WSN Cluster Head Selection Algorithm Based on Neural Network

Guo, Liang; Chen, Fangxin; Dai, Zhicheng; Liu, Zhuo

doi:10.1109/mvhi.2010.147

Cited by 13 publications

(14 citation statements)

References 5 publications

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“…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%

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: 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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“…Our proposed algorithm is a centralized cluster based protocol strongly related to LEACH-Centralized (LEACH-C) (Guo et al, 2010) and LEA2C (Enami et al, 2010) protocols. The operation of the algorithm is divided into rounds in a similar way to LEACH-C. Each round begins with a cluster setup phase, in which cluster organization takes place, followed by a data transmission phase, in which data from the simple nodes are transferred to the cluster heads.…”

Section: Algorithm Assumptionsmentioning

confidence: 99%

“…Certainly after each data transmission phase, the next cluster head will be a different node (cluster head rotation). After determining cluster head nodes, BS assign appropriate roles to all nodes by sending messages containing related cluster head ID as in LEACH-C (Guo et al, 2010).…”

Section: Three Criteriamentioning

confidence: 99%

Two Phase Clustering Method for Leach Protocol for Effective Cluster Head Selection

Rathi¹,

Viswanathan²

2014

Journal of Computer Science

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A Wireless Sensor Networks (WSN) comprise of a huge quantity of clusters of sensor nodes that can inter communicate with all nodes and with the base station through a wireless link. All clusters are corresponding to a cluster head to provide a direct communication to the base station and in addition with other cluster heads. Obviously well-organized selection of cluster head is a significant problem in the performance of the wireless sensor network. In this study, a novel two stage clustering protocol based on Self Organizing Map (SOM) neural network and Modified Fuzzy Possibilistic Clustering Algorithm (MFPCM) with the purpose of balancing the energy consumption. The clustering is carried out based on two important condition, energy level and coordinates of sensor nodes. Consequently, this novel two stage clustering method can prevent from premature death of the nodes and permit for random death of them. The experimental observation reveals that the SOM-MFPCM based clusterhead selection algorithm performs better than the cluster head election approach used in LEACH and can enable the network lifetime longer with less number of dead nodes.

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“…The clustered approach is considered a more sustainable option [20] and has already been widely adopted for the organization of WSNs because of its advantages over non-clustered, centralized schemes in terms of reliability, robustness, scalability, network lifetime, and so forth [61][62][63][64][65][66]. With clustering, the network is segregated into several clusters that consist of subsets of nodes, establishing a hierarchical structure.…”

Section: Cooperation Architecturesmentioning

confidence: 99%

Adaptive Communication Techniques for the Internet of Things

Roussos

2013

JSAN

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Abstract:The vision for the Internet of Things (IoT) demands that material objects acquire communications and computation capabilities and become able to automatically identify themselves through standard protocols and open systems, using the Internet as their foundation. Yet, several challenges still must be addressed for this vision to become a reality. A core ingredient in such development is the ability of heterogeneous devices to communicate adaptively so as to make the best of limited spectrum availability and cope with competition which is inevitable as more and more objects connect to the system. This survey provides an overview of current developments in this area, placing emphasis on wireless sensor networks that can provide IoT capabilities for material objects and techniques that can be used in the context of systems employing low-power versions of the Internet Protocol (IP) stack. The survey introduces a conceptual model that facilitates the identification of opportunities for adaptation in each layer of the network stack. After a detailed discussion of specific approaches applicable to particular layers, we consider how sharing information across layers can facilitate further adaptation. We conclude with a discussion of future research directions.

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WSN Cluster Head Selection Algorithm Based on Neural Network

Cited by 13 publications

References 5 publications

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

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

Two Phase Clustering Method for Leach Protocol for Effective Cluster Head Selection

Adaptive Communication Techniques for the Internet of Things

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