Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.

Advanced DAG-Based Ranking(ADR)Protocol for Blockchain Scalability

In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.

基于路径存储表的Hashgraph共识算法优化与实现

Hashgraph是一种数据采用有向无环图(DAG)结构的区块链共识算法,Hashgraph引入了虚拟投票的概念,允许节点在无额外通信开销的情况下并发出块,实现异步场景下的拜占庭容错。然而,Hashgraph提出的虚拟投票算法存在算法时间复杂度较高、共识运行逻辑过于复杂等问题。为此,提出一种基于路径存储表的Hashgraph优化方案。首先,提出一种基于顶点可达表的见证人判定方法,通过存储路径的方式实时记录生成事件与历史事件的可达关系,在轮次划分阶段,通过查询顶点事件的可达信息取代回溯算法,降低见证人判断算法的时间复杂度;其次,针对顶点可达表无法跨轮次判断事件关系的问题,提出一种基于历史可达表的知名见证人判定方法,历史可达表将存储见证人与历史事件之间的可达关系,通过查询历史可达表解决知名见证人判定阶段需要反复回溯视图的问题;最后,根据顶点可达表和历史可达表改进Hashgraph中复杂的共识计算,提升算法效率,加快事件确认速度。实验结果表明,所提优化方案与Hashgraph原共识算法相比,算法运行效率提升65.76%,在吞吐量方面平均提升41.27%。

一种结合信任的多验证者DAG共识协议

共识协议是区块链系统中的关键,决定着系统的性能与安全性.针对现有共识协议暴露出的扩展性、吞吐量、参与门槛等问题及DAG区块链在区块选择方面的缺陷,提出一种结合信任的多验证者DAG共识协议TMVDP.首先,提出了基于多验证者的DAG结构,多个验证节点共同负责数据验证,从而提高系统性能与可扩展性,同时降低对普通节点的性能需求.其次,提出了DAG结构中的区块信任度机制.通过赋予区块相应的信任度,调整不同情况下区块被引用的概率,从而解决了孤块问题,保证网络的高吞吐量与安全性.最后,针对账本构建四叉树索引,有效避免DAG结构中数据量过大带来的检索困难问题.实验结果表明TMVDP协议在保证系统安全性的前提下提高了共识效率与吞吐量,加快了区块查询速度,扩展性良好.

Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph

With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability for power flow calculations to support unit dispatch.Based on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce model.This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data.Experiments are conducted on Spark cluster which is deployed as a cloud computing platform.They show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale calculations.In addition, running time will be reduced when adding cluster nodes.Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.

Performance analysis and comparison of PoW,PoS and DAG based blockchains

In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application.To this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure probability.The results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load conditions.In addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.

公开选举代表投票的DAG共识机制

区块链作为一种创新型的分布式账本技术,以其去中心化、可追溯、防篡改等特性,在未来许多行业中具有广泛的应用前景.但现有单链式结构的区块链存在并发低、高延迟等问题.一种基于有向无环图(directed acyclic graph,DAG)结构的新型账本技术的出现有望突破传统区块链的性能瓶颈,但目前基于DAG型区块链系统的共识机制并不成熟.本文针对典型DAG型区块链系统Nano网络的ORV共识机制存在的安全性问题进行改进,提出了一种基于代表选举模型的公开选举代表投票共识机制,即OERV(open election representative voting).使主要代表节点的权益得到了分散,增强了去中心化程度,提高了网络安全性.实验结果表明,OERV算法性能高效,能够在不牺牲系统效率的同时增强系统的稳定性和安全性,对于推动DAG型区块链共识机制的研究有着重要的现实意义.

大数据DAG任务流调度平台技术研究与应用

文章提出了一种大数据DAG任务流调度平台技术,其能够基于DAG任务流进行调度,以及对大数据处理流程进行调度。为了实现这一目的,文章从几个方面进行详细设计,即架构设计、协议设计、引擎设计、引擎热加载机制、DAG结构、资源介质机制、调度算法、回调机制、信号机制。使用基于拖拽的方式进行流程配置,降低用户的使用难度,最终实现在企业实时/离线大数据处理流程中承担所有任务调度工作。

车联网中基于有向无环图区块链的个性化联邦互蒸馏学习方法

联邦学习(FL)作为一种分布式训练方法,在车联网(IoV)中得到了广泛应用。区别于传统机器学习,FL允许智能网联车辆(CAVs)通过共享模型而非原始数据来协同训练全局模型,从而保护CAV隐私和数据安全。为了提升联邦学习模型精度,降低通信开销,该文首先提出一种基于有向无环图(DAG)区块链和CAVs的IoV架构,分别负责全局模型共享和本地模型训练。其次,设计了一种基于DAG区块链的异步联邦互蒸馏学习(DAFML)算法在本地同时训练教师和学生模型,教师模型的专业级网络结构可取得更高精度,学生模型的轻量级网络结构可降低通信开销,并采用互蒸馏学习使教师模型和学生模型从互相转移的软标签中学习知识以更新模型。此外,为了进一步提高模型精度,基于全局训练轮次和模型精度设定个性化权值来调节互蒸馏占比。仿真结果表明,DAFML算法在模型精度和蒸馏比率方面优于其他比较算法。

Electromagnetic side-channel attack based on PSO directed acyclic graph SVM

Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.

一种基于模糊聚类的网格DAG任务图调度算法

针对网格环境中,任务调度的目标系统具有规模庞大、分布异构和动态性等特点,提出一种基于模糊聚类的网格异构任务调度算法.以往的很多调度算法需要在调度的每一步遍历整个目标系统,虽然能够获得较小的makespan,但是无疑增加了整个调度的Runtime.定义了一组刻画处理单元综合性能的特征,利用模糊聚类方法对目标系统(处理单元网络)进行预处理,实现了对处理单元网络的合理划分,使得在任务调度时能够较准确地优先选择综合性能较好的处理单元聚类,从而缩小搜索空间,大量减少任务调度时选择处理单元的时间耗费.此外,就绪任务优先级的构造既隐含考虑了关键路径上节点的执行情况对整个程序执行的影响,又考虑了异构资源对任务执行的影响.实验及性能分析比较的结果表明,定义的处理器特征能够实现对处理器网络的合理划分,而且随着目标系统规模的增大,所提出的算法优越性越来越明显.

ErlangShen:基于流水线低访问开销的图式区块链高效事务执行机制

基于有向无环图(DAG)的图式区块链能够显著提升系统性能,已成为近年来业界的研究热点。相较于传统串行化的链式区块链,图式区块链可在单位时间内并发处理大量事务从而提升吞吐量。随着事务量的激增,图式区块链面临事务执行效率低的瓶颈问题,即海量事务执行对状态数据访问的需求大幅增加,导致高昂的输入/输出(I/O)开销。实现低I/O访问主要包括两方面的全新挑战:一方面,图式区块链若直接采用传统的事务预取机制,将因执行逻辑不一致引入大量的陈旧读;另一方面,针对不同账户的状态访问会在默克尔树的高层节点中造成重复的I/O开销。为此,本文设计基于流水线的图式区块链高效事务执行机制—ErlangShen,包括Epoch粒度的状态预取机制和默克尔高层路径缓存机制来分别减少陈旧读的数量和重复的I/O开销。具体而言,ErlangShen充分分析并利用了事务访问频次的冷热特征,将访问热事务的逻辑执行与冷事务的状态预取并行化,以避免状态预取对事务执行的影响。此外,为了进一步提升事务执行的吞吐量,根据访问冷热状态事务的特性设计了定制化的并发控制方法。实验结果表明,ErlangShen机制能够减少约90%的陈旧读数量,与最新图式区块链事务处理机制Nezha相比,可将性能提升3~4倍。

基于节点选择优化的DAG-SVM多类别分类

有向无环图支持向量机(DAG-SVM)对于N类别分类问题,会构造N×(N-1)/2个支持向量机分类器(为每2个类构造一个支持向量机),DAG-SVM可能出现由于节点选择不佳而导致整个分类器分类结果较差的情况。为此,提出一种改进的DAG-SVM。通过为每一层建立备选节点集合进行节点选择,选取下层备选节点集合中训练分类精度最高的一个节点组合作为当前层节点的下层节点,从而优化DAG-SVM的拓扑结构。实验结果表明,与已有的DAG-SVM,1-vs-1SVM,1-vs-a SVM方法相比,该方法的分类精度较高。

一种基于DAG图的异构可重构任务划分方法

为了实现高性能低功耗的计算,以任务划分为研究对象,提出了异构可重构计算的基本概念和形式化描述方法,给出了基于异构计算任务和异构可重构体系结构相结合的异构可重构两种任务划分方法:节点内部可重组及网络可重建的任务划分及算法,最后验证了其任务划分方法的有效性.

可重定向C编译器中DAG及归约规则

以在嵌入式系统中建立C编译器的技术特点为主要内容,用设计实例论述了C编译器实现中前端、后端的主要工作内容。说明了在前、后端之间起桥梁作用的中间描述语言有向无环图(DAG)的设计原理及形成方法,同时还就如何将DAG与目标机系统之间形成映射关系进行描述,提出了在映射中规约规则制定的方法和原则,给出了一些有指导意义的经验性结论。

一种面向车联网的区块链模型

目前业界正尝试使用区块链增强车联网应用的可靠性、信任和安全性,但区块链在性能上的不足使其难以适应车联网的移动性和实时性要求。针对上述问题,提出了一种基于虚拟分片和有向无环图(directed acyclicgraph,DAG)的树状快速区块共识(tree-blockchain consensus,TBC),通过固定可信节点进行共识,以减少对移动车辆节点的依赖性,基于DAG构建树形结构区块链,实现了区块的并行验证和上链。针对构建的树形结构区块链,提出了一种基于匹配码的虚拟分片验证机制,通过VRF函数和简单的哈希运算将验证节点和交易进行逻辑分组配对,确保了交易分片的随机性,减轻了验证过程的计算开销,提高了共识效率。安全性分析表明,TBC可以较好地应对网络中常见的恶意信息攻击、女巫攻击和中间人攻击。仿真实验表明,TBC的共识性能优于传统共识算法,可以满足车联网的应用需求。

基于DAG图解-重构的机群系统静态调度算法

机群系统静态任务调度是 NP-完全问题 ,通常的算法是通过一些启发式算法得到多项式次优解 .该文提出的图解 -子图重构算法实现了对分布在有向无环图 (directed acyclic graph,简称 DAG)上的并行任务的快速有效调度 .该算法的复杂性为 O(log| V| × (|V|+|E|) ) ,采用递归方法实现了对任务图的有效分解和子图重构 ,生成任务群 ,完成任务调度 ,并且初步实现了对处理机的优化 .通过实例分析以及与其他启发式调度算法的性能比较 ,证明该算法是一种快速、有效、可行的任务调度算法 .

基于DAGs模型的RAID系统的设计与实现

研究了一种基于DAGs模型的自动执行的冗余磁盘阵列技术,描述了这种模型的优点以及DAG的执行机制和Roll away错误恢复策略。在此基础上讨论了RAID系统实现,并结合实际课题讨论了在实际中运用这种模型开发RAID系统的好处与优势。

基于量子粒子群优化的DAG并行任务调度研究

任务调度是网络并行计算系统的核心问题之一。在有向无环图(DAG)描述问题的基础上,提出了一种进行并行任务调度的量子粒子群优化算法。首先对DAG并行任务调度问题作出定义,并给出了优化问题的目标;然后分别讨论了问题的编码表示、解码方案、位置向量的计算方法、离散问题连续化、算法的总体流程等;最后给出算法的仿真实验情况及分析,实验结果表明,该算法有良好的全局寻优性能和快捷的收敛速度,调度效果优于遗传算法和粒子群优化算法。