Abstract: The need of a medium access control (MAC) protocol for an efficient broadcast service is of great importance to support the high-priority safety applications in vehicular ad hoc networks (VANETs). This paper introduces VeMAC, a novel multichannel TDMA MAC protocol proposed specifically for a VANET scenario. The VeMAC supports efficient one-hop and multihop broadcast services on the control channel by using implicit acknowledgments and eliminating the hidden terminal problem. The protocol reduces transmission collisions due to node mobility on the control channel by assigning disjoint sets of time slots to vehicles moving in opposite directions and to road side units. Analysis and simulation results in highway and city scenarios are presented to evaluate the performance of VeMAC and compare it with ADHOC MAC, an existing TDMA MAC protocol for VANETs. It is shown that, due to its ability to decrease the rate of transmission collisions, the VeMAC protocol can provide significantly higher throughput on the control channel than ADHOC MAC.

Abstract: We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at each node. The goal is to minimize the sum of all the cost functions, constraining the solution to be in the intersection of all the constraint sets. D-ADMM is proven to converge when the network is bipartite or when all the functions are strongly convex, although in practice, convergence is observed even when these conditions are not met. We use D-ADMM to solve the following problems from signal processing and control: average consensus, compressed sensing, and support vector machines. Our simulations show that D-ADMM requires less communications than state-of-the-art algorithms to achieve a given accuracy level. Algorithms with low communication requirements are important, for example, in sensor networks, where sensors are typically battery-operated and communicating is the most energy consuming operation.

Abstract: Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.

Abstract: The theoretically proven security of quantum key distribution (QKD) could revolutionize the way in which information exchange is protected in the future. Several field tests of QKD have proven it to be a reliable technology for cryptographic key exchange and have demonstrated nodal networks of point-to-point links. However, until now no convincing answer has been given to the question of how to extend the scope of QKD beyond niche applications in dedicated high security networks. Here we introduce and experimentally demonstrate the concept of a 'quantum access network': based on simple and cost-effective telecommunication technologies, the scheme can greatly expand the number of users in quantum networks and therefore vastly broaden their appeal. We show that a high-speed single-photon detector positioned at a network node can be shared between up to 64 users for exchanging secret keys with the node, thereby significantly reducing the hardware requirements for each user added to the network. This point-to-multipoint architecture removes one of the main obstacles restricting the widespread application of QKD. It presents a viable method for realizing multi-user QKD networks with efficient use of resources, and brings QKD closer to becoming a widespread technology.

Abstract: We propose a modeling framework for growing multiplexes where a node can belong to different networks. We define new measures for multiplexes and we identify a number of relevant ingredients for modeling their evolution such as the coupling between the different layers and the distribution of node arrival times. The topology of the multiplex changes significantly in the different cases under consideration, with effects of the arrival time of nodes on the degree distribution, average shortest path length, and interdependence.

Abstract: Due to their high fault-tolerance and scalability to large networks, consensus-based distributed algorithms have recently gained immense popularity in the sensor networks community. Large-scale camera networks are a special case. In a consensus-based state estimation framework, multiple neighboring nodes iteratively communicate with each other, exchanging their own local information about each target's state with the goal of converging to a single state estimate over the entire network. However, the state estimation problem becomes challenging when some nodes have limited observability of the state. In addition, the consensus estimate is suboptimal when the cross-covariances between the individual state estimates across different nodes are not incorporated in the distributed estimation framework. The cross-covariance is usually neglected because the computational and bandwidth requirements for its computation become unscalable for a large network. These limitations can be overcome by noting that, as the state estimates at different nodes converge, the information at each node becomes correlated. This fact can be utilized to compute the optimal estimate by proper weighting of the prior state and measurement information. Motivated by this idea, we propose information-weighted consensus algorithms for distributed maximum a posteriori parameter estimation, and their extension to the information-weighted consensus filter (ICF) for state estimation. We compare the performance of the ICF with existing consensus algorithms analytically, as well as experimentally by considering the scenario of a distributed camera network under various operating conditions.

Abstract: We consider distributed optimization by a collection of nodes, each having access to its own convex function, whose collective goal is to minimize the sum of the functions. The communications between nodes are described by a time-varying sequence of directed graphs, which is uniformly strongly connected. For such communications, assuming that every node knows its out-degree, we develop a broadcast-based algorithm, termed the subgradient-push, which steers every node to an optimal value under a standard assumption of subgradient boundedness. The subgradient-push requires no knowledge of either the number of agents or the graph sequence to implement. Our analysis shows that the subgradient-push algorithm converges at a rate of $O(\ln(t)/\sqrt{t})$, where the constant depends on the initial values at the nodes, the subgradient norms, and, more interestingly, on both the consensus speed and the imbalances of influence among the nodes.

Abstract: We study the problem of containing spreading processes in arbitrary directed networks by distributing protection resources throughout the nodes of the network. We consider two types of protection resources are available: (i) Preventive resources able to defend nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the nodes of the network. We analyze these questions in the context of viral spreading processes in directed networks. We study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of containment, and (ii) when a budget is not specified, find the minimum budget required to control the spreading process. We show that both resource allocation problems can be solved in polynomial time using Geometric Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide class of cost functions. Furthermore, our approach allows to optimize simultaneously over both preventive and corrective resources, even in the case of cost functions being node-dependent. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through an air transportation network.

Abstract: In energy harvesting communications, users transmit messages using energy harvested from nature during the course of communication. With an optimum transmit policy, the performance of the system depends only on the energy arrival profiles. In this paper, we introduce the concept of energy cooperation, where a user wirelessly transmits a portion of its energy to another energy harvesting user. This enables shaping and optimization of the energy arrivals at the energy-receiving node, and improves the overall system performance, despite the loss incurred in energy transfer. We consider several basic multi-user network structures with energy harvesting and wireless energy transfer capabilities: relay channel, two-way channel and multiple access channel. We determine energy management policies that maximize the system throughput within a given duration using a Lagrangian formulation and the resulting KKT optimality conditions. We develop a two-dimensional directional water-filling algorithm which optimally controls the flow of harvested energy in two dimensions: in time (from past to future) and among users (from energy-transferring to energy-receiving) and show that a generalized version of this algorithm achieves the boundary of the capacity region of the two-way channel.

Abstract: With the introduction of Software Defined Networking (SDN), the concept of an external and optionally centralized network control plane, i.e. controller, is drawing the attention of researchers and industry. A particularly important task in the SDN context is the placement of such external resources in the network. In this paper, we discuss important aspects of the controller placement problem with a focus on SDN-based core networks, including different types of resilience and failure tolerance. When several performance and resilience metrics are considered, there is usually no single best controller placement solution, but a trade-off between these metrics. We introduce our framework for resilient Pareto-based Optimal COntroller-placement (POCO) that provides the operator of a network with all Pareto-optimal placements. The ideas and mechanisms are illustrated using the Internet2 OS3E topology and further evaluated on more than 140 topologies of the Topology Zoo. In particular, our findings reveal that for most of the topologies more than 20% of all nodes need to be controllers to assure a continuous connection of all nodes to one of the controllers in any arbitrary double link or node failure scenario.

Abstract: Device-to-device (D2D) communications has been proposed in the literature as an underlay approach to cellular networks to allow direct transmission between two cellular devices with local communication needs. In this paper, we consider a scenario of D2D communications overlaying a cellular network and propose a new spectrum sharing protocol, which allows the D2D users to communicate bi-directionally with each other while assisting the two-way communications between the cellular base station (BS) and the cellular user (CU). We derive the achievable rate region of the sum rate of the D2D transmissions versus that of the cellular transmissions. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. Since either of the two D2D users can be the relay and there can exist multiple pairs of D2D users, we also consider the relay selection from the potential D2D users. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular users. In addition, relay selection can achieve further improvement in the sum rate of the cellular links.

Abstract: In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or without an adaptive strategy, which require global information of the underlying network such as the eigenvalues of the coupling matrix of the whole network or a centralized adaptive control scheme, we propose a novel decentralized adaptive pinning-control scheme for cluster synchronization of undirected networks using a local adaptive strategy on both coupling strengths and feedback gains. By introducing this local adaptive strategy on each node, we show that the network can synchronize using weak coupling strengths and small feedback gains. Finally, we present some simulations to verify and illustrate the theoretical results.

Abstract: The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and –more recently– correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems.

Abstract: This paper studies the problem of controlling complex networks, that is, 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 (i) we propose a metric to quantify the difficulty of the control problem as a function of the required control energy, (ii) 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 (iii) 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: (i) if the number of control nodes is constant, then the control energy increases exponentially with the number of network nodes, (ii) 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 (iii) 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.

Abstract: The control of a complex network can be achieved by different combinations of relatively few driver nodes. Tao Jia and colleagues show that this can lead to two distinct control modes—centralized or distributed—that determine the number of nodes that can act as driver node.

Abstract: Identifying the node spreading influence in networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the shortest distance between a target node and the node set with the highest k -core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barabasi–Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree k , closeness centrality, k -shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.

Abstract: To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands.

Abstract: In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to address these questions, even when the amount of data is not enough to induce a high scoring network. In this paper we propose Efron's Bootstrap as a computationally efficient approach for answering these questions. In addition, we propose to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.

Abstract: The design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks. In this paper, a new centrality measure is proposed based on the Dempster–Shafer evidence theory. The proposed measure trades off between the degree and strength of every node in a weighted network. The influences of both the degree and the strength of each node are represented by basic probability assignment (BPA). The proposed centrality measure is determined by the combination of these BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method.

Abstract: Systems, methods, and instrumentalities are disclosed such that a WTRU may obtain network operator agnostic wireless access for a service. The WTRU may receive a system information (SI) broadcast from a radio access network (RAN) node. The SI broadcast may indicate that an operator of the RAN node supports service-based network access. The WTRU may send a service request to a virtual service provider to request the service. The WTRU may receive a service response from the virtual service provider. The WTRU may receive a service response from the virtual service provider, the service response indicating a mobile network operator (MNO) to use for obtaining the service and subscription information for accessing the MNO, wherein the MNO is different than the operator of the RAN node. The WTRU may access the MNO to obtain the service.

Abstract: Synchronous local and cross-site switchover and switchback operations of a node in a disaster recovery (DR) group are described. In one embodiment, during switchover, a takeover node receives a failover request and responsively identifies a first partner node in a first cluster and a second partner node in a second cluster. The first partner node and the takeover node form a first high-availability (HA) group and the second partner node and a third partner node in the second cluster form a second HA group. The first and second HA groups form the DR group and share a storage fabric. The takeover node synchronously restores client access requests associated with a failed partner node at the takeover node.

Abstract: An on-demand compute environment comprises a plurality of nodes within an on-demand compute environment available for provisioning and a slave management module operating on a dedicated node within the on-demand compute environment, wherein upon instructions from a master management module at a local compute environment, the slave management module modifies at least one node of the plurality of nodes.

Abstract: Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10-20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves extensive debat

Abstract: The present disclosure describes a microprocessor executable remote control module operable to receive, via a remote node, a command from a vehicle owner to configure and/or alter and/or determine a state of a selected vehicle component and, when the vehicle owner is authenticated successfully by the remote control module, to configure and/or alter and/or determine a state of the selected vehicle component.

Abstract: Stochastic stability for centralized time-varying Kalman filtering over a wireless sensor network with correlated fading channels is studied. On their route to the gateway, sensor packets, possibly aggregated with measurements from several nodes, may be dropped because of fading links. To study this situation, we introduce a network state process, which describes a finite set of configurations of the radio environment. The network state characterizes the channel gain distributions of the links, which are allowed to be correlated between each other. Temporal correlations of channel gains are modeled by allowing the network state process to form a (semi-)Markov chain. We establish sufficient conditions that ensure the Kalman filter to be exponentially bounded. In the one-sensor case, this new stability condition is shown to include previous results obtained in the literature as special cases. The results also hold when using power and bit-rate control policies, where the transmission power and bit-rate of each node are nonlinear mapping of the network state and channel gains.

Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.

Abstract: An integrated structural health monitoring (SHM) system for highway bridges is presented in this article. The system described is based on a customized wireless sensor network platform with a flexible design that provides a variety of sensors that are typical to SHM. These sensors include accelerometers, strain gauges, and temperature sensors with ultra-low power consumption. A S-Mote node, an acceleration sensor board, and a strain sensor board are developed to satisfy the requirements of bridge structural monitoring. The article discusses how communication software components are integrated within TinyOS operating system to provide a flexible software platform whereas the data processing software performs analysis of acceleration, dynamic displacement, and dynamic strain data. The prototype system comprises a nearly linear multi-hop topology and is deployed on an in-service highway bridge. Data acquired from the system are used to examine network performance and to help evaluate the state of the bridge. Experimental results presented in the article show that the system enables continuous or regular interval monitoring for in-service highway bridges.