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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Impact of tropospheric modelling on GNSS vertical precision:an empirical analysis based on a local active network

The troposphere affects Global Navigation Satellite System(GNSS)signals due to the variability of the refractive index.Tropospheric delay is a function of the satellite elevation angle and the altitude of the GNSS receiver and depends on the atmospheric parameters.If the residual tropospheric delay is not modelled carefully a bias error will occur in the vertical component.In order to analyse the precise altimetric positioning based on a local active network,four scenarios in Southern Spain with different topographical,environmental,and meteorological conditions are presented,considering both favourable and non-favourable conditions.The use of surface meteorological observations allows us to take into account the tropospheric conditions instead of a standard atmosphere,but introduces a residual tropospheric bias which reduces the accuracy of precise GNSS positioning.Thus,with short observation times it is recommended not to estimate troposphere parameters,but to use an a priori model together with the standard atmosphere.The results confirm that it is possible to achieve centimetre-scale vertical accuracy and precision with real time kinematic positioning even with large elevation differences with respect to the nearest reference stations.These numerical results may be taken into consideration for improving the altimetric configuration of the local active network.

Active Network Supports for Mobile IP

The basic mobile IP protocol is difficult to implement on the traditional IP network and not flexible and efficient under certain conditions. For example, firewalls or bound- ary routers may drop packets sent by mobile nodes for security reasons. Traditional networking devices such as routers cannot dynamically load and unload extended services to achieve differ- ent qualities of services. In this paper, a new scheme of using the active network to support the mobile IP is presented. The Softnet, a prototype of active networks based on mobile agents, is introduced. The active network is characterized by the programmability of its intermediate nodes and therefore presents dynamic and flexible behaviors. Special services can be dynam- ically deployed onto the active nodes in the Softnet. This property is definitely required in implementing the mobile IP protocols. The Softnet supports not only the basic mobile IP pro- tocol but also other extended mobiIe IP protocols. Virtual networks for mobile IP services are dynamically formed by mobile agents in the Softnet to provide different qualities of services.

nanoscale communication networks： utilizing an active network architecture

A safe and reliable nication network will be of in vivo nanoscale commu- great benefit for medical diagnosis and monitoring as well as medical implant communication. This review article provides a brief introduction to nanoscale and molecular networking in general and provides opinions on the role of active networking for in vivo nanoscale information transport. While there are many in vivo communication mechanisms that can be leveraged, for example, forms of cell signaling, gap junctions, calcium and ion signaling, and circulatory borne communication, this review examines two in particular： molecular motor transport and neuronal information communication. Molecular motors transport molecules representing information and neural coding operates by means of the action potential; these mechan- isms are reviewed within the theoretical framework of an active network. This review suggests that an active networking paradigm is necessary at the nanoscale along with a new communication constraint, namely, minimiz- ing the communication impact upon the living environment. The goal is to assemble efficient nanoscale and molecular communication channels while minimizing disruption to the host organism.

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.

The Austrian Botanic Gardens Work Group,an Example of Active Networking to Promote Small Botanic Gardens

The continuously increasing demands on botanic gardens during the last few decades have led to a huge in increase administration and an urgent need for additional specialized personnel,especially botanists,teachers,database specialists and administrative staff.Instead of meeting these requirements,many botanic gardens are faceing a severe decrease in funding and personnel.Larger gardens provide the opportunity to distribute several tasks to different employees,whereas small gardens are short staffed and often run by a single curator who has to fulfill all functions.In order to meet actual demands more easily,the Austrian botanic gardens are linked nationally via an active workgroup.This network not only allows the distribution of information but also facilitates the sharing of duties.A listserver speeds up the communication and correspondence within the workgroup,collection priorities and projects(e.g.,GSPC) are coordinated,seedbanking becomes decentralized,printed matters are shared and distributed,etc.Small gardens with only few employees can participate in projects by taking on small-ideally using with their special resources-in order not to fall behind.In addition,there is also an urgent need for international networking by means of plant and seed exchange(Index Seminum),BGCI membership,discussion groups,personal contacts and projects.Mission statements,special marketing strategies for public relations,integrating projects of other workgroup members and adapted public awareness programs are important to focus attention to small gardens and to help them keep alive.

Gym-ANM: Reinforcement learning environments for active network management tasks in electricity distribution systems

Active network management(ANM)of electricity distribution networks include many complex stochastic sequential optimization problems.These problems need to be solved for integrating renewable energies and distributed storage into future electrical grids.In this work,we introduce Gym-ANM,a framework for designing reinforcement learning(RL)environments that model ANM tasks in electricity distribution networks.These environments provide new playgrounds for RL research in the management of electricity networks that do not require an extensive knowledge of the underlying dynamics of such systems.Along with this work,we are releasing an implementation of an introductory toy-environment,ANM6-Easy,designed to emphasize common challenges in ANM.We also show that state-of-the-art RL algorithms can already achieve good performance on ANM6-Easy when compared against a model predictive control(MPC)approach.Finally,we provide guidelines to create new Gym-ANM environments differing in terms of(a)the distribution network topology and param-eters,(b)the observation space,(c)the modeling of the stochastic processes present in the system,and(d)a set of hyperparameters influencing the reward signal.Gym-ANM can be downloaded at https://github.com/robinhenr y/gym-anm.

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.

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.

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.

A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity threats.In this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic cryptosystem.During the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping attacks.The correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically guaranteed.The simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.