Dey, Somdip and Saha, Suman and Singh, Amit Kumar and McDonald-Maier, Klaus (2022) SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities. Smart Cities, 5 (1). pp. 162-176. DOI https://doi.org/10.3390/smartcities5010011
Dey, Somdip and Saha, Suman and Singh, Amit Kumar and McDonald-Maier, Klaus (2022) SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities. Smart Cities, 5 (1). pp. 162-176. DOI https://doi.org/10.3390/smartcities5010011
Dey, Somdip and Saha, Suman and Singh, Amit Kumar and McDonald-Maier, Klaus (2022) SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities. Smart Cities, 5 (1). pp. 162-176. DOI https://doi.org/10.3390/smartcities5010011
Abstract
Food waste is an important social and environmental issue that the current society faces, where one third of the total food produced is wasted or lost every year while more than 820 million people around the world do not have access to adequate food. However, as we move towards a decentralized Web 3.0 enabled smart city, we can utilize cutting edge technologies such as blockchain, artificial intelligence, cloud computing and many more to reduce food waste in different phases of the supply chain. In this paper, we propose SmartNoshWaste—a blockchain based multi-layered framework utilizing cloud computing, QR code and reinforcement learning to reduce food waste. We also evaluate SmartNoshWaste on real world food data collected from the nosh app to show the efficacy of the proposed framework and we are able to reduce food waste by 9.46% in comparison to the originally collected food data based on the experimental evaluation.
Item Type: | Article |
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Uncontrolled Keywords: | food production; supply chain; blockchain; qr code; machine learning; food security; food waste; sustainability; reinforcement learning; agriculture |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Jun 2022 16:26 |
Last Modified: | 30 Oct 2024 21:03 |
URI: | http://repository.essex.ac.uk/id/eprint/33073 |
Available files
Filename: smartcities-05-00011.pdf
Licence: Creative Commons: Attribution 3.0