Showing papers on "Network topology published in 2014"

Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies

Abstract: Distributed consensus tracking is addressed in this paper for multi-agent systems with Lipschitz-type node dynamics. The main contribution of this work is solving the consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. By using tools from M-matrix theory, a class of consensus tracking protocols based only on the relative states among neighboring agents is designed. By appropriately constructing Lyapunov function, it is proved that consensus tracking in the closed-loop multi-agent systems with a fixed topology having a directed spanning tree can be achieved if the feedback gain matrix and the coupling strength are suitably selected. Furthermore, with the assumption that each possible topology contains a directed spanning tree, it is theoretically shown that consensus tracking under switching directed topologies can be achieved if the control parameters are suitably selected and the dwell time is larger than a positive threshold. The results are then extended to the case where the communication topology contains a directed spanning tree only frequently as the system evolves with time. Finally, some numerical simulations are given to verify the theoretical analysis.

Doing Topology Optimization Explicitly and Geometrically—A New Moving Morphable Components Based Framework

Abstract: In the present work, we intend to demonstrate how to do topology optimization in an explicit and geometrical way. To this end, a new computational framework for structural topology optimization based on the concept of moving morphable components is proposed. Unlike in the traditional solution frameworks, where topology optimization is achieved by eliminating unnecessary materials from the design domain or evolving the structural boundaries, optimal structural topology is obtained by optimizing the layout of morphable structural components in the proposed approach. One of the advantages of the proposed approach, which may have great potential in engineering applications, is that it can integrate the size, shape, and topology optimization in CAD modeling systems seamlessly. The approach can combine both the advantages of explicit and implicit geometry descriptions for topology optimization. It also has the great potential to reduce the computational burden associated with topology optimization substantially. Some representative examples are presented to illustrate the effectiveness of the proposed approach. 1. M. P. Bendsoe, N. Kikuchi, Generating optimal topologies in structural design using a homogenization method, Computer Methods in Applied Mechanics and Engineering, 71:

Adaptation, Learning, and Optimization Over Networks

Abstract: This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word "network" has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. At the same time, the work illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning.

Towards Deep Neural Network Architectures Robust to Adversarial Examples

Abstract: Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. How- ever, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.

Adaptive Networks

Abstract: This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents to interact locally in response to streaming data and to continually learn and adapt to track drifts in the data and models. Under reasonable technical conditions on the data, the adaptive networks are shown to be mean square stable in the slow adaptation regime, and their mean square error performance and convergence rate are characterized in terms of the network topology and data statistical moments. Classical results for single-agent adaptation and learning are recovered as special cases. The performance results presented in this work are useful in comparing network topologies against each other, and in comparing adaptive networks against centralized or batch implementations. The presentation is complemented with various examples linking together results from various domains.

Controllability Metrics, Limitations and Algorithms for Complex Networks

Abstract: This paper studies the problem of controlling complex networks, i.e., the joint problem of selecting a set of control nodes and of designing a control input to steer a network to a target state. For this problem, 1) we propose a metric to quantify the difficulty of the control problem as a function of the required control energy, 2) we derive bounds based on the system dynamics (network topology and weights) to characterize the tradeoff between the control energy and the number of control nodes, and 3) we propose an open-loop control strategy with performance guarantees. In our strategy, we select control nodes by relying on network partitioning, and we design the control input by leveraging optimal and distributed control techniques. Our findings show several control limitations and properties. For instance, for Schur stable and symmetric networks: 1) if the number of control nodes is constant, then the control energy increases exponentially with the number of network nodes; 2) if the number of control nodes is a fixed fraction of the network nodes, then certain networks can be controlled with constant energy independently of the network dimension; and 3) clustered networks may be easier to control because, for sufficiently many control nodes, the control energy depends only on the controllability properties of the clusters and on their coupling strength. We validate our results with examples from power networks, social networks and epidemics spreading.

OpenDaylight: Towards a Model-Driven SDN Controller architecture

Abstract: This paper describes a novel Software-Defined Networking (SDN) Controller architecture that is built on Model-Driven Software Engineering (MDSE) principles. It supports both the “classic” OpenFlow-based approach to SDN and emerging model-driven network management/programmability technologies, such as NETCONF/YANG. The architecture was first implemented in the OpenDaylight Project Hydrogen release, and it is being further evolved in subsequent OpenDaylight releases.

Controller Synthesis for String Stability of Vehicle Platoons

Abstract: Cooperative adaptive cruise control (CACC) allows for short-distance automatic vehicle following using intervehicle wireless communication in addition to onboard sensors, thereby potentially improving road throughput. In order to fulfill performance, safety, and comfort requirements, a CACC-equipped vehicle platoon should be string stable, attenuating the effect of disturbances along the vehicle string. Therefore, a controller design method is developed that allows for explicit inclusion of the string stability requirement in the controller synthesis specifications. To this end, the notion of string stability is introduced first, and conditions for L2 string stability of linear systems are presented that motivate the development of an H∞ controller synthesis approach for string stability. The potential of this approach is illustrated by its application to the design of controllers for CACC for one- and two-vehicle look-ahead communication topologies. As a result, L2 string-stable platooning strategies are obtained in both cases, also revealing that the two-vehicle look-ahead topology is particularly effective at a larger communication delay. Finally, the results are experimentally validated using a platoon of three passenger vehicles, illustrating the practical feasibility of this approach.

A Novel Multilevel Inverter Based on Switched DC Sources

Abstract: This paper presents a multilevel inverter that has been conceptualized to reduce component count, particularly for a large number of output levels. It comprises floating input dc sources alternately connected in opposite polarities with one another through power switches. Each input dc level appears in the stepped load voltage either individually or in additive combinations with other input levels. This approach results in reduced number of power switches as compared to classical topologies. The working principle of the proposed topology is demonstrated with the help of a single-phase five-level inverter. The topology is investigated through simulations and validated experimentally on a laboratory prototype. An exhaustive comparison of the proposed topology is made against the classical cascaded H-bridge topology.

Construction of Tree Network with Limited Delivery Latency in Homogeneous Wireless Sensor Networks

Abstract: In this paper we propose an approximation algorithm, which is called ADCMCST (algorithm with the minimum number of child nodes when the depth is restricted), to construct a tree network for homogeneous wireless sensor network, so as to reduce and balance the payload of each node, and consequently prolong the network lifetime. When the monitoring node obtains the neighbor graph, ADCMCST tries to find a tree topology with a minimum number of child nodes, and then broadcast the topology to every node, and finally a tree network is constructed. Simulation results show that ADCMCST could greatly reduce the topology formation time, and achieve good approximation results; when the compression ratio is less than 70 %, the network lifetime of ADCMCST will be larger than that of energy driven tree construction.

NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

Abstract: Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca.

A New General Topology for Cascaded Multilevel Inverters With Reduced Number of Components Based on Developed H-Bridge

Abstract: In this paper, a new general cascaded multilevel inverter using developed H-bridges is proposed. The proposed topology requires a lesser number of dc voltage sources and power switches and consists of lower blocking voltage on switches, which results in decreased complexity and total cost of the inverter. These abilities obtained within comparing the proposed topology with the conventional topologies from aforementioned points of view. Moreover, a new algorithm to determine the magnitude of dc voltage sources is proposed. The performance and functional accuracy of the proposed topology using the new algorithm in generating all voltage levels for a 31-level inverter are confirmed by simulation and experimental results.

Virtual Optical Network Embedding (VONE) Over Elastic Optical Networks

Abstract: Based on the concept of infrastructure as a service, optical network virtualization can facilitate the sharing of physical infrastructure among different users and applications. In this paper, we design algorithms for both transparent and opaque virtual optical network embedding (VONE) over flexible-grid elastic optical networks. For transparent VONE, we first formulate an integer linear programming (ILP) model that leverages the all-or-nothing multi-commodity flow in graphs. Then, to consider the continuity and consecutiveness of substrate fiber links' (SFLs') optical spectra, we propose a layered-auxiliary-graph (LAG) approach that decomposes the physical infrastructure into several layered graphs according to the bandwidth requirement of a virtual optical network request. With LAG, we design two heuristic algorithms: one applies LAG to achieve integrated routing and spectrum assignment in link mapping (i.e., local resource capacity (LRC)-layered shortest-path routing LaSP), while the other realizes coordinated node and link mapping using LAG (i.e., layered local resource capacity(LaLRC)-LaSP). The simulation results from three different substrate topologies demonstrate that LaLRC-LaSP achieves better blocking performance than LRC-LaSP and an existing benchmark algorithm. For the opaque VONE, an ILP model is also formulated. We then design a LRC metric that considers the spectrum consecutiveness of SFLs. With this metric, a novel heuristic for opaque VONE, consecutiveness-aware LRC-K shortest-path-first fit (CaLRC-KSP-FF), is proposed. Simulation results show that compared with the existing algorithms, CaLRC-KSP-FF can reduce the request blocking probability significantly.

Algorithms for Enhanced Inter-Cell Interference Coordination (eICIC) in LTE HetNets

Abstract: The success of LTE heterogeneous networks (HetNets) with macrocells and picocells critically depends on efficient spectrum sharing between high-power macros and low-power picos. Two important challenges in this context are: 1) determining the amount of radio resources that macrocells should offer to picocells, and 2) determining the association rules that decide which user equipments (UEs) should associate with picos. In this paper, we develop a novel algorithm to solve these two coupled problems in a joint manner. Our algorithm has provable guarantee, and furthermore, it accounts for network topology, traffic load, and macro-pico interference map. Our solution is standard compliant and can be implemented using the notion of Almost Blank Subframes (ABS) and Cell Selection Bias (CSB) proposed by LTE standards. We also show extensive evaluations using RF plan from a real network and discuss self-optimized networking (SON)-based enhanced inter-cell interference coordination (eICIC) implementation.

Cooperative Optimal Control for Multi-Agent Systems on Directed Graph Topologies

Abstract: This note brings together stability and optimality theory to design distributed cooperative control protocols that guarantee consensus and are globally optimal with respect to a positive semi-definite quadratic performance criterion. A common problem in cooperative optimal control is that global optimization problems generally require global information, which is not available to distributed controllers. Optimal control for multi-agent systems is complicated by the fact that the communication graph topology interplays with the agent system dynamics. In the note we use an inverse optimality approach together with partial stability to consider the cooperative consensus and pinning control. Agents with identical linear time-invariant dynamics are considered. Communication graphs are assumed directed and having fixed topology. Structured quadratic performance indices are derived that capture the topology of the graph, which allows for global optimal control that is implemented using local distributed protocols. A new class of digraphs is defined that admits a distributed solution to the global optimal control problem, namely those with simple graph Laplacian matrices.

FLOWGUARD: building robust firewalls for software-defined networks

Abstract: Software-Defined Networking (SDN) introduces significant granularity, visibility and flexibility to networking, but at the same time brings forth new security challenges. One of the fundamental challenges is to build robust firewalls for protecting OpenFlow-based networks where network states and traffic are frequently changed. To address this challenge, we introduce FlowGuard, a comprehensive framework, to facilitate not only accurate detection but also effective resolution of firewall policy violations in dynamic OpenFlow-based networks. FlowGuard checks network flow path spaces to detect firewall policy violations when network states are updated. In addition, FlowGuard conducts automatic and real-time violation resolutions with the help of several innovative resolution strategies designed for diverse network update situations. We also implement our framework and demonstrate the efficacy and efficiency of the proposed detection and resolution approaches in FlowGuard through experiments with a real-world network topology.

Distributed consensus of multi-agent systems with general linear node dynamics and intermittent communications

Abstract: SUMMARY Without assuming that the mobile agents can communicate with their neighbors all the time, the consensus problem of multi-agent systems with general linear node dynamics and a fixed directed topology is investigated. To achieve consensus, a new class of distributed protocols designed based only on the intermittent relative information are presented. By using tools from matrix analysis and switching systems theory, it is theoretically shown that the consensus in multi-agent systems with a periodic intermittent communication and directed topology containing a spanning tree can be cast into the stability of a set of low-dimensional switching systems. It is proved that there exists a protocol guaranteeing consensus if each agent is stabilizable and the communication rate is larger than a threshold value. Furthermore, a multi-step intermittent consensus protocol design procedure is provided. The consensus algorithm is then extended to solve the formation control problem of linear multi-agent systems with intermittent communication constraints as well as the consensus tracking problem with switching directed topologies. Finally, some numerical simulations are provided to verify the effectiveness of the theoretical results. Copyright © 2013 John Wiley & Sons, Ltd.

Towards Deep Neural Network Architectures Robust to Adversarial Examples

Abstract: Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. How- ever, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.

A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks

Abstract: Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless s`ensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCar in this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar’s controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, the tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

DISCO: Distributed multi-domain SDN controllers

Abstract: Software-Defined Networking (SDN) is now envisioned for Wide Area Networks (WAN) and constrained overlay networks. Such networks require a resilient, scalable and easily extensible SDN control plane. In this paper, we propose DISCO, an extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks. A DISCO controller manages its own network domain and communicates with other controllers to provide end-to-end network services. This east-west communication is based on a lightweight and highly manageable control channel. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol and we evaluated it through an inter-domain topology disruption use case.

Slim fly: a cost effective low-diameter network topology

Abstract: We introduce a high-performance cost-effective network topology called Slim Fly that approaches the theoretically optimal network diameter. Slim Fly is based on graphs that approximate the solution to the degree-diameter problem. We analyze Slim Fly and compare it to both traditional and state-of the-art networks. Our analysis shows that Slim Fly has significant advantages over other topologies in latency, bandwidth, resiliency, cost, and power consumption. Finally, we propose deadlock-free routing schemes and physical layouts for large computing centres as well as a detailed cost and power model. Slim Fly enables constructing cost effective and highly resilient data enter and HPC networks that offer low latency and high bandwidth under different HPC workloads such as stencil or graph computations.

Fast Recovery in Software-Defined Networks

Abstract: Although Software-Defined Networking and its implementation OpenFlow facilitate managing networks and enable dynamic network configuration, recovering from network failures in a timely manner remains non-trivial. The process of (a) detecting the failure, (b) communicating it to the controller and (c) recomputing the new shortest paths may result in an unacceptably long recovery time. In this paper, we demonstrate that current solutions, employing both reactive restoration or proactive protection, indeed suffer long delays. We introduce a failover scheme with per-link Bidirectional Forwarding Detection sessions and preconfigured primary and secondary paths computed by an OpenFlow controller. Our implementation reduces the recovery time by an order of magnitude compared to related work, which is confirmed by experimental evaluation in a variety of topologies. Furthermore, the recovery time is shown to be constant irrespective of path length and network size.

The Segment Routing Architecture

Abstract: Network operators anticipate the offering of an increasing variety of cloud-based services with stringent Service Level Agreements. Technologies currently supporting IP networks however lack the flexibility and scalability properties to realize such evolution. In this article, we present Segment Routing (SR), a new network architecture aimed at filling this gap, driven by use-cases defined by network operators. SR implements the source routing and tunneling paradigms, letting nodes steer packets over paths using a sequence of instructions (segments) placed in the packet header. As such, SR allows the implementation of routing policies without per-flow entries at intermediate routers. This paper introduces the SR architecture, describes its related ongoing standardization efforts, and reviews the main use-cases envisioned by network operators.

T-Storm: Traffic-Aware Online Scheduling in Storm

Abstract: Storm has emerged as a promising computation platform for stream data processing. In this paper, we first show inefficiencies of the current practice of Storm scheduling and challenges associated with applying traffic-aware online scheduling in Storm via experimental results and analysis. Motivated by our observations, we design and implement a new stream data processing system based on Storm, namely, T-Storm. Compared to Storm, T-Storm has the following desirable features: 1) based on runtime states, it accelerates data processing by leveraging effective traffic-aware scheduling for assigning/re-assigning tasks dynamically, which minimizes inter-node and inter-process traffic while ensuring no worker nodes are overloaded, 2) it enables fine-grained control over worker node consolidation such that T-Storm can achieve better performance with even fewer worker nodes, 3) it allows hot-swapping of scheduling algorithms and adjustment of scheduling parameters on the fly, and 4) it is transparent to Storm users (i.e., Storm applications can be ported to run on T-Storm without any changes). We conducted real experiments in a cluster using well-known data processing applications for performance evaluation. Extensive experimental results show that compared to Storm (with the default scheduler), T-Storm can achieve over 84% and 27% speedup on lightly and heavily loaded topologies respectively (in terms of average processing time) with 30% less number of worker nodes.

Buffer-aided cooperative communications: opportunities and challenges

Abstract: Cooperative communication can increase the throughput and/or extend the coverage of wireless networks. However, in conventional cooperative networks, half-duplex relays transmit and receive under a prefixed schedule, which does not allow them to exploit the best receiving and transmitting channels, thus limiting performance. Recently, new protocols have been proposed that circumvent this problem by making use of the additional flexibility offered by relays with buffers. Compared to conventional relaying protocols, these buffer-aided protocols provide significant gains in terms of throughput, diversity, and signal-to-noise ratio. This article outlines several buffer-aided relaying protocols for different network topologies, including one-way single- and multi-relay networks as well as two-way single-relay networks. Moreover, some practical challenges inherent to buffer-aided relaying, such as increased delay and complexity, and topics for future research are discussed.

VeriCon: towards verifying controller programs in software-defined networks

Abstract: Software-defined networking (SDN) is a new paradigm for operating and managing computer networks. SDN enables logically-centralized control over network devices through a "controller" software that operates independently from the network hardware, and can be viewed as the network operating system. Network operators can run both inhouse and third-party SDN programs (often called applications) on top of the controller, e.g., to specify routing and access control policies. SDN opens up the possibility of applying formal methods to prove the correctness of computer networks. Indeed, recently much effort has been invested in applying finite state model checking to check that SDN programs behave correctly. However, in general, scaling these methods to large networks is challenging and, moreover, they cannot guarantee the absence of errors. We present VeriCon, the first system for verifying that an SDN program is correct on all admissible topologies and for all possible (infinite) sequences of network events. VeriCon either confirms the correctness of the controller program on all admissible network topologies or outputs a concrete counterexample. VeriCon uses first-order logic to specify admissible network topologies and desired network-wide invariants, and then implements classical Floyd-Hoare-Dijkstra deductive verification using Z3. Our preliminary experience indicates that VeriCon is able to rapidly verify correctness, or identify bugs, for a large repertoire of simple core SDN programs. VeriCon is compositional, in the sense that it verifies the correctness of execution of any single network event w.r.t. the specified invariant, and can thus scale to handle large programs. To relieve the burden of specifying inductive invariants from the programmer, VeriCon includes a separate procedure for inferring invariants, which is shown to be effective on simple controller programs. We view VeriCon as a first step en route to practical mechanisms for verifying network-wide invariants of SDN programs.

Smart grid neighborhood area networks: a survey

Abstract: Smart grid is an intelligent power network featured by its two-way flows of electricity and information. With an integrated communication infrastructure, smart grid manages the operation of all connected components to provide reliable and sustainable electricity supplies. Many advanced communication technologies have been identified for their applications in different domains of smart grid networks. This paper focuses on wireless communication networking technologies for smart grid neighborhood area networks (NANs). In particular, we aim to offer a comprehensive survey to address various important issues on implementation of smart grid NANs, including network topology, gateway deployment, routing algorithms, and security. We will identify four major challenges for the implementation of NANs, including timeliness management, security assurance, compatibility design, and cognitive spectrum access, based on which the future research directions are suggested.