, a data forwarding balance tree was constructed in each sub cluster. Data forwarding path of each non-cluster head node was defined, and the moving path of a mobile data collector was determined, which used the residual energy as the basis for the network optimization. Finally, this scheme was simulated, and two traditional data gathering algorithms were compared. The results showed that the algorithm designed in this experiment could effectively balance energy consumption among all WSN nodes and had great performance improvement compared with the traditional data collection algorithm. To sum up, this algorithm can significantly reduce the energy consumption of the network and improve the lifetime of the network. </span></p>

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5447-5456
Author(s):  
R. M. Alamelu ◽  
K. Prabu
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Link Quality ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Node Degree ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Energy Efficient Clustering

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.

A Probability-Based Clustering Algorithm with CH Election for Expanding WSN Life Span

2020 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Dimitris N. Kanellopoulos ◽  
Pratik Gite
Keyword(s):  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Signal Power ◽  
Aggregated Data ◽  
Simulation Results ◽  
Buffer Length ◽  
Energy Buffer

Clustering achieves energy efficiency and scalable performance in wireless sensor networks (WSNs). A cluster is formed by several sensors nodes, and one of them is elected as Cluster-head (CH). A CH collects information from the cluster members and sends aggregated data to the base station or another CH. This article proposes a new clustering algorithm (EMESISC) that is based on each node's probability of becoming a CH. This node's probability depends on its residual energy, buffer length, and received signal power. We compared EMESISC with LEACH algorithm. Simulation results showed that EMESISC is superior to LEACH.

Salp

2020 ◽  
pp. 41-56 ◽  
Author(s):  
Vrajesh Kumar Chawra ◽  
Govind P. Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Network Lifetime ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Research Issue ◽  
Energy Hole ◽  
Node Clustering

The formation of the unequal clusters of the sensor nodes is a burning research issue in wireless sensor networks (WSN). Energy-hole and non-uniform load assignment are two major issues in most of the existing node clustering schemes. This affects the network lifetime of WSN. Salp optimization-based algorithm is used to solve these problems. The proposed algorithm is used for cluster head selection. The performance of the proposed scheme is compared with the two-node clustering scheme in the term of residual energy, energy consumption, and network lifetime. The results show the proposed scheme outperforms the existing protocols in term of network lifetime under different network configurations.

scholarly journals Routing and Clustering of Sensor Nodes in the Honeycomb Architecture

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Amine Rais ◽  
Khalid Bouragba ◽  
Mohammed Ouzzif
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Delivery ◽  
Hybrid Routing ◽  
Hybrid Routing Protocol ◽  
Hierarchical Protocols

Energy is the most valuable resource in wireless sensor networks; this resource is limited and much in demand during routing and communication between sensor nodes. Hierarchy structuring of the network into clusters allows reducing the energy consumption by using small distance transmissions within clusters in a multihop manner. In this article, we choose to use a hybrid routing protocol named Efficient Honeycomb Clustering Algorithm (EHCA), which is at the same time hierarchical and geographical protocol by using honeycomb clustering. This kind of clustering guarantees the balancing of the energy consumption through changing in each round the location of the cluster head, which is in a given vertex of the honeycomb cluster. The combination of geographical and hierarchical routing with the use of honeycomb clustering has proved its efficiency; the performances of our protocol outperform the existing protocols in terms of the number of nodes alive, the latency of data delivery, and the percentage of successful data delivery to the sinks. The simulations testify the superiority of our protocol against the existing geographical and hierarchical protocols.

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