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Sustainable environmental monitoring via energy and information efficient multi-node placement
Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.ORCID-id: 0000-0003-2665-2085
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.ORCID-id: 0000-0001-5918-3145
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.ORCID-id: 0000-0002-9756-603X
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2023 (Engelska)Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, nr 24, s. 22065-22079Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information- and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.

Ort, förlag, år, upplaga, sidor
IEEE, 2023. Vol. 10, nr 24, s. 22065-22079
Nyckelord [en]
Environmental monitoring, sensor placement, gateway configuration, wireless sensor networks, LoRaWAN, energy efficiency, multi-objective optimization, QR decomposition
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-213028DOI: 10.1109/jiot.2023.3303124Scopus ID: 2-s2.0-85167805834OAI: oai:DiVA.org:umu-213028DiVA, id: diva2:1789421
Tillgänglig från: 2023-08-19 Skapad: 2023-08-19 Senast uppdaterad: 2024-01-09Bibliografiskt granskad

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