scholarly journals ANCAEE: A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

2011 ◽  
Vol 03 (09) ◽  
pp. 307-312 ◽  
Author(s):  
A. P. Abidoye ◽  
N. A. Azeez ◽  
A. O. Adesina ◽  
K. K. Agbele
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Wireless Sensor

A Cluster Algorithm for Wireless Sensor Networks Based on Energy Efficiency

2015 ◽  
Vol 785 ◽  
pp. 744-750
Author(s):  
Lei Gao ◽  
Qun Chen
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Selection Criterion ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Head Node

In order to solve the energy limited problem of sensor nodes in the wireless sensor networks (WSN), a fast clustering algorithm based on energy efficiency for wire1ess sensor networks is presented in this paper. In the system initialization phase, the deployment region is divided into several clusters rapidly. The energy consumption ratio and degree of the node are chosen as the selection criterion for the cluster head. Re-election of the cluster head node at this time became a local trigger behavior. Because of the range of the re-election is within the cluster, which greatly reduces the complexity and computational load to re-elect the cluster head node. Theoretical analysis indicates that the timing complexity of the clustering algorithm is O(1), which shows that the algorithm overhead is small and has nothing to do with the network size n. Simulation results show that clustering algorithm based on energy efficiency can provide better load balancing of cluster heads and less protocol overhead. Clustering algorithm based on energy efficiency can reduce energy consumption and prolong the network lifetime compared with LEACH protocol.

Proficient Clustering algorithm for Wireless Sensor Networks

2020 ◽  
pp. 249-261
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

scholarly journals Clustering Algorithm of Wireless Sensor Networks Based on Residual Energy

2012 ◽  
Vol 6-7 ◽  
pp. 831-835
Author(s):  
Chang Lin Ma ◽  
Yuan Ruan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Factor ◽  
Leach Protocol ◽  
Limited Power

In order to improve the lifetime and throughput of wireless sensor networks under the limited power, an improved clustering algorithm is proposed in this paper on the basis of LEACH protocol. The energy factor is considered in this algorithm. The residual energy of all sensor nodes is referred to select cluster-heads of wireless sensor networks. The new clustering algorithm effectively improves the energy efficiency, throughput and lifetime of wireless sensor networks. The results are proved by simulations.

Proficient Clustering algorithm for Wireless Sensor Networks

2017 ◽  
pp. 345-357
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

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.

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

2017 ◽  
Vol 7 (8) ◽  
pp. 169
Author(s):  
A. Radhika ◽  
D. Haritha
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Congestion Aware

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of  importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources . Sensor nodes are prone to failure and the misbehaviour of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN.  Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol  with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.

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