Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105593
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Wang, T | - |
dc.creator | Li, Y | - |
dc.creator | Wang, G | - |
dc.creator | Cao, J | - |
dc.creator | Bhuiyan, MZA | - |
dc.creator | Jia, W | - |
dc.date.accessioned | 2024-04-15T07:35:15Z | - |
dc.date.available | 2024-04-15T07:35:15Z | - |
dc.identifier.issn | 2377-3782 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105593 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication T. Wang, Y. Li, G. Wang, J. Cao, M. Z. A. Bhuiyan and W. Jia, "Sustainable and Efficient Data Collection from WSNs to Cloud," in IEEE Transactions on Sustainable Computing, vol. 4, no. 2, pp. 252-262, 1 April-June 2019 is available at https://doi.org/10.1109/TSUSC.2017.2690301. | en_US |
dc.subject | Data delivery | en_US |
dc.subject | Energy consumption | en_US |
dc.subject | Mobile sinks | en_US |
dc.subject | Sensor-cloud | en_US |
dc.subject | Sustainability | en_US |
dc.title | Sustainable and efficient data collection from WSNs to cloud | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 252 | - |
dc.identifier.epage | 262 | - |
dc.identifier.volume | 4 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1109/TSUSC.2017.2690301 | - |
dcterms.abstract | The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on sustainable computing, Apr.-June 2019, v. 4, no. 2, p. 252-262 | - |
dcterms.isPartOf | IEEE transactions on sustainable computing | - |
dcterms.issued | 2019-04 | - |
dc.identifier.scopus | 2-s2.0-85075886575 | - |
dc.identifier.eissn | 2377-3790 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0646 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Chinese National Research Fund (NSFC) Key Project; National China 973 Project; Shanghai Scientific Innovation Act of STCSM; 985 Project of Shanghai Jiao Tong University; National Natural Science Foundation (NSF) of China; Operation Platform and the Foster Project for Graduate Student in Research and Innovation of Huaqiao University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20675045 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Cao_Sustainable_Efficient_Data.pdf | Pre-Published version | 1.6 MB | Adobe PDF | View/Open |
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