Abstract
In this work, an Edge-to-Cloud Analytics architecture model for Waste Management and Hazard Alert system is developed for prompt management of waste dumpsites. The system enables IoT smart nodes to monitor dumpsites and generate alerts on the detection of gas emissions, smoke, and fire. The custom-designed IoT dumpsite edge aggregator module is achieved using MQ-7 (carbon monoxide), MQ-2 (smoke/methane), MQ-135 (CO2, benzene, NH3, NOx), and flame sensors for proactive detection of environmental hazards. The scalable Edge-to-Cloud Analytics architectural model produces efficient data stream uploading from the IoT sensor nodes into the cloud domain with an MKR-1000 Edge Controller used to achieve edge-to-fog gateway processing. Three algorithms developed for the system include IoT node characterization, sensor reading/calibration, and data transmission to remote servers using Simultaneous Wireless Information Power Transfer (SWIPT). A proof of concept was demonstrated by building three IoT dumpsite nodes that were used for testing. The Testbed deployment with visualization patterns is observed on Open Source ThingSpeak Cloud-native platform. The key contributions to knowledge include the use of Edge-to-Cloud analytics for dumpsite monitoring and prompt evacuation, the introduction of SWIPT to conserve energy for optimized valued-insights and the development of an architectural model that allows lower computer-storage resources at the edge. This is very good since a single IoT device only serves a small geographic area. Future work will investigate throughput and latency minimization, API containerization for light deployments using Docker Swarm and Kubernetes. Since most dumpsites are remarkably close to end-users, edge to cloud (IoT) analytics is recommended for a sustainable solution to waste management in Nigeria.
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Acknowledgment
The authors wish to acknowledge and appreciate the Tertiary Education Trust Fund (TETFUND), Nigeria for funding the Waste Management and Hazard Alert System in 2019 and the Federal University of Technology Owerri management for providing a conducive environment for the research.
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The IoT researchers assert no conflict of interest. The funding sponsors are not involved with any role in the conceptualization, study design, data collection, data analysis, manuscript writing, and results in publication.
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Mbonu, E.S., Okafor, K.C., Chukwudebe, G.A., Ikerionwu, C.O., Amadi, E.C. (2021). Using Edge-to-Cloud Analytics IoT Dumpsite Monitor for Proactive Waste Management. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12957. Springer, Cham. https://doi.org/10.1007/978-3-030-87013-3_35
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