Hybrid-learning based data gathering in wireless sensor networks

Mohammad Abdur Razzaque, Ismail Fauzi, Akhtaruzzaman Adnan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


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
Subtitle of host publication5th Asian Conference, ACIIDS 2013, Proceedings, Part II
EditorsAli Selamat, Ngoc Thanh Nguyen, Habibollah Haron
PublisherSpringer, Springer Nature
Number of pages10
ISBN (Electronic)978364235430
ISBN (Print)9783642365423
Publication statusPublished - 12 Mar 2013
Externally publishedYes

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Wireless Sensor Networks
  • Data Compression
  • Learning
  • Collaborative Learning


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