Smart Contract Application for Managing Land Administration System Transactions

Stefanovic, Miroslav; Pržulj, Đorđe; Ristić, Sonja; Stefanović, Darko; Nikolić, Danilo

doi:10.1109/access.2022.3164444

Cited by 20 publications

(15 citation statements)

References 52 publications

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“…M. Stefanovic et al in [23] uses ERC-721 and ERC-20 to optimize transfers in land administration systems. Land administration involves managing ownership history as well as physical, spatial and topographic information about real estate.…”

Section: Related Workmentioning

confidence: 99%

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

et al. 2023

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Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.

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Section: Related Workmentioning

confidence: 99%

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

et al. 2023

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“…One of the main bene ts of the technology, the redundance of storage, brings challenges to storage capacity (Goragandhi et al, 2021). That is the reason why it has been suggested to store only the hash values of accompanying documents in the Blockchain (Stefanovic et al, 2022) and (Yadav et al, 2022) purpuse to create two chains, where "Metadata is stored in the mainchain that is publicly accessible, and non-transactional data, such as pictures, contracts, PDF, other allied information, is stored in the sidechain".…”

Section: Transition Costsmentioning

confidence: 99%

“…Even though a complete disintermediation may be very di cult " Although one of the initial premises of blockchain was that there is no more need for trusted third parties, and in the proposed solution transfer is possible without trusted third parties. But, for real estate transactions, the existence of notaries (or similar o cers) will probably always be necessary" (Stefanovic et al, 2022). Removing notaries as an intermediary will require implementing an ex-post con ict resolution system (Garcia-Teruel & Simón-Moreno, 2021).…”

Section: Transition Costsmentioning

confidence: 99%

“…still raises legal and regulatory issues(Sladić et al, 2021).Four legal challenges were identi ed to Land Administration Systems: "control of IDs; the legality of contracts; registration of co-ownership; and amendment of the ledger. "(Stefanovic et al, 2022). The title right of property rights will be represented by tokens that must be performed lawfully in line with the respective jurisdiction.…”

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confidence: 99%

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A Systematic Literature Review on Blockchain for Real Estate Transactions: Benefits, Challenges, Enablers, and Inhibitors

Cunha

Silva

2023

Preprint

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This paper provides a systematic literature review of using blockchain applications for real estate transactions. For this purpose, we analyse the main benefits, challenges that need to be tackled, enablers and inhibitors to the adoption of technology. Due to its perceived features, Blockchain can increase transparency and security, offering trust to the market, reducing the number of intermediaries and, consequently, decreasing transaction costs and time spent. Nevertheless, several challenges need to be overcome for the market to feel secure in adopting the Blockchain technology, especially the ones related to legal enforcement. This paper broadly discusses the benefits and challenges, enablers, and inhibitors raised in peer-reviewed articles published so far. We found that there is a fair possibility of reducing intermediaries and thus decreasing fraud, time spent and costs. Still, this possibility needs to be carefully analysed, and further research must be done to understand which intermediaries can be removed without compromising transactions’ security.

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“…T HE enormous popularity of cryptocurrencies like Bitcoin [1] and Ethereum [2] enabled the development of blockchain technology as a secure solution for the Industrial Internet of Things (IIoT). There are applications for blockchain in a variety of industries, including health [3]- [5], agriculture [6], [7], supply chain management [8], education [9], real estate [10], [11] and manufacturing [12]. The blockchain's immutability, consensus, and smart contract capabilities impose a robust architecture that does away with third-party verification requirements, enables auditing and traceability, and promotes transactions between various parties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

et al. 2023

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Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.

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Smart Contract Application for Managing Land Administration System Transactions

Cited by 20 publications

References 52 publications

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

A Systematic Literature Review on Blockchain for Real Estate Transactions: Benefits, Challenges, Enablers, and Inhibitors

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

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