Distributed sensing with low-cost mobile sensors toward a sustainable IoT
Distributed sensing with low-cost mobile sensors toward a sustainable IoT
Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT.
96-102
Cetinkaya, Oktay
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Zaghari, Bahareh
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Bulot, Florentin M. J.
47870de2-3ba2-4425-b07a-16ce48ee3956
Damaj, Wissam
1cf14090-963a-40c0-99fe-b77dd30ed885
Jubb, Stephen A.
6ca22f68-3fa5-457f-9c98-bf09e1e9aef9
Stein, Sebastian
fb227373-7242-4982-b84b-90bc79617a50
Weddell, Alex S.
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Mayfield, Martin
6edd32cb-ef5d-495b-a5ca-7e826de33df0
Beeby, Steve
ba565001-2812-4300-89f1-fe5a437ecb0d
Cetinkaya, Oktay
6cb457a5-77b8-415d-b524-9e8728c35f0a
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
Bulot, Florentin M. J.
47870de2-3ba2-4425-b07a-16ce48ee3956
Damaj, Wissam
1cf14090-963a-40c0-99fe-b77dd30ed885
Jubb, Stephen A.
6ca22f68-3fa5-457f-9c98-bf09e1e9aef9
Stein, Sebastian
fb227373-7242-4982-b84b-90bc79617a50
Weddell, Alex S.
3d8c4d63-19b1-4072-a779-84d487fd6f03
Mayfield, Martin
6edd32cb-ef5d-495b-a5ca-7e826de33df0
Beeby, Steve
ba565001-2812-4300-89f1-fe5a437ecb0d
Cetinkaya, Oktay, Zaghari, Bahareh, Bulot, Florentin M. J., Damaj, Wissam, Jubb, Stephen A., Stein, Sebastian, Weddell, Alex S., Mayfield, Martin and Beeby, Steve
(2021)
Distributed sensing with low-cost mobile sensors toward a sustainable IoT.
IEEE Internet of Things Magazine, 4 (3), .
(doi:10.1109/IOTM.0101.2100007).
Abstract
Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT.
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e-pub ahead of print date: 7 September 2021
Identifiers
Local EPrints ID: 476851
URI: http://eprints.soton.ac.uk/id/eprint/476851
ISSN: 2576-3180
PURE UUID: c6b75d64-cd68-4627-b873-c27c2d8d2e0a
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Date deposited: 17 May 2023 17:07
Last modified: 21 Sep 2024 01:54
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Contributors
Author:
Oktay Cetinkaya
Author:
Bahareh Zaghari
Author:
Florentin M. J. Bulot
Author:
Wissam Damaj
Author:
Stephen A. Jubb
Author:
Sebastian Stein
Author:
Alex S. Weddell
Author:
Martin Mayfield
Author:
Steve Beeby
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