Hybrid-learning based data gathering in wireless sensor networks
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...
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| Format: | Conference or Workshop Item |
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2013
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| _version_ | 1796859576778227712 |
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| author | Razzaque, M. A. Fauzi, I. Adnan, A. |
| author_facet | Razzaque, M. A. Fauzi, I. Adnan, A. |
| author_sort | Razzaque, M. A. |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T19:29:13Z |
| format | Conference or Workshop Item |
| id | utm.eprints-51109 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:29:13Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | utm.eprints-511092017-09-17T07:19:34Z http://eprints.utm.my/51109/ Hybrid-learning based data gathering in wireless sensor networks Razzaque, M. A. Fauzi, I. Adnan, A. QA75 Electronic computers. Computer science 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. 2013 Conference or Workshop Item PeerReviewed Razzaque, M. A. and Fauzi, I. and Adnan, A. (2013) Hybrid-learning based data gathering in wireless sensor networks. In: Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). https://doi.org/10.1007/978-3-642-36543-0_10 |
| spellingShingle | QA75 Electronic computers. Computer science Razzaque, M. A. Fauzi, I. Adnan, A. Hybrid-learning based data gathering in wireless sensor networks |
| title | Hybrid-learning based data gathering in wireless sensor networks |
| title_full | Hybrid-learning based data gathering in wireless sensor networks |
| title_fullStr | Hybrid-learning based data gathering in wireless sensor networks |
| title_full_unstemmed | Hybrid-learning based data gathering in wireless sensor networks |
| title_short | Hybrid-learning based data gathering in wireless sensor networks |
| title_sort | hybrid learning based data gathering in wireless sensor networks |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT razzaquema hybridlearningbaseddatagatheringinwirelesssensornetworks AT fauzii hybridlearningbaseddatagatheringinwirelesssensornetworks AT adnana hybridlearningbaseddatagatheringinwirelesssensornetworks |