Entanglement distribution is a core function of quantum networks, and the paths used for this purpose are composed of quantum and conventional network components and are routed through physical facility sites. A game theoretic model is formulated for the defense of entanglement paths in a quantum network infrastructure by modeling the correlations and probabilities of reinforcement and failure of its components. A sum-form utility function is used to capture the cost-benefit trade-offs in reinforcing the entanglement path components to defend against their failures and attacks. Under Nash Equilibrium criteria, estimates of survival probabilities of entanglement paths are derived using the parameters and correlations of quantum, conventional, hybrid, and facility components. They provide insights into the dependencies of entanglement paths on its components, including cross-boundary effects of conventional, quantum, and facility components.

With the rapid advancement of robotics technologies, a group of robots are able to communicate with one another by wireless transmissions and form a robot swarm. Robot swarm has many applications and a typical one is target search in which swarm robots are sent to places that might be dangerous for human workers, and they coordinate with one another to search for targets such as survivors in a disaster. However, motion coordination of swarm robots for target search has received little attention especially when the targets are mobile. In this work, we develop a motion coordination algorithm for swarm robots to search for targets in an unknown area. Our basic idea is to divide the search area into grids and build a gray-scale map in which each grid is associated with a gray scale indicating the efficiency of searching targets in this grid. The Voronoi diagram is adopted to coordinate swarm robots to search different portions of the search area for maximizing search efficiency. By theoretical analysis, our motion coordination algorithm is validated to ensure that all static targets are guaranteed to be found. We derive an upper-bound on the total time for robots to traverse all the grids in the search area. Extensive simulations are conducted and the results show that the proposed motion coordination algorithm outperforms the state-of-the-art and achieves a success rate of over 90% in finding all mobile targets with low search latency.

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying $\mathrm{Pu} / \mathrm{Np}$ target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Fundamentally and practically, quantum networks and conventional networks are inextricably tied, since the basic quantum protocols such as teleportation require both networks and the conventional network fiber is also used for the quantum network. A Recursive System of Systems (RSOS) model is developed for quantum-conventional (QC) networks by modeling the correlations at various levels based on the failure and attack modes of quantum, conventional and hybrid components and the propagative effects across QC boundaries. A game-theoretic formulation is developed to capture the cost-benefit trade-offs of the provider in defending against component attacks, using sum-form utility functions. By applying the Nash Equilibrium results, the conditions and sensitivity functions of the survival probabilities of a QC network at different levels are derived using the strong dependencies between quantum and conventional infrastructures. The results provide insights into the dependencies between conventional and quantum networks, including cross QC boundary effects in terms of disruption impact of conventional networks on quantum networks, and vice versa.

Networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.

Minimizing energy consumption of data centers is important to reduce carbon emissions. Virtual machines (VMs) consolidation is a typical technique to utilize the available data center resources and thus improve energy efficiency. The cooling systems consume up to 50% of the total data center electricity. In this work, we investigate the joint energy optimization of cooling systems and VM consolidations in cloud data centers. We propose a cooling-aware VM consolidation (CAVC for short) algorithm to the problem. The CAVC algorithm is a two-stage solution: 1) we first relax the constraints of the problem and determine an optimal number of physical machines (PMs) and an optimal CPU utilization of the PMs that yields the minimum cooling power; and 2) based on the initial solution of the first stage, we consolidate the VMs into the predetermined PMs with the predetermined CPU utilization ratio as much as possible. To the best of authors’ knowledge, this is the first work that jointly considers the VM consolidation and the cooling systems in minimizing energy consumption of cloud data centers. We derive an approximation ratio of CAVC over the optimal solution. The real-world data set (i.e., Google cluster data) is adopted in the simulations and the results show that the CAVC algorithm yields very close energy consumption to the theoretical lower bound.

This research studies defense strategies of an interdependent system in the face of rational attacks. We propose a sequential game between an attacker and a defender for an interdependent System of Systems (SoS) to explore the effect of interdependency on an optimal defense strategy. We develop an algorithm of backward induction to obtain the Nash equilibrium of the game. The attacker is the first mover as he applies an attack strategy on constituent systems that maximizes his utility. The defender observes and responds by a defense strategy that maximizes her utility. Both players' utilities are expressed as the difference between a player's reward due to SoS functionality (dysfunctionality) and the cost of the action. The sensitivity analysis compares the effects of different parameters on the attacker's and defender's strategies such as the effectiveness of defense (attack), the unit cost of defense (attack) and the interdependency level of constituent systems.

We consider a class of Recursive System of Systems (RSoS), wherein systems are recursively defined and the basic systems at finest level are composed of discrete cyber and physical components. This formulation captures the models of systems that are adaptively refined to account for their varied structure, such as sites of a heterogeneous distributed computing infrastructure. The components can be disrupted by cyber or physical means, and can also be suitably reinforced to survive the attacks. We characterize the disruptions at each level of recursion using aggregate failure correlation functions that specify the conditional failure probability of RSoS given the failure of an individual system at that level. At finest levels, the survival probabilities of basic systems satisfy simple product-form, first-order differential conditions using the multiplier functions, which generalize conditions based on contest success functions and statistical independence of component survival probabilities. We formulate the problem of ensuring the performance of RSoS as a game between an attacker and a provider, each with a utility function composed of a survival probability term and a cost term, both expressed in terms of the number of basic system components attacked and reinforced. We derive sensitivity functions at Nash Equilibrium that highlight the dependence of survival probabilities of systems on cost terms, correlation functions, and their partial derivatives. We apply these results to a simplified model of distributed high-performance computing infrastructures.

Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the systems play another critical role in determining the infrastructure robustness specified by its survival probability. In this paper, we develop game-theoretic models between a defender and an attacker for a generic system of systems using inherent parameters and conditional survival probabilities that characterize the interdependencies. We derive Nash Equilibrium conditions for the cases of interdependent and independent systems of systems under sum-form utility functions. We derive expressions for the infrastructure survival probability that capture its dependence on cost and system parameters, and also on dependencies that are specified by conditional probabilities. We apply the results to cyber-physical systems which show the effects on system survival probability due to defense and attack intensities, inherent robustness, unit cost, target valuation, and interdependencies.

We consider an infrastructure consisting of a network of systems each composed of discrete components that can be reinforced at a certain cost to guard against attacks. The network provides the vital connectivity between systems, and hence plays a critical, asymmetric role in the infrastructure operations. We characterize the system-level correlations using the aggregate failure correlation function that specifies the infrastructure failure probability given the failure of an individual system or network. The survival probabilities of systems and network satisfy first-order differential conditions that capture the component-level correlations. We formulate the problem of ensuring the infrastructure survival as a game between an attacker and a provider, using the sum-form and product-form utility functions, each composed of a survival probability term and a cost term. We derive Nash Equilibrium conditions which provide expressions for individual system survival probabilities, and also the expected capacity specified by the total number of operational components. These expressions differ only in a single term for the sum-form and product-form utilities, despite their significant differences. We apply these results to simplified models of distributed cloud computing infrastructures.

Smart meters are integral to demand response in emerging smart grids, by reporting the electricity consumption of users to serve application needs. But reporting real-time usage information for individual households raises privacy concerns. Existing techniques to guarantee differential privacy (DP) of smart meter users either are not fault tolerant or achieve (possibly partial) fault tolerance at high communication overheads. In this paper, we propose a fault-tolerant protocol for smart metering that can handle general communication failures while ensuring DP with significantly improved efficiency and lower errors compared with the state of the art. Our protocol handles fail-stop faults proactively by using a novel design of future ciphertexts, and distributes trust among the smart meters by sharing secret keys among them. We prove the DP properties of our protocol and analyze its advantages in fault tolerance, accuracy, and communication efficiency relative to competing techniques. We illustrate our analysis by simulations driven by real-world traces of electricity consumption.

Privacy protection of time-series data, such as traces of household electricity usage reported by smart meters, is of much practical importance. Solutions are available to improve data privacy by perturbing clear traces to produce noisy versions visible to adversaries, e.g., in battery-based load hiding (BLH) against non-intrusive load monitoring (NILM). A foundational task for research progress in the area is the definition of privacy measures that can truly evaluate the effectiveness of proposed protection methods. It is a difficult problem since resilience against any attack algorithms known to the designer is inconclusive, given that adversaries could discover or indeed already know stronger algorithms for attacks. A more basic measure is information-theoretic in nature, which quantifies the inherent information available for exploitation by an adversary, independent of how the adversary exploits it or indeed any assumed computational limitations of the adversary. In this paper, we analyze information-theoretic measures for privacy protection and apply them to several existing protection methods against NILM. We argue that although these measures abstract away the details of attacks, the kind of information the adversary considers plays a key role in the evaluation, and that a new measure of offline conditional entropy is better suited for evaluating the privacy of perturbed real-world time-series data, compared with other existing measures.

Smart meters are integral to demand response in emerging smart grids, by reporting the electricity consumption of users to serve application needs. But reporting real-time usage information for individual households raises privacy concerns. Existing techniques to guarantee differential privacy (DP) of smart meter users either are not fault tolerant or achieve (possibly partial) fault tolerance at high communication overheads. In this paper, we propose a fault-tolerant protocol for smart metering that can handle general communication failures while ensuring DP with significantly improved efficiency and lower errors compared with the state of the art. Our protocol handles fail-stop faults proactively by using a novel design of future ciphertexts, and distributes trust among the smart meters by sharing secret keys among them. We prove the DP properties of our protocol and analyze its advantages in fault tolerance, accuracy, and communication efficiency relative to competing techniques. We illustrate our analysis by simulations driven by real-world traces of electricity consumption.

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.

A number of networked infrastructure systems rely on both cyber and physical components for their continued operation. We present graph models for a class of such systems, wherein both cyber and physical parts must be made robust, possibly using different methods at different costs. We present methods for ensuring that the system survives, with specified probability PS, cyber and physical degradations due to natural, incidental, or intentional factors. Based on first and second order statistics of the profiles of passive degradations, we present methods to compute the robustness levels needed to ensure PS. Then, we consider the case of intentional compromises, where cost profiles of the provider and compromiser are known to various extents. We present a game-theoretic formulation based on provider and disrupter cost and benefit functions, and their mutual knowledge. We present strategies and performance boundaries of these formulations in ensuring PS under utility functions that are sums of terms corresponding to infrastructure survival and cyber-physical costs.

We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor's coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain wherein the sensor at PoI i decides to go to the next PoI j with transition probability pij . We use steepest descent to compute the transition probabilities for optimal tradeoff between two performance goals concerning the distributions of per-PoI coverage times and exposure times, respectively. We also discuss how other important goals such as energy efficiency and entropy of the coverage schedule can be addressed. For computational efficiency, we show how to optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that the structure of our problem is complex in that there may exist surprisingly many local optima in the solution space, causing basic steepest descent to get stuck easily at a local optimum. To solve the problem, we show how proper incorporation of noise in the search process can get us out of the local optima with high probability. We provide simulation results to verify the accuracy of our analysis, and show that our method can converge to the globally optimal control parameters under different assigned weights to the performance goals and different initial parameters.

Mobility traces of people and vehicles have been collected and published to assist the design and evaluation of mobilee 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 (captured in both real and synthetic traces), 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.

We analyze the quality of monitoring (QoM) of stochastic events by a periodic sensor which monitors a point of interest (PoI) for q time every p time. We show how the amount of information captured at a PoI is affected by the proportion q/p , the time interval p over which the proportion is achieved, the event type in terms of its stochastic arrival dynamics and staying times and the utility function. The periodic PoI sensor schedule happens in two broad contexts. In the case of static sensors, a sensor monitoring a PoI may be periodically turned off to conserve energy, thereby extending the lifetime of the monitoring until the sensor can be recharged or replaced. In the case of mobile sensors, a sensor may move between the PoIs in a repeating visit schedule. In this case, the PoIs may vary in importance, and the scheduling objective is to distribute the sensor's coverage time in proportion to the importance levels of the PoIs. Based on our QoM analysis, we optimize a class of periodic mobile coverage schedules that can achieve such proportional sharing while maximizing the QoM of the total system.

We analyze the ability of a stochastic coverage algorithm to achieve both accurate threat-based coverage and effective information capture. When mobile sensors are used to cover the region over time, the goal of threat-based coverage is to allocate the sensors' coverage time between the subregions in proportion to their threat levels. We show that, in contrast to prior results on mobile coverage for maximizing simple event capture, limiting mobility by strategically pausing the sensor is important for threat-based coverage of physical world monitoring. Besides being energy efficient, pausing has two desirable effects. First, it can improve the accuracy of the threat-based coverage, in particular, the accuracy increases monotonically with a pause time parameter, and a large enough parameter will ensure exact matching of the sensor's coverage profile with the region's threat profile. Second, diverse natural phenomena require a non-negligible sensing time to overcome statistical uncertainties posed by the random nature of the phenomena. Suitable pausing allows a subregion to be observed long enough for reliable results.

Mobile sensors can be used to effect complete coverage of a surveillance area for a given threat over time, thereby reducing the number of sensors necessary. The surveillance area may have a given threat profile as determined by the kind of threat, and accompanying meteorological, environmental, and human factors. In planning the movement of sensors, areas that are deemed higher threat should receive proportionately higher coverage. We propose a coverage algorithm for mobile sensors to achieve a coverage that will match - over the long term and as quantified by an RMSE metric - a given threat profile. Moreover, the algorithm has the following desirable properties: 1) stochastic, so that it is robust to contingencies and makes it hard for an adversary to anticipate the sensor's movement, 2) efficient, and 3) practical, by avoiding movement over inaccessible areas. Further to matching, we argue that a fairness measure of performance over the shorter time scale is also important. We show that the RMSE and fairness are, in general, antagonistic, and argue for the need of a combined measure of performance, which we call efficacy. We show how a pause time parameter of the coverage algorithm can be used to control the trade-off between the RMSE and fairness, and present an efficient offline algorithm to determine the optimal pause time maximizing the efficacy. Finally, we discuss the effects of multiple sensors, under both independent and coordinated operation. Extensive simulation results - under realistic coverage scenarios - are presented for performance evaluation.

We analyze the quality of monitoring (QoM) of stochastic events by a periodic sensor which monitors a point of interest (PoI) for q time every p time. We show how the amount of information captured at a PoI is affected by the proportion q/p, the time interval p over which the proportion is achieved, the event type, and the stochastic event arrival dynamics and staying times. The periodic PoI sensor schedule happens in two broad contexts. In the case of static sensors, a sensor monitoring a PoI may be periodically turned off to conserve energy, thereby extending the lifetime of the monitoring until the sensor can be recharged or replaced. In the case of mobile sensors, a sensor may move between the PoIs in a repeating visit schedule. In this case, the PoIs may vary in importance, and the scheduling objective is to distribute the sensor's coverage time in proportion to the importance levels of the PoIs. Based on our QoM analysis, we optimize a class of periodic mobile coverage schedules that can achieve such proportional sharing while maximizing the QoM of the total system.

We present the design, implementation, and experimental evaluation of a wireless sensor network for near real-time structural health monitoring. We use simple custom-built gages to unequivocally detect cracks in critical structural elements. The main data reports have a low data rate and are naturally resilient to loss. We show how a variety of low-cost, off-the-shelf data acquisition/communication devices can be used to support remote monitoring by a control center. The heterogeneous hardware is accommodated by the use of open technology standards, and a software architecture that is portable, modular, and highly configurable. We present an experimental evaluation of our structural assessment network, using a full-scale three-story reinforced concrete building, subjected to lateral forces emulating forces induced by earthquakes. Our results show that a set of 12 strategically positioned sensors on the three floors achieved a zero false-alarm rate, in the sense that each reported breakage can be traced to cracks exceeding the specified total width, and a 100% detection rate for cracks that are covered by a sensor.

The localization of a radioactive source can be solved in closed-form using 4 ideal sensors and the Apollonius circle in a noise- and error-free environment. When measurement errors and noise such as background radiation are considered, a larger number of sensors is needed to produce accurate results, particularly for extremely low source intensities. In this paper, we present an efficient fusion algorithm that can exploit measurements from n sensors to improve the localization accuracy, and show how the accuracy scales with n. We report testbed results for a 0.911 μCi source to illustrate the effectiveness of our algorithm, in particular performance comparisons with state-of-the-art fusion algorithms based on Mean of Estimates (MoE) and Maximum Likelihood Estimation (MLE). We show that ITP is more accurate than MoE, whereas the choice between ITP and MLE is generally a tradeoff between accuracy and run time efficiency. Higher-intensity radioactive sources are not safe for actual experiments. In this case, we present simulation results based on a validated simulation model. We show that a low-intensity 400 μCi source, similar to the radioactivity of a concealed dirty bomb, can be localized to within 32.5 m using a sensor density of about 1 per 1100 m 2 in a surveillance area.

No abstract available.