Smart water conservation through a machine learning and blockchain-enabled decentralized edge computing network

【Author】 Thakur, Tanvi; Mehra, Aryan; Hassija, Vikas; Chamola, Vinay; Srinivas, Rallapalli; Gupta, Karunesh K.; Singh, Ajit Pratap

【Source】APPLIED SOFT COMPUTING

【影响因子】8.263

【Abstract】Forecasting precise water usage corresponding to various beneficial usages is important for optimal and sustainable planning and management of water resources. Due to rapid population growth, there is an urgent need for devising water saving solutions. In this paper, we propose a blockchain based incentivized edge computing framework for water saving using soft computing methodologies. The framework facilitates decision makers in creating awareness among people about water savings in a easily understandable scientific way. Our incentivized blockchain based model uses edge computing at the house nodes of the network to predict the actual usage of a particular household in the locality based on several factors such as number of people, average income of family, profession of the members and previous water demands. By using Feed Forward Networks and Mixture Density Networks, we predict the water usage in terms of input factors and historical usage respectively, thus incorporating machine computing into the framework. With the two values from these methods, a comparison is made with the actual amount of water used by the householders. This research proposes deployment of the smart contract on the blockchain network for efficient and accurate reward distribution. Incentives and rewards are given in the blockchain network to houses with lesser consumption and penalties are imposed when usage crosses predicted and historic usage. The model ensures that accurate incentives are provided to the people in order to motivate them to avoid wastage of water. Results show that the methods used in our work perform better than other relevant networks on a self-synthesized dataset. The proposed methods converge well and show higher spatio-temporal accuracy. (C) 2021 Elsevier B.V. All rights reserved.

【Keywords】Edge computing; Blockchain; Machine learning; Water conservation

【发表时间】2021 JUL

【收录时间】2022-01-02

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