Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time synchronized via the Global Positioning System. We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we realize the first deployed fiber network demonstration of remote state preparation (RSP), a fundamental quantum communications protocol with utility for performing remote private “blind” quantum computing. We further demonstrate RSP not only at one location but over three nodes in three locations. In general, our results highlight an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands.

The problem of navigating an autonomous mobile robot through unexplored terrain of obstacles is discussed. The case when the obstacles are "known" has been extensively studied in literature. Completely unexplored obstacle terrain is considered. In this case, the process of navigation involves both learning the information about the obstacle terrain and path planning. An algorithm is presented to navigate a robot in an unexplored terrain that is arbitrarily populated with disjoint convex polygonal obstacles in the plane. The navigation process is constituted by a number of traversals; each traversal is from an arbitrary source point to an arbitrary destination point. The proposed algorithm is proven to yield a convergent solution to each path of traversal. Initially, the terrain is explored using a rather primitive sensor, and the paths of traversal made may be suboptimal. The visibility graph that models the obstacle terrain is incrementally constructed by integrating the information about the paths traversed so far. At any stage of learning, the partially learned terrain model is represented as a learned visibility graph, and it is updated after each traversal. It is proven that the learned visibility graph converges to the visibility graph with probability one when the source and destination points are chosen randomly. Ultimately, the availability of the complete visibility graph enables the robot to plan globally optimal paths and also obviates the further usage of sensors.

Mobility traces of people and vehicles have been collected and published to assist the design and evaluation of mobile networks, such as large-scale urban sensing networks. Although the published traces are often made anonymous in that the true identities of nodes are replaced by random identifiers, the privacy concern remains. This is because in real life, nodes are open to observations in public spaces, or they may voluntarily or inadvertently disclose partial knowledge of their whereabouts. Thus, snapshots of nodes' location information can be learned by interested third parties, e.g., directly through chance/engineered meetings between the nodes and their observers, or indirectly through casual conversations or other information sources about people. In this paper, we investigate how an adversary, when equipped with a small amount of the snapshot information termed as side information, can infer an extended view of the whereabouts of a victim node appearing in an anonymous trace. Our results quantify the loss of victim nodes' privacy as a function of the nodal mobility, the inference strategies of adversaries, and any noise that may appear in the trace or the side information. Generally, our results indicate that the privacy concern is significant in that a relatively small amount of side information is sufficient for the adversary to infer the true identity (either uniquely or with high probability) of a victim in a set of anonymous traces. For instance, an adversary is able to identify the trace of 30%-50% of the victims when she has collected 10 pieces of side information about a victim.

Electricity grids are critical infrastructures. They are credible targets of active (e.g., terrorist) attacks since their disruption may lead to sizable losses economically and in human lives. It is thus crucial to develop decision support that can guide administrators in deploying defense resources for system security and reliability. Prior work on the defense of critical infrastructures has typically used static or Stackelberg games. These approaches view network interdictions as one-time events. However, infrastructure protection is also a continual process in which the defender and attacker interact to produce dynamic states affecting their best actions, as witnessed in the continual attack and defense of transmission networks in Colombia and Yemen. In this paper, we use zero-sum Markov games to model these interactions subject to underlying uncertainties of real-world events and actions. We solve equilibrium mixed strategies of the players that maximize their respective minimum payoffs with a time-decayed metric. We also show how the defender can use deception as a defense mechanism. Using results for a 5-bus system, a WECC 9-bus system, and an IEEE standard 14-bus system, we illustrate that our game model can provide useful insights. We also contrast our results with those of static games, and quantify the gain in defender payoff due to misinformation of the attacker.

Detection of random signals under a distributed setting is considered. Due to the random nature of the spatial phenomenon being observed, the sensor decisions collected at the fusion center are correlated. Assuming that local detectors are single threshold binary quantizers, a novel approach for the fusion of correlated decisions is proposed using the theory of copulas. The proposed approach assumes only the knowledge of the marginal distribution of sensor observations but no prior knowledge of their joint distribution. Using a Neyman-Pearson (NP) framework for detection at the fusion center, the optimal fusion rule is derived. An example involving the detection of nuclear radiation is presented to illustrate the proposed approach, and results demonstrating the efficiency of the copula-based fusion rule are shown.

UltraScienceNet is an experimental wide area network testbed to enable the development of networking technologies required for next-generation large-scale scientific applications. It provides on-demand dedicated high-bandwidth channels for large data transfers, and also high-resolution high-precision channels for fine control operations. In the initial deployment its data plane consists of several thousand miles of dual 10 Gb/s lambdas. The channels are provisioned on demand using layer 1 and 2 switches in the backbone and multiple service provisioning platforms at the edges in a flexible configuration using a secure control plane. A centralized scheduler is employed to compute future channel allocations, and a signaling daemon is used to generate the configuration signals to switches at appropriate times. The control plane is implemented using an out-of-band virtual private network, which encrypts the switching signals, and also provides authenticated user and application access. Transport experiments are conducted on a smaller test connection that provides us useful information about the basic properties and issues of utilizing dedicated channels in applications.