Digital marketing has transformed referral marketing, revealing limitations in traditional centralized systems such as trust, transparency, and efficiency, however, the potential advantages of decentralized systems remain underexplored. This paper investigates the feasibility of a high-volume, decentralized referral system. The approach assesses smart contract prototypes for cost-effectiveness and performance in high-user engagement scenarios in different EVM-compatible blockchains and referral strategies, such as multilevel referrals. Findings confirm the technical viability as a blueprint for designing and implementing similar systems, highlighting challenges in real-world deployments, such as Sybil attacks, and the interplay between technical and economical design factors.
In this paper, we consider DAG-based Distributed Ledger Technologies (DLTs), i.e., DLTs where each block can reference several previous blocks hence forming a Directed Acyclic Graph of Blocks (BDAG). Each block has a weight (usually a constant normalized to one) and our goal is to compute the heaviest sub-BDAG that does not contain conflicting blocks. First, we prove that computing such a sub-BDAG is NP-complete. Then, we show that the difficulty comes from con-current conflicts and we present an optimal algorithm that is polynomial if the number of concurrent conflicts is bounded. Finally, we evaluate the performance of our algorithm on random BDAGs against an existing algorithm called GHOSTDAG and show that, in addition to being optimal, our algorithm is also more efficient in practice.
This paper addresses the problem of blockchain scalability, problem that might be detrimental to their widespread adoption. Blockchain data can be categorized into both an application part and a consensus part, the latter one growing linearly over time. The authors in [1] provably solves this problem in the context of work-based blockchains. Their solution achieves an exponential reduction both for storage and communication assuming a static mining difficulty, that is assuming that the blockchain is beeing built by a fixed number of miners. This paper improves upon this work by handling variable mining difficulty. Experimental results show that our solution still guarantees exponential reduction of both storage and communication.
This paper addresses the challenge of interoperability between blockchain networks in healthcare. It presents a new conceptual model that uses non-fungible tokens (NFTs) to promote seamless communication between different blockchain systems. These tokens play a key role in securing data exchange and protecting medical information by serving as unique identifiers. Expected outcomes of this approach include improved interoperability, streamlined workflows, and improved patient outcomes.
The Move language provides superior security and verifiability compared to existing smart contract languages such as Solidity. As the language becomes more popular, gas optimization will become an important field of research. This paper presents the first work on gas optimization in the Move language. We chose Aptos as the underlying platform for our analysis since it is the leading Move-enabled blockchain platform, and it was the first to develop a gas meter. In this paper, we describe Aptos' gas meter in detail. Then, we analyze the vast research on gas optimization in Solidity, and explore how it can be implemented in the Move language. Finally, this paper proposes 11 gas optimization patterns and principles for the Move language, presents 5 patterns that decrease the time complexity of the smart contract but have no effect on gas consumption, and implements a sample smart contract for each proposed gas optimization pattern. Our results show that the proposed patterns reduce gas consumption in a typical smart contract by 7 – 56%.
