Version 1
: Received: 10 November 2022 / Approved: 10 November 2022 / Online: 10 November 2022 (04:49:09 CET)
How to cite:
Rajani, K.R.; Gaddam, A.; Gaddam, J. IoT based Smart Waste Management Using Deep Reinforcement Learning. Preprints2022, 2022110190. https://doi.org/10.20944/preprints202211.0190.v1
Rajani, K.R.; Gaddam, A.; Gaddam, J. IoT based Smart Waste Management Using Deep Reinforcement Learning. Preprints 2022, 2022110190. https://doi.org/10.20944/preprints202211.0190.v1
Rajani, K.R.; Gaddam, A.; Gaddam, J. IoT based Smart Waste Management Using Deep Reinforcement Learning. Preprints2022, 2022110190. https://doi.org/10.20944/preprints202211.0190.v1
APA Style
Rajani, K.R., Gaddam, A., & Gaddam, J. (2022). IoT based Smart Waste Management Using Deep Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202211.0190.v1
Chicago/Turabian Style
Rajani, K.R., Anuroop Gaddam and Jyotheesh Gaddam. 2022 "IoT based Smart Waste Management Using Deep Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202211.0190.v1
Abstract
Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for example, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking; extracting and reporting the actionable insights from the underlying data; and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for example a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.
Keywords
sustainability; smart cities; Internet of Things (IoT); multi-agent deep reinforcement learning; smart waste management; smart sensors
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
