Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things
With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to e...
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| Format: | Article |
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MDPI AG
2017-10-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/8/4/123 |
| _version_ | 1819157277450436608 |
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| author | Amany Abu Safia Zaher Al Aghbari Ibrahim Kamel |
| author_facet | Amany Abu Safia Zaher Al Aghbari Ibrahim Kamel |
| author_sort | Amany Abu Safia |
| collection | DOAJ |
| description | With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors’ lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks’ nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs’ locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss. |
| first_indexed | 2024-12-22T16:06:12Z |
| format | Article |
| id | doaj.art-06465f016911422f92ba503249c3aaf8 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-22T16:06:12Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-06465f016911422f92ba503249c3aaf82022-12-21T18:20:36ZengMDPI AGInformation2078-24892017-10-018412310.3390/info8040123info8040123Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of ThingsAmany Abu Safia0Zaher Al Aghbari1Ibrahim Kamel2Department of Computer Science, University of Sharjah, P.O. Box 27272 Sharjah, UAEDepartment of Computer Science, University of Sharjah, P.O. Box 27272 Sharjah, UAEDepartment of Electrical & Computer Engineering, University of Sharjah, P.O. Box 27272 Sharjah, UAEWith the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors’ lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks’ nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs’ locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.https://www.mdpi.com/2078-2489/8/4/123mobile wireless sensor networksphenomena detectionmobile sinkenergy-efficient algorithmIoT |
| spellingShingle | Amany Abu Safia Zaher Al Aghbari Ibrahim Kamel Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things Information mobile wireless sensor networks phenomena detection mobile sink energy-efficient algorithm IoT |
| title | Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things |
| title_full | Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things |
| title_fullStr | Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things |
| title_full_unstemmed | Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things |
| title_short | Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things |
| title_sort | efficient data collection by mobile sink to detect phenomena in internet of things |
| topic | mobile wireless sensor networks phenomena detection mobile sink energy-efficient algorithm IoT |
| url | https://www.mdpi.com/2078-2489/8/4/123 |
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