Sustainable Environmental Monitoring via Energy and Information Efficient Multinode Placement

• Published in: IEEE internet of things journal Vol. 10; no. 24; pp. 22065 - 22079
• Main Authors: Ahmad, Sabtain, Uyanık, Halit, Ovatman, Tolga, Sandıkkaya, Mehmet Tahir, De Maio, Vincenzo, Brandić, Ivona, Aral, Atakan
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 15.12.2023
• Subjects: Communication; Computer networks; Data collection; Energy management; Environmental monitoring; Internet of Things; Optimization; Placement; Renewable energy; Sampling; Sensors; Water quality
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3303124

The Internet of Things (IoT) 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 article, 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: 1) 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; 2) 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 3) 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.