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.

Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph

Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.

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.

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.

Clustering and Scheduling Method Based on Task Duplication

A new heuristic approach that resembles the evolution of interpersonal relationships in human society is put forward for the problem of scheduling multitasks represented by a directed acyclic graph. The algorithm includes dynamic-group, detachgraph and front-sink components. The priority rules used are new. Relationship number, potentiality, weight and merge degree are defined for cluster＇s priority, and task potentiality for tasks＇ priority. Experiments show the algorithm could get good result in short time. The algorithm produces another optimal solution for the classic MJD benchmark. Its average performance is better than five latter-day representative algorithms, especially six benchmarks of the nines.

Multi-satellite observation integrated scheduling method oriented to emergency tasks and common tasks

Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.

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.

NEW BOUNDS ON TIME AND NUMBER OF PROCESSORS FOR MULTIPROCESSOR OPTIMAL SCHEDULES

Two problems for task schedules in a multiprocessor parallel system are discussed in Ans paper (1) given a partially ordered set of tasks represented by the venices of an acyclic directed graph with their corresponding processing bines, derive the lower bound on the Annimum time(LBMT) needed to process the task graph for a given number of processors. (2) Determine the lower bound on minimum number of processors(LBMP) needed to complete those tasks in minimum bine. It is shown that the proposed LBMT is sharper than previously Known values and the comPUtational aspeCts of these bounds are also discussed.

Dynamic Routing Algorithm for Increasing Robustness in Satellite Networks

In low earth orbit（LEO） and medium earth orbit（MEO） satellite networks, the network topology changes rapidly because of the high relative speed movement of satellites. When some inter-satellite links （ISLs） fail, they can not be repaired in a short time. In order to increase the robustness for LEO/MEO satel- lite networks, an effective dynamic routing algorithm is proposed. All the routes to a certain node are found by constructing a destination oriented acyclic directed graph（DOADG） with the node as the destination. In this algorithm, multiple routes are provided, loop-free is guaranteed, and as long as the DOADG maintains, it is not necessary to reroute even if some ISLs fail. Simulation results show that comparing to the conventional routing algorithms, it is more efficient and reliable, costs less transmission overhead and converges faster.

Crowd sensing data delivery based on tangle DAG network

In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a crowd sensing data interaction method based on tangle directed acyclic graph(DAG)network.In this method,users and platforms are regarded as nodes of the network in the process of performing crowd sensing tasks.First,the heaviest chain is generated through the main chain strategy to ensure the stability of the network.Then,the hidden Markov model(HMM)prediction model is used to improve the correlation of the perceived data to improve the performance.Then,the confidential transaction and commitment algorithm is used to ensure the reliability of the transaction,overcome the security risks faced by the trusted third party,and simplify the group intelligence aware transaction mode.Finally,through simulation experiments,the security and feasibility of the group intelligence aware data delivery method based on tangle DAG network are verified.

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty,Belief Rule Base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination Belief Rule Base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.

A mobile edge computing-based applications execution framework for Internet of Vehicles

Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a whole.In comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning.In our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks.Both vehicles and MEC server within the communication range can be used as candidate offloading nodes.Then,the offloading involves assigning these computing nodes to subtasks.In addition,the proposed offloading scheme deal with the delay constraint of each subtask.The experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.

A Scheduling Optimization Technique Based on Reuse in Spark to Defend Against APT Attack

Advanced Persistent Threat （APT） attack, an attack option in recent years, poses serious threats to the security of governments and enterprises data due to its advanced and persistent attacking characteristics. To address this issue, a security policy of big data analysis has been proposed based on the analysis of log data of servers and terminals in Spark. However, in practical applications, Spark cannot suitably analyze very huge amounts of log data. To address this problem, we propose a scheduling optimization technique based on the reuse of datasets to improve Spark performance. In this technique, we define and formulate the reuse degree of Directed Acyclic Graphs （DAGs） in Spark based on Resilient Distributed Datasets （RDDs）. Then, we define a global optimization function to obtain the optimal DAG sequence, that is, the sequence with the least execution time. To implement the global optimization function, we further propose a novel cost optimization algorithm based on the traditional Genetic Algorithm （GA）. Our experiments demonstrate that this scheduling optimization technique in Spark can greatly decrease the time overhead of analyzing log data for detecting APT attacks.

Accelerating DAG-Style Job Execution via Optimizing Resource Pipeline Scheduling

The volume of information that needs to be processed in big data clusters increases rapidly nowadays. It is critical to execute the data analysis in a time-efficient manner. However, simply adding more computation resources may not speed up the data analysis significantly. The data analysis jobs usually consist of multiple stages which are organized as a directed acyclic graph (DAG). The precedence relationships between stages cause scheduling challenges. General DAG scheduling is a well-known NP-hard problem. Moreover, we observe that in some parallel computing frameworks such as Spark, the execution of a stage in DAG contains multiple phases that use different resources. We notice that carefully arranging the execution of those resources in pipeline can reduce their idle time and improve the average resource utilization. Therefore, we propose a resource pipeline scheme with the objective of minimizing the job makespan. For perfectly parallel stages, we propose a contention-free scheduler with detailed theoretical analysis. Moreover, we extend the contention-free scheduler for three-phase stages, considering the computation phase of some stages can be partitioned. Additionally, we are aware that job stages in real-world applications are usually not perfectly parallel. We need to frequently adjust the parallelism levels during the DAG execution. Considering reinforcement learning (RL) techniques can adjust the scheduling policy on the fly, we investigate a scheduler based on RL for online arrival jobs. The RL-based scheduler can adjust the resource contention adaptively. We evaluate both contention-free and RL-based schedulers on a Spark cluster. In the evaluation, a real-world cluster trace dataset is used to simulate different DAG styles. Evaluation results show that our pipelined scheme can significantly improve CPU and network utilization.

Identifiability of intermediate variables on causal paths

We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.

Discovering causes and effects of a given node in Bayesian networks

Causal relationships among variables can be depicted by a causal network of these variables. We propose a local structure learning approach for discovering the direct causes and the direct effects of a given target variable. In the approach, we first find the variable set of parents, children, and maybe some descendants （PCD） of the target variable, but generally we cannot distinguish the parents from the children in the PCD of the target variable. Next, to distinguish the causes from the effects of the target variable, we find the PCD of each variable in the PCD of the target variable, and we repeat the process of finding PCDs along the paths starting from the target variable. Without constructing a whole network over all variables, we find only a local structure around the target variable. Theoretically, we show the correctness of the proposed approach under the assumptions of faithfulness, causal sufficiency, and that conditional independencies are correctly checked.

Authentication scheme for industrial Internet of things based on DAG blockchain

Internet of things(IoT) can provide the function of product traceability for industrial systems. Emerging blockchain technology can solve the problem that the current industrial Internet of things(IIoT) system lacks unified product data sharing services. Blockchain technology based on the directed acyclic graph(DAG) structure is more suitable for high concurrency environments. But due to its distributed architecture foundation, direct storage of product data will cause authentication problems in data management. In response, IIoT based on DAG blockchain is proposed in this paper, which can provide efficient data management for product data stored on DAG blockchain, and an authentication scheme suitable for this structure is given. The security of the scheme is based on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai and Wolf(LRSW) who also show that it holds for generic groups. The sequential aggregation signature scheme is more secure and efficient, and the new scheme is safe in theory and it is more efficient in engineering.

CONVERGENCE OF BACKPRIOPAG ATION WITH MOMENTUM FOR NETWORK A RCHITECTURES WITH SKIP CONNECTIONS

We study a class of deep neural networks with architectures that form a directed acyclic graph(DAG).For backpropagation defined by gradient descent with adaptive momentum,we show weights converge for a large class of nonlinear activation functions.'The proof generalizes the results of Wu et al.(2008)who showed convergence for a feed-forward network with one hidden layer.For an example of the effectiveness of DAG architectures,we describe an example of compression through an AutoEncoder,and compare against sequential feed-forward networks under several metrics.

Mathematical and Numerical Aspects of the Adaptive Fast Multipole Poisson-Boltzmann Solver

This paper summarizes the mathematical and numerical theories and computational elements of the adaptive fast multipole Poisson-Boltzmann(AFMPB)solver.We introduce and discuss the following components in order:the Poisson-Boltzmann model,boundary integral equation reformulation,surface mesh generation,the nodepatch discretization approach,Krylov iterative methods,the new version of fast multipole methods(FMMs),and a dynamic prioritization technique for scheduling parallel operations.For each component,we also remark on feasible approaches for further improvements in efficiency,accuracy and applicability of the AFMPB solver to largescale long-time molecular dynamics simulations.The potential of the solver is demonstrated with preliminary numerical results.