A Real-Time TCP Stream Reassembly Mechanism in High-Speed Network

With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.

Consistency and Asymptotic Property of a Weighted Least Squares Method for Networked Control Systems

In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.

A Survey on Real-Time MAC Protocols in Wireless Sensor Networks

As wireless sensor network becomes pervasive, new requirements have been continuously emerged. How-ever, the most of research efforts in wireless sensor network are focused on energy problem since the nodes are usually battery-powered. Among these requirements, real-time communication is one of the big research challenges in wireless sensor networks because most of query messages carry time information. To meet this requirement, recently several real-time medium access control protocols have been proposed for wireless sensor networks in the literature because waiting time to share medium on each node is one of main source for end-to-end delay. In this paper, we first introduce the specific requirement of wireless sensor real-time MAC protocol. Then, a collection of recent wireless sensor real-time MAC protocols are surveyed, classified, and described emphasizing their advantages and disadvantages whenever possible. Finally we present a dis-cussion about the challenges of current wireless sensor real-time MAC protocols in the literature, and show the conclusion in the end.

Comparative Analysis between Conventional PI, Fuzzy Logic and Artificial Neural Network Based Speed Controllers of Induction Motor with Considering Core Loss and Stray Load Loss

Most of the controllers of IM （induction motor） for industrial applications have been designed based on PI controller without consideration of CL （core loss） and SLL （stray load loss）. To get the precise performances of torque as well as rotor speed and flux, the above mentioned losses should be considered. Conventional PI controller has overshoot effect at the transient period of the speed response curve. On the other hand, fuzzy logic and ANN （artificial neural network） based controllers can minimize the overshoot effect at the transient period because they have the abilities to deal with the nonlinear systems. In this paper, a comparative analysis is done between PI, fuzzy logic and ANN based speed controllers to find the suitable control strategy for IM with consideration of CL and SLL. The simulation analysis is done by using Matlab/Simulink software. The simulation results show that the fuzzy logic based speed controller gives better responses than ANN and conventional PI based speed controllers in terms of rotor speed, electromagnetic torque and rotor flux of IM.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Load Balancing Algorithm for Migrating Switches in Software-Dened Vehicular Networks

In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered by different intelligent devices can not handle due to it’s restricted capability.To manage this,it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness.The ow of data through the multiple controllers also varies,resulting in an unequal distribution of load between different controllers.One major drawback of the multiple controllers is their constant conguration of the mapping of the switch-controller,quickly allowing unequal distribution of load between controllers.To overcome this drawback,Software-Dened Vehicular Networking(SDVN)has evolved as a congurable and scalable network,that has quickly achieved attraction in wireless communications from research groups,businesses,and industries administration.In this paper,we have proposed a load balancing algorithm based on latency for multiple SDN controllers.It acknowledges the evolving characteristics of real-time latency vs.controller loads.By choosing the required latency and resolving multiple overloads simultaneously,our proposed algorithm solves the loadbalancing problems with multiple overloaded controllers in the SDN control plane.In addition to the migration,our algorithm has improved 25%latency as compared to the existing algorithms.

Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.

A Minimum-energy Path-preserving Topology Control Algorithm for Wireless Sensor Networks

The topology control strategies of wireless sensor networks are very important for reducing the energy consumption of sensor nodes and prolonging the life-span of networks. In this paper, we put forward a minimum-energy path-preserving topology control （MPTC） algorithm based on a concept of none k-redundant edges. MPTC not only resolves the problem of excessive energy consumption because of the unclosed region in small minimum-energy communication network （SMECN）, but also preserves at least one minimum-energy path between every pair of nodes in a wireless sensor network. We also propose an energy-efficient reconfiguration protocol that maintains the minimum-energy path property in the case where the network topology changes dynamically. Finally, we demonstrate the performance improvements of our algorithm through simulation.

Multi-goal Control of Chaotic Connected Complex Networks

Beam transport network(BTN)with small world(SW)(so-called BTN-SW)and Lorenz chaotic connectednetwork with scale-free(SF)are taken as two typical examples,we proposed a global linear coupling and combined withlocal error feedback methods in sub-networks to realize multi-goal control method of halo and chaos in two networksabove.The simulation results show that the methods above is effective for any chaotic connected networks and has apotential of applications in based-halo-chaos secure communication.

Load balancing strategy of heterogeneous wireless networks based on multi-hop routing algorithm of ad hoc network

Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in communication hot spots. Whereas in some neighboring under-loaded cells, bandwidth may be superfluous because there are only few users to request services. In order to raise resource utilization of the whole heterogeneous networks, several novel load balancing strategies are proposed, which combine the call ad- mission control policy and multi-hop routing protocol of ad-hoc network for load balancing. These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some parameters like load index and route cost, etc., and then transfer the denied users into neighboring under-loaded cell with surplus channel according to optimum multi-hop routing algorithm. Simulation results show that the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless netorks, improve the load balance index efficiently, and avoid the call block phenomenon almost absolutely.

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.

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.

NONLINEAR STABLE ADAPTIVE CONTROL BASED UPON ELMAN NETWORKS

Elman networks' dynamical modeling capability is discussed in this paper firstly.According to Elman networks' unique structure,a weight training algorithm is designed and a nonlinear adaptive controller is constructed.Without the PE presumption,neural networks controller's closed loop properties are studied and the whole Elman networks' passivity is demonstrated.

Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.

Reliable and Load Balance-Aware Multi-Controller Deployment in SDN

Software Defined Networking(SDN) provides flexible network management by decoupling control plane and data plane. However, such separation introduces the issues regarding the reliability of the control plane and controller load imbalance in the distributed SDN network, which will cause the low network stability and the poor controller performance. This paper proposes Reliable and Load balance-aware Multi-controller Deployment(RLMD) strategy to address the above problems. Firstly, we establish a multiple-controller network model and define the relevant parameters for RLMD. Then, we design the corresponding algorithms to implement this strategy. By weighing node efficiency and path quality, Controller Placement Selection(CPS) algorithm is introduced to explore the reliable deployments of the controllers. On this basis, we design Multiple Domain Partition(MDP) algorithm to allocate switches for controllers according to node attractability and controller load balancing rate, which could realize the reasonable domain planning. Finally, the simulations show that, compared with the typical strategies, RLMD has the better performance in improving the reliability of the control plane and balancing the distribution of the controller loads.

A Distributed Decision Mechanism for Controller Load Balancing Based on Switch Migration in SDN

Software Defined Networking(SDN) provides flexible network management by decoupling control plane from data plane. And multiple controllers are deployed to improve the scalability and reliability of the control plane, which could divide the network into several subdomains with separate controllers. However, such deployment introduces a new problem of controller load imbalance due to the dynamic traffic and the static configuration between switches and controllers. To address this issue, this paper proposes a Distribution Decision Mechanism(DDM) based on switch migration in the multiple subdomains SDN network. Firstly, through collecting network information, it constructs distributed migration decision fields based on the controller load condition. Then we choose the migrating switches according to the selection probability, and the target controllers are determined by integrating three network costs, including data collection, switch migration and controller state synchronization. Finally, we set the migrating countdown to achieve the ordered switch migration. Through verifying several evaluation indexes, results show that the proposed mechanism can achieve controller load balancing with better performance.

Coordinated control of coastal multi-source multi-load system with desalination load: a review

Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benefits of seawater desalination, the desalination load can be combined with renewable energy sources such as solar energy, wind energy, and ocean energy or with the power grid to ensure its effective regulation. Utilizing energy internet(EI) technology, energy balance demand of the regional power grid, and coordinated control between coastal multi-source multi-load and regional distribution network with desalination load is reviewed herein. Several key technologies, including coordinated control of coastal multi-source multi-load system with seawater desalination load, flexible interaction between seawater desalination and regional distribution network, and combined control of coastal multi-source multi-load storage system with seawater desalination load, are discussed in detail. Adoption of the flexible interaction between seawater desalination and regional distribution networks is beneficial for solving water resource problems, improving the ability to dissipate distributed renewable energy, balancing and increasing grid loads, improving the safety and economy of coastal power grids, and achieving coordinated and comprehensive application of power grids, renewable energy sources, and coastal loads.

Load Distribution of Base Stations in User-Centric Heterogeneous UDN

In ultra-dense networks(UDN),multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations(BSs).The data rate and quality of service can be improved.However,BSs in user-centric paradigm are required to serve more users due to this multiple association scheme.The improvement of system performance may be limited by the improving load of BSs.In this letter,we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN.Based on open loop power control(OLPC),a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio(SINR)for any typical user.As for any BS in different tiers,by leveraging stochastic geometry,we derive the Probability Mass Function(PMF)of the number of the served users,the Cumulative Distribution Function(CDF)of total power consumption,and the CDF bounds of downlink sum data rate.The accuracy of the theoretical analysis is validated by numerical simulations,and the effect the system parameters on the load of BSs is also presented.