Neural Network Enabled WSN Management for Energy Efficient Routing Mechanism

Kamila, Nilayam Kumar; Dhal, Sunil Kumar; Nayak, Bhagirathi

doi:10.17485/ijst/2016/v9i26/89824

Cited by 8 publications

(2 citation statements)

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“…EOMR protocol also balanced the load & thus throughput is increases which lead to maximum life span of the network and less delay to end to end in the network. In [18], the authors have proposed NNEWM (neural network enabled WSN management) model for increasing the energy efficiency in wireless sensor networks. This proposed model addressed the issues of reverse transmission, load sharing and self-silence with integration of techniques such as selforganizing map (SOM), k-means algorithm and cluster head management.…”

Section: Related Workmentioning

confidence: 99%

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Singh

Pareek²

2022

Int. j. commun. netw. inf. secur.

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Energy in a wireless sensor network (WSN) is rendered as the major constraint that affects the overall feasibility and performance of a network. With the dynamic and demanding requirements of diverse applications, the need for an energy efficient network persists. Therefore, this paper proposes a mechanism for optimizing the energy consumption in WSN through the integration of artificial neural networks (ANN) and Kohonen self-organizing map (KSOM) techniques. The clusters are formed and re-located after iteration for effective distribution of energy and reduction of energy depletion at individual nodes. Furthermore, back propagation algorithm is used as a supervised learning method for optimizing the approach and reducing the loss function. The simulation results show the effectiveness of the proposed energy efficient network.

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Section: Related Workmentioning

confidence: 99%

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Singh

Pareek²

2022

Int. j. commun. netw. inf. secur.

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“…Krishna, Niranjan and Shireesha [13] suggested an ML-based clustering strategy for efficiently assigning sensors to the closest cluster. Kumar Kamila, Dhal and Nayak [14] employed machine learning to improve the routing mechanism in a WSN. The envisaged routing algorithm aims to extend lifetime of the network and transfer information packets in the shortest possible time.…”

Section: Routingmentioning

confidence: 99%

Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms

Leonid

2022

JCNS

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Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.

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Managing Data Transmission To Improve Lifetime of Wireless Sensor Network

Abbas

Khanjar

2019

2019 First International Conference of Computer and Applied Sciences (CAS)

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Neural Network Enabled WSN Management for Energy Efficient Routing Mechanism

Cited by 8 publications

References 0 publications

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms

Managing Data Transmission To Improve Lifetime of Wireless Sensor Network

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