IMPLEMENTING SECURITY FOR ACTIVE NETWORKS IN INTERNET

Active networks is primarily a Defense Advanced Research Projects Agency(DARPA)-funded project focusing on the research of mechanisms, applications, and operating systems to develop a reconfigurable network infrastructure. This letter proposes an Secure Active Tracing System (SATS) to implementing security for active networking in Internet. Unlike currently existing schemes, SATS reduces the computational overloads by executing the filtering operation on selected packet streams only when needed.

Resources Management in Active Networks

In an active network, users can insert customized active codes into activenodes, to execute. Thus it needs more resources than those required by conventional networks, andthese resources must be effectively monitored and managed. Management policies in existing OSs aretoo complicated to apply to simple active packets. In this paper, we present new resourcesmanagement policies that are mainly adoped to manage CPU, storage and transmission bandwidth.Namely, we use SPF algorithm to schedule and process active packets, and import an interval queuemethod to allocate transmission bandwidth, and use feedback mechanism to control congestion . At thesame time, we design some experiments on prototype systems with and without resources managementpolicies respectively. The experiments results show that management policies presented by us caneffectively manage resources in active nodes and can improve the performance of active networks.

DEAON： dynamically-evolving active overlay networks for scalable information retrieval

In this paper, we propose the dynamically-evolving active overlay network （DEAON）, which is an efficient, scalable yet simple protocol to facilitate applications of decentralized information retrieval in P2P networks. DEAON consists of three novel components ： a Desirable Topology Construction and Adaptation algorithm to guide the evolution of the overlay topology towards a small-world-like graph; a Semantic-based Neighbor Selection scheme to conduct an online neighbor ranking; a Topology-aware Intelligent Search mechanism to forward incoming queries to deliberately selected neighbors. We deploy and compare DEAON with other several existing distributed search techniques over static and dynamic environments. The results indicate that DEAON outperforms its competitors by achieving higher recall rate while using much less network resources, in both of the above environments.

A Critical Review of Active Distribution Network Reconfiguration:Concepts,Development,and Perspectives

In recent years,the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex.Consequently,a large number of active distribution network reconfiguration techniques have emerged to reduce system losses,improve system safety,and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network.While scholars have previously reviewed these methods,they all have obvious shortcomings,such as a lack of systematic integration of methods,vague classification,lack of constructive suggestions for future study,etc.Therefore,this paper attempts to provide a comprehensive and profound review of 52 methods and applications of active distribution network reconfiguration through systematic method classification and enumeration.Specifically,these methods are classified into five categories,i.e.,traditional methods,mathematical methods,meta-heuristic algorithms,machine learning methods,and hybrid methods.A thorough comparison of the various methods is also scored in terms of their practicality,complexity,number of switching actions,performance improvement,advantages,and disadvantages.Finally,four summaries and four future research prospects are presented.In summary,this paper aims to provide an up-to-date and well-rounded manual for subsequent researchers and scholars engaged in related fields.

A Cluster-Based Secure Active Network Environment

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallely processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Multi-Path Routing and Resource Allocation in Active Network

An algorithm of traffic distribution called active multi-path routing (AMR)in active network is proposed. AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic among multiple paths. It is combined to bandwidthresource allocation and the congestion restraint mechanism to avoid congestion happening and worsen.So network performance can be improved greatly. The frame of AMR includes adaptive trafficallocation model, the conception of supply bandwidth and its' allocation model, the principle ofcongestion restraint and its' model, and the implement of AMR based on multi-agents system in activenetwork. Through simulations, AMR has distinct effects on network performance. The results show AMRisa valid traffic regulation algorithm.

Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors. A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator. The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced. The neuro-identifier and the neuro-controller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC. By adjusting the neuro-identifier and the neuro-controller alternatively, the manipulator was controlled on line for achieving the desired dynamic performance. Finally, a planar 3R redundant manipulator with one smart link was utilized as an illustrative example. The simulation results proved the validity of the control strategy.

GPS Rapid Static and Kinematic Positioning Based on GPS Active Network

This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

A Novel Active Network Architecture Based on Extensible Services Router

Active networks are a new kind of packet-switched networks in which packets have code fragments that are executed on the intermediary nodes (routers). The code can extend or modify the foundation architecture of a network. In this paper, the authors present a novel active network architecture combined with advantages of two major active networks technology based on extensible services router. The architecture consists of extensible service router, active extensible components server and key distribution center (KDC). Users can write extensible service components with programming interface. At the present time, we have finished the extensible services router prototype system based on Highly Efficient Router Operating System (HEROS), active extensible components server and KDC prototype system based on Linux.

Topological probability and connection strength induced activity in complex neural networks

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.

Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

Leader-follower Optimal Selection Method for Distributed Control System in Active Distribution Networks

Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

Optimal Operation Control Strategies for Active Distribution Networks Under Multiple States:A Systematic Review

With the large-scale integration of distributed renewable generation(DRG)and increasing proportion of power electronic equipment,the traditional power distribution network(DN)is evolving into an active distribution network(ADN).The operation state of an ADN,which is equipped with DRGs,could rapidly change among multiple states,which include steady,alert,and fault states.It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs.In this paper,the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states.The multi-state identification indicators and identification methods are summarized in detail.The multi-state regulation capacity quantification methods are analyzed considering controllable resources,quantification indicators,and quantification methods.A detailed survey of optimal operation control strategies,including multi-state operations,is presented,and key problems and outlooks for the expansion of ADN are discussed.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.