INCENTIVISE HOUSEHOLDS TO ADOPT PROPER WASTE MANAGEMENT AND WATER CONSERVATION METHODS VIA BLOCKCHAIN TECHNOLOGY

Author: Karan Ahluwalia

ABSTRACT

Nobel Laureates, Richard Thaler and Cass R. Sunstein, in their Nobel Prize winning work, Nudge: Improving Decisions about Health, Wealth, and Happiness, introduced the idea that ‘governments can help people make better decisions while respecting their freedom of choice.’  This upended the neo-classical economic theory that believed that “people make rational economic decisions on the basis of complete

Information.” 1 Drawing inspiration from the work of Nobel Laureate Richard Thaller, our plan is to nudge and incentivise households to adopt proper waste management and water conservation methods communities across India to choose sustainable sources of energy and be conscious of their resource consumption.

Keywords: Blockchain, Web3, Crypto, Karan Ahluwalia

REFERENCES

  • Kaushik, A.K. (2019) the development of smart water markets using blockchain … – JSTOR. Available at: https://www.jstor.org/stable/resrep19980.4 (2020)
  • Arunmozhi et al. Application of blockchain and smart contracts in autonomous vehicle supply chains: an experimental design Transport. Res. Part E: Logist. Transport Rev. (2022)
  • S.M.H. Bamakan et al. A survey of blockchain consensus algorithms performance evaluation criteria Expert Syst. Appl. (2020)
  • Banabilah et al. Federated learning review: fundamentals, enabling technologies, and future applications Inf. Process. Manag. (2022)
  • Banabilah et al. Federated learning review: fundamentals, enabling technologies, and future applications Inf. Process. Manag. (2022)
  • A.K. Biswas et al. A probable cheating-free (t,n) threshold secret sharing scheme with enhanced blockchain [J] Comput. Electr. Eng. (2022)
  • Centobelli et al. Blockchain technology for bridging trust, traceability and transparency in circular supply chain Inf. Manag. (2022)
  • Chen et al. A training-integrity privacy-preserving federated learning scheme with trusted execution environment Inf. Sci. (2020)
  • T.M. Choi et al. Blockchain in logistics and production from Blockchain 1.0 to Blockchain 5.0: an intra-inter-organizational framework Transport. Res. E Logist. Transport. Rev. (2022)
  • A.G. Gad et al. Emerging trends in blockchain technology and applications: a review and outlook J. King Saud Univ.-Comput. Info. Sci. (2022)
  • Hameed et al. A taxonomy study on securing Blockchain-based Industrial applications: an overview, application perspectives, requirements, attacks, countermeasures, and open issues J. Indus. Inf. Integr. (2022)
  • Kumar et al. leveraging blockchain for ensuring trust in IoT: a survey J. King Saud Univ.-Comput. Info. Sci. (2022)
  • Lasla et al. Green-PoW: an energy-efficient blockchain proof-of-work consensus algorithm Comput. Network. (2022)
  • Li et al. Privacy computing: concept, computing framework, and future development trends Engineering (2019)
  • Liu et al. An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system Inf. Sci. (2021)
  • Liu et al. P-PBFT: an improved blockchain algorithm to support large-scale pharmaceutical traceability [J] Comput. Biol. Med. (2023)
  • O.O. Olakanmi et al. Trust-aware and incentive-based offloading scheme for secure multi-party computation in Internet of Things Internet Things (2022)
  • Peng et al. Privacy preservation in permissionless blockchain: a survey Digit. Commun. Netw. (2021)
  • Qu Blockchain in medical informatics J. Indus. Inf. Integr. (2022)
  • P.V.R.P. Raj et al. Procurement, traceability and advance cash credit payment transactions in supply chain using blockchain smart contracts Comput. Ind. Eng. (2022)
  • Wang et al. Business Innovation based on artificial intelligence and Blockchain technology Inf. Process. Manag. (2022)
  • Wang et al. Enhancing privacy preservation and trustworthiness for decentralized federated learning Inf. Sci. (2023)
  • Xie et al. TEBDS: a trusted execution environment-and-blockchain-supported IoT data sharing system Future Generat. Comput. Syst. (2023)
  • P.C.M. Arachchige et al. A trustworthy privacy preserving framework for machine learning in industrial IoT systems IEEE Trans. Ind. Inf. (2020)
  • ARM Security Technology Building a Secure System Using Trustzone Technology (White paper) (2009)
  • G.R. Blakley Safeguarding Cryptographic keys
  • X.Q. Cai et al. Blockchain principles and core technologies Chin. J. Comput. (2021)
  • Chandramouli et al. A survey on perfectly secure verifiable secret-sharing ACM Comput. Surv. (2022)
  • Chen et al. An incentive-compatible rational secret sharing scheme using blockchain and smart contract Sci. China Inf. Sci. (2021)
  • Chen et al. Privacy-preserving deep learning model for decentralized vanets using fully homomorphic encryption and blockchain IEEE Trans. Intell. Transport. Syst. (2021)
  • Chen et al. Verifiable homomorphic secret sharing for low degree polynomials IEEE Trans. Depend. Sec. Comput. (2022)
  • Chillotti et al. Scooby: improved multi-party homomorphic secret sharing based on FHE
  • Desai et al. SECAUCTEE: securing auction smart contracts using trusted execution environments
  • H.B. Desai et al. Blockfla: accountable federated learning via hybrid blockchain architecture
  • Y.Q. Diao et al. Double privacy protection method of coalition chain based on group signature and homomorphic encryption J. Comput. Res. Dev. (2022)
  • X.H. Diao et al. Intelligent computing scheme of blockchain based on trusted execution environment
  • Du et al. Blockchain-aided edge computing market: smart contract and consensus mechanisms IEEE Trans. Mobile Comput. (2022)
  • Elshamy et al. improving the efficiency of RMSProp optimizer by utilizing Nestrove in deep learning Sci. Rep. (2023)
  • Falcetta et al. Privacy-preserving deep learning with homomorphic encryption: an introduction IEEE Comput. Intell. Mag. (2022)
  • Fang et al. Edge computing privacy protection method based on blockchain and federated learning J. Commun. (2021)
  • Feng et al. Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: a deep reinforcement learning approach IEEE Internet Things J. (2019)
  • Feng et al. Regulatable and hardware-based proof of stake to approach nothing at stake and long range attacks IEEE Trans. Serv. Comput. (2022)
  • M.A. Ferrag et al. The performance evaluation of blockchain-based security and privacy systems for the Internet of Things: a tutorial IEEE Internet Things J. (2021)
  • Han et al. How can incentive mechanisms and blockchain benefit with each other? a survey ACM Comput. Surv. (2022)
  • M.U. Hassan et al. Anomaly detection in blockchain networks: a comprehensive survey IEEE Commun. Surv. Tutorials (2022)
  • N.Q. Hieu et al. Resource management for blockchain-enabled federated learning: a deep reinforcement learning approach arXiv preprint arXiv: 2004.04104 (2020)
  • Hoekstra et al. using innovative instructions to create trustworthy software solutions HASP@ ISCA (2013)
  • Houtan et al. A survey on blockchain-based self-sovereign patient identity in healthcare IEEE Access (2020)
  • T.Y. Hu et al. Research on contract security and privacy security of smart contracts Chin. J. Comput. (2021)
  • Huang et al. constructing fair secure multi-party computation based on blockchain Appl. Res. Comput. (2020)
  • Huo et al. A comprehensive survey on blockchain in industrial internet of things: motivations, research progresses, and future challenges IEEE Commun. Surv. Tutor. (2022)