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  • 1.
    Ahmad, Sabtain
    et al.
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Uyanık, Halit
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Ovatman, Tolga
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Sandıkkaya, Mehmet Tahir
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    De Maio, Vincenzo
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Brandić, Ivona
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Aral, Atakan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sustainable environmental monitoring via energy and information efficient multi-node placement2023In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 24, p. 22065-22079Article in journal (Refereed)
    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.

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