SO-CPP: Sailfish optimization-based controller placement in IoT-enabled software-defined wireless sensor networks

One of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software-defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi-constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy-C-means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi-sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques.     

基于等级域的多服务Agent协作求解问题研究

在合同网当中引入等级域的概念,建立相应的基于等级域的多服务Agent模型GF-CNM,并采用随机TOP-N算法对等级域中不同等级的各个服务Agent的等级跃迁进行了算法描述和分析。该模型能减少任务协作时引起的网络通信量,避免对不相关服务Agent求解的时间开销,并均衡协作任务的分布,在一定程度上避免了“忙者越忙,闲者越闲”的“马太效应”,有效地缓解了资源受限条件下的任务协作求解问题。     

Link scheduling with power control for throughput enhancement in multihop wireless networks

Joint scheduling and power control schemes have previously been proposed to reduce power dissipation in wireless ad hoc networks. However, instead of power consumption, throughput is a more important performance concern for some emerging multihop wireless networks, such as wireless mesh networks. This paper examines joint link scheduling and power control with the objective of throughput improvement. The MAximum THroughput link Scheduling with Power Control (MATH-SPC) problem is first formulated and then a mixed integer linear programming (MILP) formulation is presented to provide optimal solutions. However, simply maximizing the throughput may lead to a severe bias on bandwidth allocation among links. To achieve a good tradeoff between throughput and fairness, a new parameter called the demand satisfaction factor (DSF) to characterize the fairness of bandwidth allocation and formulate the MAximum Throughput fAir link Scheduling with Power Control (MATA-SPC) problem is defined. An MILP formulation and an effective polynomial-time heuristic algorithm, namely, the serial linear programming rounding (SLPR) heuristic, to solve the MATA-SPC problem are also presented. Numerical results show that bandwidth can be fairly allocated among all links/flows by solving the MILP formulation or by using the heuristic algorithm at the cost of a minor reduction of network throughput. In addition, extensions to end-to-end throughput and fairness and multiradio wireless multihop networks are discussed.     

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

Enhanced performance based on cross-layer design from physical to transport layers for multihop wireless networks

This paper puts forward a novel cognitive cross-layer design algorithms for multihop wireless networks optimization across physical,mediam access control(MAC),network and transport layers.As is well known,the conventional layered-protocol architecture can not provide optimal performance for wireless networks,and cross-layer design is becoming increasingly important for improving the performance of wireless networks.In this study,we formulate a specific network utility maximization(NUM)problem that we believe is appropriate for multihop wireless networks.By using the dual algorithm,the NUM problem has been optimal decomposed and solved with a novel distributed cross-layer design algorithm from physical to transport layers.Our solution enjoys the benefits of cross-layer optimization while maintaining the simplicity and modularity of the traditional layered architecture.The proposed cross-layer design can guarantee the end-to-end goals of data flows while fully utilizing network resources.Computer simulations have evaluated an enhanced performance of the proposed algorithm at both average source rate and network throughput.Meanwhile,the proposed algorithm has low implementation complexity for practical reality.     

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross‐layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time‐varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. Copyright © 2013 John Wiley & Sons, Ltd.     

Software-defined networking QoS optimization based on deep reinforcement learning

To solve the problem that the QoS optimization schemes which based on heuristic algorithm degraded often due to the mismatch between parameters and network characteristics in software-defined networking scenarios,a software-defined networking QoS optimization algorithm based on deep reinforcement learning was proposed.Firstly,the network resources and state information were integrated into the network model,and then the flow perception capability was improved by the long short-term memory,and finally the dynamic flow scheduling strategy,which satisfied the specific QoS objectives,were generated in combination with deep reinforcement learning.The experimental results show that,compared with the existing algorithms,the proposed algorithm not only ensures the end-to-end delay and packet loss rate,but also improves the network load balancing by 22.7% and increases the throughput by 8.2%.     

Cross-Layer Design of Wireless Multihop Backhaul Networks With Multiantenna Beamforming

A cross-layer design approach is considered for joint routing and resource allocation for the physical (PHY) and the medium access control (MAC) layers in multihop wireless backhaul networks. The access points (APs) are assumed to be equipped with multiple antennas capable of both transmit and receive beamforming. A nonlinear optimization problem is formulated, which maximizes the fair throughput of the APs in the network under the routing and the PHY/MAC constraints. Dual decomposition is employed to decouple the original problem into smaller subproblems in different layers, which are coordinated by the dual prices. The network layer subproblem can be solved in a distributed manner and the PHY layer subproblem in a semidistributed manner. To solve the PHY layer subproblem, an iterative minimum mean square error (IMMSE) algorithm is used with the target link signal-to-interference-and-noise-ratio (SINR) set dynamically based on the price generated from the upper layers. A scheduling heuristic is also developed, which improves the choice of the transmission sets over time. Simulation results illustrate the efficacy of the proposed cross-layer design.     

一种基于分段路由的多路径流传输机制

针对传统网络多路径流量调度时存在的负载均衡效能差、路径部署困难的问题，利用软件定义网络的集中控制优势，设计了一种基于分段路由的多路径流传输（Segment Routing based Multipath Flow Transmission，SRMFT）机制.首先，以实现数据流的协同最优调度为目标，建立了SRMFT最优化模型；其次，采用分段路由技术和最简段标识序列（Segment IDentify sequence，SIDs）生成算法将多路径流调度问题转化为最简SIDs的选择问题，并设计了流调度算法求解；最后，试验结果表明，同等网络流量模型下，与较典型的多路径流传输机制相比，SRMFT有效提高了网络的对分带宽，降低了短流的传输时延，同时具有较低的流表存储开销.     

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.     

Modeling throughput gain of network coding in multi-channel multi-radio wireless ad hoc networks

In this paper, we model the network throughput gains of two types of wireless network coding (NC) schemes, including the conventional NC and the analog NC schemes, over the traditional non-NC transmission scheduling schemes in multihop, multi-channel, and multi-radio wireless ad hoc networks. In particular, we first show that the network throughput gains of the conventional NC and analog NC are (2n)/(2n-1) and n/(n-1), respectively, for the n-way relay networks where n ges 2. Second, we propose an analytical framework for deriving the network throughput gain of the wireless NC schemes over general wireless network topologies. By solving the problem of maximizing the network throughput subject to the fairness requirements under our proposed framework, we quantitatively analyze the network throughput gains of these two types of wireless NC schemes for a variety of wireless ad hoc network topologies with different routing strategies. Finally, we develop a heuristic joint link scheduling, channel assignment, and routing algorithm that aims at approaching the optimal solution to the optimization problem under our proposed framework.     

软件定义网络容错控制平面的最小覆盖布局方法

容错控制平面通过将多个控制器部署在不同的网络设备上进而增强网络的可靠性,但是大量的控制器部署带来了巨大的布局成本,严重地限制了容错控制平面在实际网络中的部署与应用。为了解决上述问题,该文首先构造了容错控制平面的最小覆盖布局模型,然后设计了一种基于局部搜索策略的启发式控制器布局算法,避免搜索结果陷入局部最优解。在不同规模网络中的仿真结果表明,相对于其他算法,所提算法可以在保证网络容错需求的同时,降低网络中部署控制器的数量。

     

Cross-Layer Rate Optimization for Proportional Fairness in Multihop Wireless Networks With Random Access

In this paper, we address the rate control problem in a multihop random access wireless network, with the objective of achieving proportional fairness amongst the end-to-end sessions. The problem is considered in the framework of nonlinear optimization. Compared with its counterpart in a wired network where link capacities are fixed, rate control in a multihop random access network is much more complex and requires joint optimization at both the transport and link layers. This is due to the fact that the attainable throughput on each link in the network is “elastic” and is typically a nonconvex and nonseparable function of the transmission attempt rates. Two cross-layer algorithms, a dual-based algorithm and a penalty-based algorithm, are proposed in this paper to solve the rate control problem in a multihop random access network. Both algorithms can be implemented in a distributed manner, and work at the link layer to adjust link attempt probabilities and at the transport layer to adjust session rates. We prove rigorously that the two proposed algorithms converge to the globally optimal solutions. Simulation results are provided in support of our conclusions.     

An evolutionary algorithm for broadcast scheduling in wireless multihop networks

A technical challenge in successful deployment and utilization of wireless multihop networks (WMN) are to make effective use of the limited channel bandwidth. One method to solve this challenge is broadcast scheduling of channel usage by the way of time division multiple access (TDMA). Three evolutionary algorithms, namely genetic algorithm (GA), immune genetic algorithm (IGA) and memetic algorithm (MA) are used in this study to solve broadcast scheduling for TDMA in WMN. The aim is to minimize the TDMA cycle length and maximize the node transmissions with reduced computation time. In comparison to GA and IGA, MA actively aim on improving the solutions and is explicitly concerned in exploiting all available knowledge about the problem. The simulation results on numerous problem instances confirm that MA significantly outperforms several heuristic and evolutionary algorithms by solving well-known benchmark problem in terms of solution quality, which also demonstrates the effectiveness of MA in efficient use of channel bandwidth.     

基于网络编码的无线网络多流问题研究

多流问题研究多对源、宿节点之间所能达到的最大吞吐。在无线网络中,解决该问题的关键在于量化无线干扰。由于网络编码能够在一定程度上克服无线干扰的影响,因此通过使用超边来描述编码发送,并构造关于超边的冲突图,可以实现对网络编码条件下无线干扰（以协议干扰模型为例）的量化,进而解决网络编码条件下的多流问题。此外,针对在超边冲突图中搜集所有极大独立集的NP难问题,提出了一种实用的搜集算法,并给出了相关的数字结果。     

Software-defined dynamic bandwidth optimization (SD-DBO) algorithm for optical access and aggregation networks

The optical access networks and aggregation networks are necessary to be controlled together to improve the bandwidth resource availability globally. Unified control architecture for optical access networks and aggregation networks is designed based on software-defined networking controller, the function modules of which have been described and the related extended protocol solution has been given. A software-defined dynamic bandwidth optimization (SD-DBO) algorithm is first proposed for optical access and aggregation networks, which can support unified optimizations and efficient scheduling by allocating bandwidth resources from a global network view in real time. The performance of the proposed algorithm has been verified and compared with traditional DBA algorithm in terms of resource utilization rate and average delay time. Simulation result shows that SD-DBO algorithm performs better.     

Cross-Layer-based Local Repair for Maximizing Goodput and Minimizing Control Messages in Multicasting MANET

The Multicast Ad hoc On-Demand Distance Vector (MAODV) protocol achieves multicast routing in self-organizing wireless mobile on-demand networks, e.g., Mobile Ad-hoc Networks (MANETs). However, unreliable wireless links degrade network reliability and network goodput, and the unreliable link problem becomes worse in multicasting because a multicast tree consists of more number of wireless links. MAODV adopts a broadcast-type local repair, and thus yields a large number of broadcast-type repair messages, increases extensive control overhead, and involves largely power consumption. Thus, a cross-layer unicast-type multihop local repair approach is proposed to recover broken links in multicasting MANETs. Additionally, the cross-layer mechanism provides mobile nodes to send a cross-layer message to the TCP sender to keep current congestion window (cwnd) and slow start threshold (ssthresh) when downstream links are temporarily broken, and then increases network goodput. Finally, the optimal number of neighbor-tiers is analyzed and the optimal substitute node is identified. Numerical results demonstrate that the proposed approach outperforms other approaches in successful repair rate, control message overhead, packet delivery ratio, and network goodput.     

Joint scheduling and power control supporting multicasting in wireless ad hoc networks

This paper addresses the problem of power control in a multihop wireless network supporting multicast traffic. We face the problem of forwarding packet traffic to multicast group members while meeting constraints on the signal-to-interference-plus-noise ratio (SINR) at the intended receivers. First, we present a distributed algorithm which, given the set of multicast senders and their corresponding receivers, provides an optimal solution when it exists, which minimizes the total transmit power. When no optimal solution can be found for the given set of multicast senders and receivers, we introduce a distributed, joint scheduling and power control algorithm which eliminates the weak connections and tries to maximize the number of successful multicast transmissions. The algorithm allows the other senders to solve the power control problem and minimize the total transmit power. We show that our distributed algorithm converges to the optimal solution when it exists, and performs close to centralized, heuristic algorithms that have been proposed to address the joint scheduling and power control problem.     

A utility-based distributed maximum lifetime routing algorithm for wireless networks

Energy-efficient routing is a critical problem in multihop wireless networks due to the severe power constraint of wireless nodes. Despite its importance and many research efforts toward it, a distributed routing algorithm that maximizes network lifetime is still missing. To address this problem, this paper proposes a novel utility-based nonlinear optimization formulation to the maximum lifetime routing problem. Based on this formulation, a fully distributed localized routing algorithm is further presented, which is proved to converge at the optimal point, where the network lifetime is maximized. Solid theoretical analysis and simulation results are presented to validate the proposed solution.     

Real-time traffic management by a parallel algorithm

The joint access-control and routing problem for distributed networks is formulated as an equilibrium programming problem (EPP). The strategy, for an upcoming control period, decreases call blocking and balances network load by maximizing network residual capacity, and fairly rejects at the source the source-destination demands that are expected to exceed network capacity. The EPP formulation allows for both decentralized implementation of the joint access control and routing problem and massive parallelization of the optimization procedure to satisfy real-time requirements. The computational complexity of the algorithm decreases proportionally with the number of processors used. The convergence of the decentralized algorithm to the network-wide optimum is proved. Compared to the case where subnet EPPs are solved independently by each controller, simulation results show significantly better utilization of network resources when subnet controllers jointly solve the network-wide EPP. Performance analysis of the algorithm and numerical studies show that the algorithm is well suited for real-time implementation in large networks or internetworks