Using self organizing map in wireless sensor network for designing energy efficient topologies

Patra, Chiranjib; Chattopadhyay, Matangini; Bhaumik, Parama; Roy, Anjan Guha

doi:10.1109/wirelessvitae.2011.5940819

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…The cell form enhances both the energy consumption and channel recycles. Patra et al, (2011) proposed the application of KSOM technique to cluster nodes in a heterogeneous sensor network that enhanced the node transmission power to enhance the energy saving in the network. Chen et al, (2014) proposed that the well-constructed network topology offers a vital support for target tracking, data fusion and routing in WSNs.…”

Section: Related Workmentioning

confidence: 99%

K-Means Clustering in WSN with Koheneon SOM and Conscience Function

Bataineh¹,

Samkari²,

Abdualla³

et al. 2019

MAS

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Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map (KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

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Section: Related Workmentioning

confidence: 99%

K-Means Clustering in WSN with Koheneon SOM and Conscience Function

Bataineh¹,

Samkari²,

Abdualla³

et al. 2019

MAS

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“…Cordina and Debono proposed a clustering routing algorithm based on Fuzzy ART neural network, which combines neural networks and fuzzy adaptive resonance theory to optimize the routing of network nodes, and balanced network energy consumption and prolonged the network lifetime [12]. Patra et al took advantage of SOFM neural networks for node clustering in a heterogeneous sensor network, which optimized node transmission power to improve the energy-saving performance of the routing through the network training, and combined neural network to improve the robustness of the network topology [13,14].…”

Section: Related Workmentioning

confidence: 99%

SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

Chen

Yue

2014

Journal of Sensors

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Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.

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