Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks

RejinaParvin, J.; Vasanthanayaki, C.

doi:10.1109/jsen.2015.2416208

Cited by 128 publications

(34 citation statements)

References 22 publications

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“…The study has also used particle swarm optimization for topology construction. A similar form of usage of particle swarm optimization was also seen in the work of Parvin and Vasanthanayaki . Another frequently used approach towards clustering is to consider the mobility of the sink node in WSN.…”

Section: Related Workmentioning

confidence: 93%

“…After reviewing the existing mechanism of clustering optimization, the following are the open‐end research problems identified: Adoption of iterative approach: Majority of the existing mechanism have used iterative mechanism towards performing clustering operation. Multiple rounds of similar operation are carried out in recursive order for finding the best cluster head is witnessed.…”

Section: Research Problemmentioning

confidence: 99%

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Interstellar‐based topology control scheme for optimal clustering performance in WSN

Jyothi

Usha

2020

Int J Communication

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Summary Clustering is one of the essential operations in wireless sensor network (WSN) to ensure organized data aggregation followed by energy efficiency. However, obtaining optimal clustering performance is yet an unsolved problem in WSN. Review of existing approaches towards cluster optimization shows that effective balance between energy efficiency and topology control is still missing. Therefore, the proposed system presents a unique topology control mechanism using a novel concept of interstellar orientation toward optimizing the clustering performance in WSN. Adopting an analytical research methodology, the proposed system introduces two interstellar‐based topology control system, which targets the maximum saving of resource consumption of the cluster head. The simulated outcome of the study shows that the proposed topology control system offers significant energy conservation performance in comparison to the existing hierarchical clustering scheme in WSN.

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Section: Related Workmentioning

confidence: 93%

Section: Research Problemmentioning

confidence: 99%

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Jyothi

Usha

2020

Int J Communication

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“…Suganthi and Rajagopalan [11] have applied PSO in its continuous state, but they suggested using a multiple population swarm instead of using the standard PSO with one population. Rejina Parvin and Vasanthanayaki [12] used PSO to prevent residual nodes in wireless sensor networks (nodes that don"t belong to any cluster). Their idea has been applied to optimize energy consumption, throughput, packet delivery ratio, and network lifetime of the wireless sensor networks.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Boulouard¹,

Haddadi²,

Bouhafer³

et al. 2018

IJECE

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Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, "Bat-Cluster (BC)." This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

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“…Issues of maintaining higher degree of fault tolerance while scheduling the allocation process of task can be also handled by PSO as seen in the work carried out by Guo et al [14]. The work carried out by Parvin [15] has used PSO for overcoming non-participation process of nodes during aggregation process. Wu and Lin [16] have investigated the effect of PSO for exploring the specific absorption rate of wireless body area network.…”

Section: Introductionmentioning

confidence: 99%

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

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Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency. [5] etc. There has been a voluminous amount of investigation carried out in this area for addressing such issues, but still majority of the issues are yet to meet its full-proof solution. The primary cause of this problem is basically the sensor node, which is very small in size, posses low computational capabilities, and powered by a battery that has limited lifetime. The biggest challenge for the researchers to prove the applicability of their presented system in real-time sensor node that is not found to be discussed in majority of the existing research work. However, some of the researchers avoid such problems by considering either benchmark test bed, or adopt the configuration of some real-time motes e.g. Berkley Mote, MicaZ mote [6], etc. Hence, optimization is the best possibility in such scenario of node resources constraints. There are various methods by which optimizations have been carried out towards improving the performance of sensor node e.g. neural network, genetic algorithm, swarm intelligences etc [7]. Particle Swarm Optimization (PSO) is one such technique that uses many numbers of iterations in order to explore the best solution against the problems posed [8], [9]. From computational viewpoint, PSO enhances the candidate solution in the perspective of the anticipated outcomes and given problem in wireless sensor network. The problem is optimized by considering the candidate solution and its population (also called as particles) and this form of the candidate solution is subjected to iterative processing in order to obtain personal and global best outcome from the position and velocity of the particles. The primary reason behind the adoption of PSO is basically its intelligence-based approach that can be possibly implemented on any of Keyword: Clustering

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Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks

Cited by 128 publications

References 22 publications

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

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