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UAV-Based IoT Platform: A Crowd Surveillance Use Case.

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Title: UAV-Based IoT Platform: A Crowd Surveillance Use Case.
Authors: Motlagh, Naser Hossein, Bagaa, Miloud, Taleb, Tarik
Source: IEEE Communications Magazine. Feb2017, Vol. 55 Issue 2, p128-134. 7p.
Subjects: Drone aircraft control systems, Mobile computing equipment, Internet of things, Electronic surveillance, Human facial recognition software, Equipment & supplies
Abstract: Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Communications Magazine is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Drone+aircraft+control+systems%22">Drone aircraft control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+computing+equipment%22">Mobile computing equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+surveillance%22">Electronic surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Equipment+%26+supplies%22">Equipment & supplies</searchLink>
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  Data: Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Communications Magazine is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/MCOM.2017.1600587CM
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        Text: English
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      – SubjectFull: Mobile computing equipment
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      – SubjectFull: Internet of things
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      – SubjectFull: Electronic surveillance
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      – SubjectFull: Human facial recognition software
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              Text: Feb2017
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