Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners
<p dir="ltr">Internet of Things applications can greatly benefit from accurate prediction models. The performance of prediction models is highly dependent on the quantity and quality of their training data. In this paper, we investigate the creation of a dynamic ensemble from distrib...
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| مؤلفون آخرون: | |
| منشور في: |
2018
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513526006022144 |
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| author | Mehdi Mohammadi (5024105) |
| author2 | Ala Al-Fuqaha (4434340) |
| author2_role | author |
| author_facet | Mehdi Mohammadi (5024105) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mehdi Mohammadi (5024105) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2018-10-21T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2018.2877153 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Exploiting_the_Spatio-Temporal_Patterns_in_IoT_Data_to_Establish_a_Dynamic_Ensemble_of_Distributed_Learners/25304245 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Materials engineering Data models Predictive models Training data Load modeling Computational modeling Sensors Distributed deep neural networks spatio-temporal analysis ensemble deep learning bloom filter Internet of Things smart city |
| dc.title.none.fl_str_mv | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Internet of Things applications can greatly benefit from accurate prediction models. The performance of prediction models is highly dependent on the quantity and quality of their training data. In this paper, we investigate the creation of a dynamic ensemble from distributed deep learning models by considering the spatiotemporal patterns embedded in the training data. Our dynamic ensemble does not depend on offline configurations. Instead, it exploits the spatiotemporal patterns embedded in the training data to generate dynamic weights for the underlying weak distributed deep learners to create a stronger learner. Our evaluation experiments using three real-world datasets in the context of the smart city show that our proposed dynamic ensemble strategy leads to an improved error rate of up to 33% compared to the baseline strategy even when using31of the training data. Moreover, using only 20% of the training data, the error rate of the model slightly increased by up to 2 in terms of mean square error. This increase is 82% less than the 11.3 increase seen in the baseline model. Therefore, our approach contributes to the reduced network traffic while not hindering the accuracy significantly.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2018.2877153" target="_blank">https://dx.doi.org/10.1109/access.2018.2877153</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0e5ac34dd7b24bd02a2d77990a686f2c |
| identifier_str_mv | 10.1109/access.2018.2877153 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25304245 |
| publishDate | 2018 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed LearnersMehdi Mohammadi (5024105)Ala Al-Fuqaha (4434340)EngineeringMaterials engineeringData modelsPredictive modelsTraining dataLoad modelingComputational modelingSensorsDistributed deep neural networksspatio-temporal analysisensemble deep learningbloom filterInternet of Thingssmart city<p dir="ltr">Internet of Things applications can greatly benefit from accurate prediction models. The performance of prediction models is highly dependent on the quantity and quality of their training data. In this paper, we investigate the creation of a dynamic ensemble from distributed deep learning models by considering the spatiotemporal patterns embedded in the training data. Our dynamic ensemble does not depend on offline configurations. Instead, it exploits the spatiotemporal patterns embedded in the training data to generate dynamic weights for the underlying weak distributed deep learners to create a stronger learner. Our evaluation experiments using three real-world datasets in the context of the smart city show that our proposed dynamic ensemble strategy leads to an improved error rate of up to 33% compared to the baseline strategy even when using31of the training data. Moreover, using only 20% of the training data, the error rate of the model slightly increased by up to 2 in terms of mean square error. This increase is 82% less than the 11.3 increase seen in the baseline model. Therefore, our approach contributes to the reduced network traffic while not hindering the accuracy significantly.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2018.2877153" target="_blank">https://dx.doi.org/10.1109/access.2018.2877153</a></p>2018-10-21T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2018.2877153https://figshare.com/articles/journal_contribution/Exploiting_the_Spatio-Temporal_Patterns_in_IoT_Data_to_Establish_a_Dynamic_Ensemble_of_Distributed_Learners/25304245CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/253042452018-10-21T09:00:00Z |
| spellingShingle | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners Mehdi Mohammadi (5024105) Engineering Materials engineering Data models Predictive models Training data Load modeling Computational modeling Sensors Distributed deep neural networks spatio-temporal analysis ensemble deep learning bloom filter Internet of Things smart city |
| status_str | publishedVersion |
| title | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| title_full | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| title_fullStr | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| title_full_unstemmed | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| title_short | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| title_sort | Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners |
| topic | Engineering Materials engineering Data models Predictive models Training data Load modeling Computational modeling Sensors Distributed deep neural networks spatio-temporal analysis ensemble deep learning bloom filter Internet of Things smart city |