A novel approach for finding optimal number of cluster head in wireless sensor network

Ranjan, Ravi; Kar, Subrat

doi:10.1109/ncc.2011.5734751

Cited by 9 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [29] two deployment scenarios, a 2d homogeneous spatial Poisson point process was used for the random deployment model for homogeneous sensor networks and deterministic deployment. In these scenarios average energy spend in WSN at each round is calculated in order to find the optimal probability of a node to a CH thereby the optimal number of CHs is determined.…”

Section: A Non-ai-based Clustering Methodsmentioning

confidence: 99%

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

View full text Add to dashboard Cite

The rapid advancement in Wireless Sensor Network (WSN) technology has enabled smart environments to provide ubiquitous real-time applications in various fields such as industry, smart city, transport, health and Internet of Things (IoT). Energy is the most significant resource in WSNs as it has a direct effect on their lifetime. The efficient use of energy is required for the lifetime extension of WSNs. One of the well-known methods for achieving high scalability and efficient resource allocation in WSN is a clustering of sensor nodes. In this paper, the Chicken Swarm Optimization based Clustering Algorithm (CSOCA) is proposed to improve energy efficiency in WSNs. The chicken swarm optimization is discretized by applying a sigmoid function to individuals. Moreover, we proposed CSOCA with Genetic Algorithm (CSOCA-GA) which is an improvement to CSOCA by employing the Genetic Algorithm's processes in CSOCA. CSOCA-GA utilizes crossover and mutation processes for individuals with low fitness value to extend the population diversity. CSOCA and CSOCA-GA are tested and compared with other similar algorithms to confirm their effectiveness in terms of extending WSN lifetime and reducing energy consumption. INDEX TERMS Wireless sensor network, clustering, Internet of Things, genetic algorithm, chicken swarm optimization.

show abstract

Section: A Non-ai-based Clustering Methodsmentioning

confidence: 99%

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

View full text Add to dashboard Cite

show abstract

“…Then, CH selection approach using spatial Poisson process is proposed with randomity and predictable deployment in homogeneous sensor networks. 35 This CH selection approach determines optimal node probability associated with sensor nodes for the purpose of segregating potential nodes in the network. It mainly uses mean energy consumption for CH selection at the end of each round.…”

Section: Traditional Ch Selection Schemesmentioning

confidence: 99%

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Balamurugan

Janakiraman

Priya

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are capable of offering data dissemination among the nodes such that the exploration of a network's potential could be performed based on the frequency range. It is highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issues of network lifetime, coverage, scheduling and data aggregation. Prolonging network lifetime confirms energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation.Clustering schemes are highly suitable for effectively utilizing the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. This problem of energy-aware optimization included in the clustering process is an NP optimization problem. At this juncture, metaheuristic optimization algorithms are potential candidates for optimizing energy that attributes towards predominant sustenance in network lifetime. In this article, a modified African buffalo and group teaching optimization algorithm (MABGTOA) is proposed for achieving energy stability and maintaining network lifetime by efficient cluster head (CH) selection during the process of clustering. This MABGTOA scheme is developed for sustaining the tradeoff existing between the rate of exploitation and exploration that aids in efficient selection of CHs, thus maintaining lifetime and energy stability in the network. The simulation experiments of the proposed MABGTOA confirm its predominance by offering increased throughput by 18.21% and sustenance in residual energy by 15.48% when compared to the benchmarked schemes taken for comparative investigation.

show abstract

“…Hence it is recommended to use the minimum amount of energy for all sensor operations including sensing, processing and communication in order to prolong the life of sensor networks. Ranjan and Kar [18] have provided a method for determining the optimal number of cluster heads for homogeneous sensor networks using reasonable energy consumption model.…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Security Implementations in Smart Sensor Networks

Firdhous

2016

Trends in Ambient Intelligent Systems

View full text Add to dashboard Cite

Wireless sensor networks have become one of the widely deployed networking technologies in the recent times due to the capabilities and advantages of them. The applications of wireless sensor networks include many civilian and industrial applications to military applications. Due to the distributed nature of these networks, deployment in remote and open areas and many constraints in individual nodes, these networks are vulnerable to several security threats. Many security mechanisms and algorithms proposed for the implementation in the traditional networks cannot be implemented in wireless sensor networks due to the unique nature of these networks and nodes. Many active research programmes have been carried out throughout the world for making wireless sensor networks more secure and user friendly. This chapter takes an in-depth look at some of the prominent mechanisms, schemes, algorithms and protocols published in the literature.

show abstract

A novel approach for finding optimal number of cluster head in wireless sensor network

Cited by 9 publications

References 6 publications

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Security Implementations in Smart Sensor Networks

Contact Info

Product

Resources

About