An Energy-Efficient Layered Clustering Algorithm for Routing in Wireless Sensor Networks

2017 ◽  
Vol 8 (3) ◽  
pp. 43-66 ◽  
Author(s):  
Alphonse PJA ◽  
Sivaraj C ◽  
Janakiraman T N.
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Conservation ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Transmission Rate ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Simulation Experiments

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.

An Energy-Efficient Layered Clustering Algorithm for Routing in Wireless Sensor Networks

2020 ◽  
pp. 238-262
Author(s):  
P. J. A. Alphonse ◽  
C. Sivaraj ◽  
T. N. Janakiraman
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Conservation ◽  
Energy Management ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Transmission Rate ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor

Efficient energy management is a key issue in battery equipped wireless sensor networks (WSNs). The cluster based routing in WSNs is a prominent approach for energy conservation of the network which provides a hierarchical data collection mechanism. In order to maximize the energy conservation of sensor nodes, this paper proposes an Energy-efficient Layered Clustering Algorithm (ELCA) for routing in wireless sensor networks. ELCA constructs two layers of clusters to reduce the transmission rate and to balance the energy consumption of sensors. As early energy depletion of clusterheads (CHs) is a major limitation in clustering, this algorithm provides local remedy for energy suffering CHs through efficient CH substitution scheme. The performance of the proposed algorithm is analysed through extensive simulation experiments and verified by compared results with existing clustering algorithms.

scholarly journals An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy-Efficient Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Oluwasegun Julius Aroba ◽  
Nalindren Naicker ◽  
Timothy Adeliyi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Gaussian Elimination ◽  
Clustering Algorithms ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fast Time

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

An Energy-Efficient Multilevel Clustering Algorithm for Heterogeneous Wireless Sensor Networks

2011 ◽  
Vol 3 (3) ◽  
pp. 62-79
Author(s):  
Surender Soni ◽  
Vivek Katiyar ◽  
Narottam Chand
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Large Area ◽  
Topology Management ◽  
Clustering Protocol ◽  
Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are generally believed to be homogeneous, but some sensor nodes of higher energy can be used to prolong the lifetime and reliability of WSNs. This gives birth to the concept of Heterogeneous Wireless Sensor Networks (HWSNs). Clustering is an important technique to prolong the lifetime of WSNs and to reduce energy consumption as well, by topology management and routing. HWSNs are popular in real deployments (Corchado et al., 2010), and have a large area of coverage. In such scenarios, for better connectivity, the need for multilevel clustering protocols arises. In this paper, the authors propose an energy-efficient protocol called heterogeneous multilevel clustering and aggregation (HMCA) for HWSNs. HMCA is simulated and compared with existing multilevel clustering protocol EEMC (Jin et al., 2008) for homogeneous WSN. Simulation results demonstrate that the proposed protocol performs better.

An Energy-Efficient Multilevel Clustering Algorithm for Heterogeneous Wireless Sensor Networks

2012 ◽  
pp. 286-305
Author(s):  
Surender Soni ◽  
Vivek Katiyar ◽  
Narottam Chand
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Large Area ◽  
Topology Management ◽  
Clustering Protocol ◽  
Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are generally believed to be homogeneous, but some sensor nodes of higher energy can be used to prolong the lifetime and reliability of WSNs. This gives birth to the concept of Heterogeneous Wireless Sensor Networks (HWSNs). Clustering is an important technique to prolong the lifetime of WSNs and to reduce energy consumption as well, by topology management and routing. HWSNs are popular in real deployments (Corchado et al., 2010), and have a large area of coverage. In such scenarios, for better connectivity, the need for multilevel clustering protocols arises. In this paper, the authors propose an energy-efficient protocol called heterogeneous multilevel clustering and aggregation (HMCA) for HWSNs. HMCA is simulated and compared with existing multilevel clustering protocol EEMC (Jin et al., 2008) for homogeneous WSN. Simulation results demonstrate that the proposed protocol performs better.

A Clustering Algorithm for Wireless Sensor Networks Basing on Distance and Distribution

2011 ◽  
Vol 474-476 ◽  
pp. 1221-1227
Author(s):  
Ying Liao ◽  
Wei Xu Hao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hierarchical Network ◽  
The Common

Wireless sensor networks (WSNs) detect and monitor the outside physical state by the sensor nodes organizing automatically. Utilizing clustering algorithm to form hierarchical network topology is the common method which implements managing network and aggregating data in WSNs. Different from the previous clustering algorithms, this article proposes a clustering algorithm for WSNs based on distance and distribution to generate clusters considering residual energy of nods in WSNs with inhomogeneous distribution. The simulation result indicates that the algorithm can establish more balanceable clustering structure effectively and enhance the network life cycle obviously.<b></b>

scholarly journals EDCH: A Novel Clustering Algorithm for Wireless Sensor Networks

2019 ◽  
Vol 4 (3) ◽  
pp. 45-51
Author(s):  
Raj Kumar Pyage ◽  
H. G. Chandrakanth
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Main Role ◽  
Considerable Decrease

In wireless sensor networks, sensor nodes play the most important role. These sensor nodes are mainly un-chargeable, so it an issue regarding lifetime of the network.  The main objective of this research is concerning clustering algorithms to minimize the energy utilization of each sensor node, and maximize the sensor network lifetime of WSNs. In this paper, we propose a novel clustering algorithm for wireless sensor networks (WSN) that decrease the networks energy consumption and significantly prolongs its lifetime. Here main role play distribution of CHs ( Cluster Heads) across the network. Our simulation result shows considerable decrease in network energy utilization and therefore increase the network lifetime.  

scholarly journals DHCFL: Dual Head Clustering Based on Fuzzy Logic for Wireless Sensor Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 1049-1054
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Data Transmission ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor

Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios.

scholarly journals Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 478
Author(s):  
Xiao Yan ◽  
Cheng Huang ◽  
Jianyuan Gan ◽  
Xiaobei Wu
Keyword(s):  
Game Theory ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Sensor Node ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

UCAEE: Uniform Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Karuna Babber
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Minimum Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: The advent of wireless sensor networks makes it possible to track the events even in the remotest areas that too without human intervention. But severe resource constraints, generally energy of sensor nodes push researchers worldwide to develop energy efficient protocols in order to accomplish the application objectives of these networks. Objective: However, till date there is no energy efficient routing protocol which provides uniformity with maximum resource utilization for WSNs. Methods: In this paper, a Uniform Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (UCAEE) has been proposed. UCAEE is a base station controlled algorithm where entire sensing area is partitioned into uniform clusters. The motive of the algorithm is to split the sensing area into uniform clusters and to select cluster heads and gate-way nodes within each cluster so that the network energy can be balanced in a best possible way. Conclusion: UCAEE achieves minimum energy consumption during data transmission and reception. Results: Simulation results indicate that proposed UCAEE algorithm conserves more energy than its contemporary clustering algorithms like LEACH, PEGASIS and SECA and promises better network lifetime of wireless sensor networks.

scholarly journals GAECH: Genetic Algorithm Based Energy Efficient Clustering Hierarchy in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
B. Baranidharan ◽  
B. Santhi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Stability ◽  
Energy Efficient Clustering

Clustering the Wireless Sensor Networks (WSNs) is the major issue which determines the lifetime of the network. The parameters chosen for clustering should be appropriate to form the clusters according to the need of the applications. Some of the well-known clustering techniques in WSN are designed only to reduce overall energy consumption in the network and increase the network lifetime. These algorithms achieve increased lifetime, but at the cost of overloading individual sensor nodes. Load balancing among the nodes in the network is also equally important in achieving increased lifetime. First Node Die (FND), Half Node Die (HND), and Last Node Die (LND) are the different metrics for analysing lifetime of the network. In this paper, a new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function. The fitness function in GAECH forms well-balanced clusters considering the core parameters of a cluster, which again increases both the stability period and lifetime of the network. The experimental results also clearly indicate better performance of GAECH over other algorithms in all the necessary aspects.

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