This paper presents an intelligent strategy to track the moving nodes in wireless sensor networksWireless Sensor Network (WSN) to increase energy efficiency and tracking accuracy. The immediate location of nodes in WSN is of great importance in many applications. However, the power and cost at the various communication layers for the node localization has arisen as a critical difficulty. To improve the energy performance of the network’s power-deprived sensorSensors nodes, a distributed event-based adaptive target tracking technique is proposed. Unlike traditional target tracking systems, this research addresses the functional and parametric uncertainties introduced in the network dynamicsDynamics resulted from multi-path fading, reflections, and unknown disturbances. These uncertainties are assessed using a neural networkNeural Networks framework with a wavelet kernel function that has localized time–frequency estimation features. The usage of a wavelet neural networkNeural Networks (WNN) to accurately identify uncertainty improves the tracking algorithm’s robustness. This research derives the appropriate neural networkNeural Networks tuning principles for training and accurate target tracking. A simulationSimulation study has been performed in the research work to ensure that the recommended approach is effective and accurate.


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    Titel :

    Self-Recurrent Neural Network-Based Event-Triggered Mobile Object Tracking Strategy for Sensor Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Bagal, Vishwalata (Autor:in) / Patil, A. V. (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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