Hybrid-learning based data gathering in wireless sensor networks

Mohammad Abdur Razzaque, Ismail Fauzi, Akhtaruzzaman Adnan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Prediction based data gathering or estimation is a very frequent phenomenon in wireless sensor networks (WSNs). Learning and model update is in the heart of prediction based data gathering. A majority of the existing prediction based data gathering approaches consider centralized and some others use localized and distributed learning and model updates. Our conjecture in this work is that no single learning approach may not be optimal for all the sensors within a WSN, especially in large scale WSNs. For, example for source nodes, which are very close to sink, centralized learning could be better compared to distributed one and vice versa for the further nodes. In this work, we explore the scope of possible hybrid (centralized and distributed) learning scheme for prediction based data gathering in WSNs. Numerical experimentations with two sensor datasets and their results of the proposed scheme, show the potential of hybrid approach.

    Original languageEnglish
    Title of host publicationIntelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
    Number of pages10
    EditionPART 2
    Publication statusPublished - 11 Mar 2013
    Event5th Asian Conference on Intelligent Information and Database Systems - Kuala Lumpur, Malaysia
    Duration: 18 Mar 201320 Mar 2013
    Conference number: 5

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume7803 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference5th Asian Conference on Intelligent Information and Database Systems
    Abbreviated titleACIIDS 2013
    CityKuala Lumpur


    Dive into the research topics of 'Hybrid-learning based data gathering in wireless sensor networks'. Together they form a unique fingerprint.

    Cite this