Tangle is a distributed ledger technology that stores data as a directed acyclic graph (DAG). Unlike blockchain, Tangle does not require dedicated miners for its operation; this makes Tangle suitable for Internet of Things (IoT) applications. Distributed ledgers have a built-in transaction rate control mechanism to prevent congestion and spamming; this is typically achieved by increasing or decreasing the proof of work (PoW) difficulty level based on the number of users. Unfortunately, this simplistic mechanism gives an unfair advantage to users with high computing power. This paper proposes a principal-agent problem (PAP) framework from microeconomics to control the transaction rate in Tangle. With users as agents and the transaction rate controller as the principal, we design a truth-telling mechanism to assign PoW difficulty levels to agents as a function of their computing power. The solution of the PAP is achieved by compensating a higher PoW difficulty level with a larger weight/reputation for the transaction. The mechanism has two benefits, (1) the security of Tangle is increased as agents are incentivized to perform difficult PoW, and (2) the rate of new transactions is moderated in Tangle. The solution of PAP is obtained by solving a mixed-integer optimization problem. We show that the optimal solution of the PAP increases with the computing power of agents. The structural results reduce the search space of the mixed-integer program and enable efficient computation of the optimal mechanism. Finally, via numerical examples, we illustrate the transaction rate control mechanism and study its impact on the dynamics of Tangle.
翻译:Tangle是一种将数据存储为有向无环图(DAG)的分布式账本技术。与区块链不同,Tangle不需要专用矿工来运行,这使得它非常适用于物联网(IoT)应用。分布式账本具有内置的交易速率控制机制,以防止拥堵和垃圾信息攻击。这通常是通过根据用户数量增加或减少工作证明(PoW)难度级别来实现的。不幸的是,这种简单的机制给拥有高计算能力的用户带来了不公平的优势。本文提出了一个来自微观经济学的主体代理问题(PAP)框架,用于控制Tangle的交易速率。将用户视为代理,将交易速率控制器视为主体,我们设计了一个真实报告机制,以代理的计算能力作为难度水平的分配依据。PAP的解决方案通过向交易赋予更高的权重/声誉来对更高的PoW难度水平进行补偿。该机制有两个好处:(1)安全性得到提高,因为代理被激励执行困难的PoW,(2)Tangle中的新交易速率被调节。通过解决混合整数优化问题来获得PAP的最优解。我们表明,PAP的最优解随着代理的计算能力增加而增加。结构结果减少了混合整数编程的搜索空间,并实现了最优机制的高效计算。最后,通过数值示例,我们展示了交易速率控制机制,并研究其对Tangle动态的影响。