TRUSTWORTHY AND EFFICIENT BLOCKCHAIN-BASED E-COMMERCE MODEL

Amidst the rising popularity of digital marketplaces, addressing issues such as non-
payment/non-delivery crimes, centralization risks, hacking threats, and the complexity of
ownership transfers has become imperative. Many existing studies exploring blockchain
technology in digital marketplaces and asset management merely touch upon various application scenarios without establishing a unified platform that ensures trustworthiness and
efficiency across the product life cycle. In this thesis, we focus on designing a reliable and efficient e-commerce model to trade various assets. To enhance customer engagement through
consensus, we utilize the XGBoost algorithm to identify loyal nodes from the platform entities pool. Alongside appointed nodes, these loyal nodes actively participate in the consensus
process. The consensus algorithm guarantees that all involved nodes reach an agreement on
the blockchain’s current state. We introduce a novel consensus mechanism named Modified-
Practical Byzantine Fault Tolerance (M-PBFT), derived from the Practical Byzantine Fault
Tolerance (PBFT) protocol to minimize communication overhead and improve overall efficiency. The modifications primarily target the leader election process and the communication
protocols between leader and follower nodes within the PBFT consensus framework.

In the domain of tangible assets, our primary objective is to elevate trust among various
stakeholders and bolster the reputation of sellers. As a result, we aim to validate secondhand
products and their descriptions provided by the sellers before the secondhand products are
exchanged. This validation process also holds various entities accountable for their actions.
We employ validators based on their location and qualifications to validate the products’
descriptions and generate validation certificates for the products, which are then securely
recorded on the blockchain. To incentivize the participation of validator nodes and up-
hold honest validation of product quality, we introduce an incentive mechanism leveraging
Stackelberg game theory.

On the other hand, for optimizing intangible assets management, we employ Non-Fungible
Tokens (NFT) technology to tokenize these assets. This approach enhances traceability of
ownership, transactions, and historical data, while also automating processes like dividend
distributions, royalty payments, and ownership transfers through smart contracts. Initially,
sellers mint NFTs and utilize the InterPlanetary File System (IPFS) to store the files related
to NFTs, NFT metadata, or both since IPFS provides resilience and decentralized storage solutions to our network. The data stored in IPFS is encrypted for security purposes.
Further, to aid sellers in pricing their NFTs efficiently, we employ the Stackelberg mechanism. Furthermore, to achieve finer access control in NFTs containing sensitive data and
increase sellers’ profits, we propose a Popularity-based Adaptive NFT Management Scheme
(PANMS) utilizing Reinforcement Learning (RL). To facilitate prompt and effective asset
sales, we design a smart contract-powered auction mechanism.

Also, to enhance data recording and event response efficiency, we introduce a weighted
L-H index algorithm and transaction prioritization features in the network. The weighted
L-H index algorithm determines efficient nodes to broadcast transactions. Transaction prior-
itization prioritizes certain transactions such as payments, verdicts during conflicts between
sellers and validators, and validation reports to improve the efficiency of the platform. Simulation experiments are conducted to demonstrate the accuracy and efficiency of our proposed
schemes.