研华基于Ethernet的I/O模块ADAM-6000系列新增加Peer to peer和GCL功能

研华科技日前宣布其基于以太网的数据采集I/O模块ADAM-6000系列增加两种新的功能:Peer to Peer和GCL(Graphical Condition Logic),这两种创新功能的增加使得其应用更具智能化和便利性,并且可以同时应用于多种ADAM-6000模块中。

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer.In recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction stability.In order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in SGs.The proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter optimization.In addition,MOGOA-based FS technique is utilized in the selection of optimum subset of features.Besides,DELM-based predictive model is also applied in forecasting the load requirements.The proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive outcome.To inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter dataset.In the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

基于P2P网络的Active XML缓存机制研究

Active XML是一种新型的具有交换内涵数据功能的语言架构。在Active XML文档中内涵数据是通过本地或远程Web services调用方式来获取。在Peer to Peer网络中,Active XML能改善工作任务的划分和分布。然而,如果一个站点属于多个Peer to Peer网络中的一员并且这个站点提供的服务被大量站点频繁调用,这个站点就会成为整个Web服务系统的瓶颈。在本文中,我们提出了一种在Peer to Peer网络中基于AXML技术的缓存机制的良好解决方案。该方案可以利用Active XML自身特性极大的简化缓存过程。

Data Integration Strategy for Database Grids Based on P2P Framework

The differences between the data integration of a dynamic database grid （DBG） and that of a distributed database system are analyzed, and three kinds of data integration strategies are given on the background of DBG based on Peer to Peer （P2P） framework, including the centralized data integration （CDI） strategy, the distributed data integration （DDI） strategy and the filter-based data integration （FDDI） strategy. CDI calls all the database grid services （DGSs） at a single node, DDI disperses the DGSs to multiple nodes, while FDDI schedules the data integration nodes based on filtering the keywords returned from DGSs. The performance of these three integration strategies are compared with and analyzed by simulation experiments. FDDI is more evident for filtering the keywords with data redundancy increasing. Through the reduction of large amount of data transportation, it effectively shortens the executing time for the task and improves its efficiency.

Fintech platforms:Lax or careful borrowers’screening?

Can peer-to-peer lending platforms mitigate fraudulent behaviors?Or have lending players been acting similar to free-riders?This paper constructs a new proxy to investigate lending platform misconduct and compares the FICO score and the LendingClub credit grade.To examine whether the lack of verification by the Fintech platform affects lenders’collection performance,I explore the recovery rate(RR)of non-performing loans through a mixed-continuous model.The regression results show that the degree of prudence taken by the lending platform in the pre-screening activity negatively affects the detection of some misreporting borrowers.I also find that the Fintech platform’s missing verification information(e.g.,annual income and employment length)affects the RR of non-performing loans,thereby hampering lenders’collection performance.

OPEN ARCHITECTURE FOR INTERNET BASED TELE-ROBOTIC SYSTEM

Building an Internet based tele-robotic system requires the developers beingproficient in computer theory, network technology, control theory, etc. A flexible, open structuredand module based framework for tele-control system is proposed on the basis of abstraction andanalysis of existing Internet-based control systems. This framework is designed following the peerto peer (P2P) distributed computing model. As a key to the system, the XML based ontology ofresources/modules/peers is discussed, so do the model for dynamically allocating of resources, basedon which the coordination among modules or peers can then be implemented. The experiment system andits experimental results prove the feasibility of the framework and its ease to use.

Consensus-Based Approach to Day-Ahead Joint Local Energy-Reserve Market Design Considering Uncertainties of Renewable Energy

The penetration of distributed renewable generationprovides green energy to the local energy system. However, theuncertainties it brings to the system are becoming more andmore unacceptable, making it difficult to efficiently operate thelocal energy system. In a foreseeable future, there could bean increasing number of flexible resources, such as naturalgas power plants and interruptible demands, which providesolutions to these uncertainties. In this paper, a novel day-aheadenergy market design for the local energy system is proposed.This market allows its participants to trade day-ahead energyin a peer-to-peer approach. In addition, flexible resources canprovide up/down-ward reserves to the system through reservetrading, where the local system operator (LSO) takes chargeof reserve procurement. Then, to operate the market, a marketclearing model is proposed incorporating operating costs of theday-ahead and real time stages. A consensus-based approach isapplied which enables the market to clear in a decentralizedmethod. Finally, since the LSO bears the reserve procurementcosts as well as the operating costs in the real time stage,we then propose a cost reallocation model to transfer theexpenses to the renewables. Case studies demonstrate the validityand efficiency of the proposed market design, including localenergy trading facilitation, high and stable revenue preservation,high computation efficiency and prevention of strategic biddingbehaviors.

WTR:A Reputation Metric for Distributed Hash Tables Based on a Risk and Credibility Factor

The growing number of popular peer to peer applications during the last five years has implied for researchers to focus on how to build trust in such very large scale distributed systems. Reputation systems have shown to be a very good solution to build trust in presence of malicious nodes. We propose in this paper a new metric for reputation systems on top of a Distributed Hash Table that uses a notion of risk to make the applications aware of certain behaviours of malicious nodes. We show that our metric is able to significantly reduce the number of malicious transactions, and that it also provides very strong resistance to several traditional attacks of reputations systems. We also show that our solution can easily scale, and can be adapted to various Distributed Hash Tables.

DEFEAT:A decentralized federated learning against gradient attacks

As one of the most promising machine learning frameworks emerging in recent years,Federated learning(FL)has received lots of attention.The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally.However,recent studies have shown that traditional centralized federated learning is vulnerable to various attacks,such as gradient attacks,where a malicious server collects local model gradients and uses them to recover the private data stored on the client.In this paper,we propose a decentralized federated learning against aTtacks(DEFEAT)framework and use it to defend the gradient attack.The decentralized structure adopted by this paper uses a peer-to-peer network to transmit,aggregate,and update local models.In DEFEAT,the participating clients only need to communicate with their single-hop neighbors to learn the global model,in which the model accuracy and communication cost during the training process of DEFEAT are well balanced.Through a series of experiments and detailed case studies on real datasets,we evaluate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.

Day-ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

Multi-energy systems are one of the key technologies to tackle energy crisis and environmental pollution.An energy hub(EH)is a minimum multi-energy system.Interconnection of multiple EHs through energy routers(ERs)can realize mutual energy assistance.This paper proposes a peer-to-peer(P2P)energy sharing strategy between EHs including ERs in an interconnected system,which is divided into two levels.In the lower level,a method of determining the charging/discharging constraints of energy storage devices is proposed.Based on the Lyapunov optimization method,virtual queues are used to model the energy storage devices and flexible loads in the system.The objective is to minimize the overall operating cost of the interconnected system.In the upper level,a non-cooperative game model is introduced to minimize the cost of purchasing power from other EHs for each EH.A best response-based method is adapted to find the Nash equilibrium.The simulation outcomes demonstrate that application of the proposed strategy can reduce operating costs of an interconnected system and each EH.On basis of a real-world dataset of interconnected EHs,both analytical and numerical results show the effectiveness of the proposed strategy.