In this article, we establish a comprehensive theoretical framework for remote estimation in a networked system composed of a source that is observed by a sensor, a remote monitor that needs to estimate the state of the source in real time, and a communication channel that connects the source to the monitor. The source is a partially observable dynamical process, and the communication channel is a packet-erasure channel with feedback. We consider a novel communication model that captures implicit information. Our main objective is to identify the optimal strategies and the fundamental performance limits of the underlying system in the sense of a causal tradeoff between the packet rate and the mean square error when both forward and backward channels are unreliable. We characterise an optimal coding policy profile consisting of a scheduling policy for an encoder and an estimation policy for a decoder, collocated with the source and the monitor, respectively. We derive the recursive equations that must be solved online by the encoder and the decoder. In addition, we prove that the value function, originally defined over an expanding information set, admits a lower-dimensional representation depending only on two variables. We discuss the structural properties of the optimal policies, and analyse the computational complexity of an algorithm proposed for their computation. We then examine a range of special cases derived from our main theoretical results. We complement the theoretical results with a numerical analysis, and compare the performance of different remote estimation tasks in various operating regimes.

This research presents a novel approach to bridging the gap between the interpretable and flexible nature of Behavior Trees (BTs) and the rigorous formal verification and synthesis capabilities of temporal logics. Temporal logics, such as Linear Temporal Logic (LTL) and Metric Interval Temporal Logic (MITL), are widely used for task specification due to their intuitive syntax for expressing temporally evolving behaviors. However, encoding complex task dependencies and recovery actions in temporal logic can lead to intractability. BTs, known for their modular structure and dynamic adaptability, have gained popularity in robotics for task specification. Despite the advantages of BTs, their flexible structure complicates formal analysis for safety and performance guarantees, limiting their use in control synthesis. This work presents a novel approach by translating BTs into Timed Automata (TA), thus enabling falsification (counterexample generation) with Uppaal to identify inconsistencies and ensure language completeness, especially when defined with timing constraints. This integration allows for the detection of potential inconsistencies, the monitoring of temporal properties, and the synthesis of automaton and sampling based control strategies that guarantee satisfaction of task objectives.

Unmanned Aerial Vehicles (UAVs) are envisioned as vital components of next-generation networks, driving diverse applications across various domains. UAV-enabled networks typically encompass diverse edge devices, including UAVs, ground stations, sensors, and other IoT devices, all generating vast amounts of data. Hence, in recent years, there has been a significant surge of interest in federated learning (FL) within UAV-enabled networks. However, most of the approaches in the state-of-the-art suffer from a single point of failure as well as massive communication overhead and excessive delay due to the existence of a central aggregator. To tackle this shortcoming, this paper proposes a framework for decentralized FL in UAV-enabled networks. Our approach consists of two parts; i) First, we propose a UAV placement scheme while ensuring the connectivity of the network of deployed UAVs. ii) Next, we use a consensus-based approach for decentralized FL among the UAV agents. We verify the effectiveness of our approach via extensive numerical simulation.

While reinforcement learning (RL) has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive RL methods are being thoroughly studied. In this work, we provide a definition of robust RL policies and formulate a risk-sensitive RL problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely used Monte Carlo policy gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments.

In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation.

Distributed massive MIMO networks are envisioned to realize cooperative multi-point transmission in next-generation wireless systems. For efficient cooperative hybrid beamforming, the cluster of access points (APs) needs to obtain precise estimates of the uplink channel to perform reliable downlink precoding. However, due to the radio frequency (RF) impairments between the transceivers at the two en-points of the wireless channel, full channel reciprocity does not hold which results in performance degradation in the cooperative hybrid beamforming (CHBF) unless a suitable reciprocity calibration mechanism is in place. We propose a two-step approach to calibrate any two hybrid nodes in the distributed MIMO system. We then present and utilize the novel concept of reciprocal tandem to propose a low-complexity approach for jointly calibrating the cluster of APs and estimating the downlink channel. Finally, we validate our calibration technique's effectiveness through numerical simulation.

We study feedback control of a dynamical process over a lossy channel equipped with a hybrid automatic repeat request protocol that connects a sensor to an actuator. The dynamical process is modeled by a Gauss-Markov process, and the lossy channel by a packet-erasure channel with ideal feedback. We suppose that data is communicated in the format of packets with negligible quantization error. In such a networked control system, whenever a packet loss occurs, there exists a tradeoff between transmitting new sensory information with a lower success probability and retransmitting previously failed sensory information with a higher success probability. In essence, an inherent tradeoff between freshness and reliability. To address this tradeoff, we consider a linear-quadratic-regulator performance index, which penalizes state deviations and control efforts over a finite horizon, and jointly design optimal policies for an encoder and a decoder, which are collocated with the sensor and the actuator, respectively. Our emphasis here lies specifically on designing switching and control policies, rather than error-correcting codes. We derive the structural properties of the optimal encoding and decoding policies. We show that the former is a threshold switching policy and the latter is a certainty-equivalent control policy. In addition, we specify the iterative equations that the encoder and the decoder need to solve in order to implement the optimal policies.

We study networked control of a dynamical process over a lossy channel with a hybrid automatic repeat request protocol that connects a sensor to an actuator. The dynamical process is modeled by a Gauss-Markov process, and the lossy channel by a packet-erasure channel with ideal feedback. We suppose that data is communicated in the format of packets with negligible quantization error. In such a networked control system, whenever a packet loss occurs, there exists a tradeoff between transmitting new sensory information with a lower success probability and retransmitting previously failed sensory information with a higher success probability. In essence, an inherent tradeoff between freshness and reliability. To address this tradeoff, we consider a linear-quadratic-regulator performance index, which penalizes state deviations and control efforts over a finite horizon, and jointly design optimal encoding and decoding policies for the encoder and the decoder, which are collocated with the sensor and the actuator, respectively. Our emphasis here lies specifically on designing switching and control policies, rather than error-correcting codes. We show that the optimal encoding policy is a threshold switching policy and the optimal decoding policy is a certainty-equivalent control policy. In addition, we determine the equations that the encoder and the decoder need to solve in order to implement the optimal policies. More specifically, we show that the encoder must solve the Kalman filtering equations, a mismatch linear equation, and a Bellman optimality equation, while the decoder must solve a linear filtering equation and an algebraic Riccati equation.

In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results - together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method - further validate our theoretical propositions.

Low-complexity estimation and correction of carrier frequency offset (CFO) are essential in orthogonal frequency division multiplexing (OFDM). In this paper, we propose a low-overhead blind CFO estimation technique based on cyclic prefix (CP), in multi-input multi-output (MIMO)-OFDM systems. We propose to use antenna diversity for CFO estimation. Given that the RF chains for all antenna elements at a communication node share the same clock, the carrier frequency offset (CFO) between two points may be estimated by using the combination of the received signal at all antennas. We improve our method by combining the antenna diversity with time diversity by considering the CP for multiple OFDM symbols. We provide a closed-form expression for CFO estimation and present algorithms that can considerably improve the CFO estimation performance at the expense of a linear increase in computational complexity. We validate the effectiveness of our estimation scheme via extensive numerical analysis.

Constant temporospatial variations in the user demand complicate the end-to-end (E2E) network slice (NS) resource provisioning beyond the limits of the existing best-effort schemes that are only effective under accurate demand forecasts for all NSs. This paper proposes a practical two-time-scale resource allocation framework for E2E network slicing under demand uncertainty. At each macro-scale instance, we assume that only the spatial probability distribution of the NS demands is available. We formulate the NSs resource allocation problem as a stochastic mixed integer program (SMIP) with the objective of minimizing the total CN and RAN resource costs. At each microscale instance, given the exact NSs demand profiles known at operation time, a linear program is solved to jointly minimize the unsupported traffic and RAN cost. We verify the effectiveness of our resource allocation scheme through numerical experiments.

A Network Slice (NS) is a set of network resources deployed to deliver premium service to a group of users as described in a Service Level Agreement (SLA). We consider that premium service entails per user Quality of Service (QoS) requirements instead of aggregate NS metrics as in the majority of the literature. Since the users in a NS may run applications with different QoS requirements, aggregate metrics are inadequate. Their inefficiency is even more pronounced in Radio Access Network Slices (RANSs). In this case, even if all users run the same application, provisioning bandwidth based on aggregate metrics may violate the QoS requirements of users experiencing poorer channel conditions. To resolve this issue, we propose a method to compute the bandwidth required to deliver per user average packet delay guarantees in enhanced Mobile Broadband (eMBB) RANSs by dimensioning a multiclass queueing system. Our method considers a varying number of NS users over time and allows the online adaptation of the NS bandwidth to its current traffic load, which significantly limits bandwidth overprovisioning. We also describe in detail how an SLA is reached between the tenant and the Network Operator (NO) based on the cdf of the required NS bandwidth. We conduct simulations to show the need for per user guarantees and the efficiency of our method.

Time Division Duplex (TDD)-based distributed massive MIMO systems are envisioned as candidate solution for the physical layer of 6G multi-antenna systems supporting cooperative hybrid beamforming that heavily relies on the obtained uplink channel estimates for efficient coherent downlink pre-coding. However, due to the hardware impairment between the transmitter and the receiver, full channel reciprocity does not hold between the downlink and uplink direction. Such reciprocity mismatch deteriorates the performance of mm-Wave hybrid beam-forming and has to be estimated and compensated for, to avoid performance degradation in the co-operative hybrid beamforming.In this paper, we address the channel reciprocity calibration between any two nodes at two levels. We decompose the problem into two sub-problems. In the first sub-problem, we calibrate the digital chain, i.e. obtain the mismatch coefficients of the (DAC/ADC) up to a constant scaling factor. In the second sub-problem, we obtain the (PA/LNA) mismatch coefficients. At each step, we formulate the channel reciprocity calibration as a least-square optimization problem that can efficiently be solved via conventional methods such as alternative optimization with high accuracy. Finally, we verify the performance of our channel reciprocity calibration approach through extensive numerical experiments.

Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention, both from industry and academia, over the past several years. Beam placement is among the major resource allocation problems in multi-beam NGSO systems. In this paper, we formulate the beam placement problem as a Euclidean disk cover optimization model. We aim at minimizing the number of placed beams while satisfying the total downlink traffic demand of targeted ground terminals without exceeding the capacity of the placed beams. We present a low-complexity deterministic annealing (DA)-based algorithm to solve the NP-hard optimization model for near-optimal solutions. We further propose an extended variant of the previous model to ensure the traffic assigned to the beams is balanced. We verify the effectiveness of our proposed methods by means of numerical experiments and show that our scheme is superior to the state-of-the-art methods in that it covers the ground users by fewer number of beams on average.

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS$i$states that its resource demand should be met for$P_{i}^{H}$fraction of time, and for$P_{i}^{L}\leq P_{i}^{H}$fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler.

Inherent in virtually every iterative machine learning algorithm is the problem of hyperparameter tuning, which includes three major design parameters: 1) the complexity of the model, e.g., the number of neurons in a neural network; 2) the initial conditions, which heavily affect the behavior of the algorithm; and 3) the dissimilarity measure used to quantify its performance. We introduce an online prototype-based learning algorithm that can be viewed as a progressively growing competitive-learning neural network architecture for classification and clustering. The learning rule of the proposed approach is formulated as an online gradient-free stochastic approximation algorithm that solves a sequence of appropriately defined optimization problems, simulating an annealing process. The annealing nature of the algorithm contributes to avoiding poor local minima, offers robustness with respect to the initial conditions, and provides a means to progressively increase the complexity of the learning model, through an intuitive bifurcation phenomenon. The proposed approach is interpretable, requires minimal hyperparameter tuning, and allows online control over the performance-complexity tradeoff. Finally, we show that Bregman divergences appear naturally as a family of dissimilarity measures that play a central role in both the performance and the computational complexity of the learning algorithm.

Federated learning (FL) has received significant attention from both academia and industry, as an emerging paradigm for building machine learning models in a communication-efficient and privacy preserving manner. It enables potentially a massive number of resource constrained agents (e.g. mobile devices and IoT devices) to train a model by a repeated process of local training on agents and centralized model aggregation on a central server. To overcome the single-point-of-failure and scalability issues of the traditional FL frameworks, decentralized (server-less) FL has been proposed. In a decentralized FL setting, agents implement consensus techniques by exchanging local model updates. Despite bypassing the direct exchange of raw data between the collaborating agents, this scheme is still vulnerable to various security and privacy threats such as data poisoning attack.In this paper, we propose trust as a metric to measure the trustworthiness of the FL agents and thereby enhance the security of the FL training. We first elaborate on trust as a security metric by presenting a mathematical framework for trust computation and aggregation within a multi-agent system. We then discuss how this framework can be incorporated within a decentralized FL setup introducing the trusted decentralized FL algorithm. Finally, we validate our theoretical findings by means of numerical experiments.

In pursuance of the unused spectrum in higher frequencies, millimeter wave (mmWave) bands have a pivotal role. However, the high path-loss and poor scattering associated with mmWave communications highlight the necessity of employing effective beamforming techniques. In order to efficiently search for the beam to serve a user and to jointly serve multiple users it is often required to use a composite beam which consists of multiple disjoint lobes. A composite beam covers multiple desired angular coverage intervals (ACIs) and ideally has maximum and uniform gain (smoothness) within each desired ACI, negligible gain (leakage) outside the desired ACIs, and sharp edges. We propose an algorithm for designing such ideal composite codebook by providing an analytical closed-form solution with low computational complexity. There is a fundamental trade-off between the gain, leakage and smoothness of the beams. Our design allows to achieve different values in such trade-off based on changing the design parameters. We highlight the shortcomings of the uniform linear arrays (ULAs) in building arbitrary composite beams. Consequently, we use a recently introduced twin-ULA (TULA) antenna structure to effectively resolve these inefficiencies. Numerical results are used to validate the theoretical findings.

We propose an online resource allocation scheme for end-to-end network slices. Our aim is to provide guarantees for the overall delay of the slice users, while minimizing the operational costs of the mobile network operator. Hence, we develop our scheme based on an optimization problem, where we jointly perform bandwidth allocation in the radio access network, and service function chain embedding in the core network. We show that the scheme has polynomial time complexity. The performance of our scheme is evaluated via simulations. Results clearly show that the proposed scheme is fast and highly scalable with respect to the number of users.

SDN-enabled Integrated satellite-terrestrial networks (ISTNs), can provide several advantages including global seamless coverage, high reliability, low latency, etc. and can be a key enabler towards next generation networks. To deal with the complexity of the control and management of the integrated network, leveraging the concept of software-defined networking (SDN) will be helpful. In this regard, the SDN controller placement problem in SDN-enabled ISTNs becomes of paramount importance. In this paper, we formulate an optimization problem for the SDN controller placement with the objective of minimizing the average failure probability of SDN control paths to ensure the SDN switches receive the instructions in the most reliable fashion. Simultaneously, we aim at deploying the SDN controllers close to the satellite gateways to ensure the connection between the two layers occurs with the lowest latency. We first model the problem as a mixed integer linear program (MILP). To reduce the time complexity of the MILP model, we use submodular optimization techniques to generate near-optimal solutions in a time-efficient manner. Finally, we verify the effectiveness of our approach by means of simulation, showing that the approximation method results in a reasonable optimality gap with respect to the exact MILP solution.

In the presence of human driven vehicles (HDVs), traffic shock waves are a naturally occurring phenomena, which contributes to congestion and efficiency degradation in highway networks. The introduction of connected autonomous vehicles (CAVs) with advanced sensing, actuation and communication capabilities allow new approaches in control to be applied in order to solve the problem of shock waves. This work on shock wave dissipation, introduces a communication-based cooperative control method for CAVs in multi-lane highways in a mixed traffic setting. The method allows for proactive control application and exhibits good shock wave dissipation performance even with low CAV penetration levels. Results are verified on a three lane circular highway loop using realistic traffic simulation software.

With the increasing attention to the integrated satellite-terrestrial networks (ISTNs), the satellite gateway placement problem becomes of paramount importance. The resulting network performance may vary depending on the different design strategies. In this paper a joint satellite gateway placement and routing strategy for the terrestrial network is proposed to minimize the overall cost of gateway deployment and traffic routing, while adhering to the average delay requirement for traffic demands. Although traffic routing and gateway placement can be solved independently, the dependence between the routing decisions for different demands makes it more realistic to solve an aggregated model instead. We develop a mixed integer linear program (MILP) formulation for the problem. We relax the integrality constraints to achieve a linear program (LP) which reduces time-complexity at the expense of a sub-optimal solution. We further propose a variant of the proposed model to balance the load between the selected gateways.

We propose a human-robot interface based on potentials recorded through surface Electroencephalographic sensors, aiming to decode human visual attention into motion in three-dimensional space. Low-frequency components are extracted and processed in real time, and subspace system identification methods are used to derive the optimal, in mean squared sense, linear dynamics generating the position vectors. This results in a human-robot interface that can be used directly in robot teleoperation or as part of a shared-control robotic manipulation scheme, feels natural to the user, and is appropriate for upper extremity amputees, since it requires no limb movement. We validate our methodology by teleoperating a redundant, anthropomorphic robotic arm in real time. The system's performance outruns similar EMG-based systems, and shows low long-term model drift, indicating no need for frequent model re-training.

Lack of network availability or limited access to communication services are among the challenges that public safety officials and first responders could face during disasters. Networking infrastructure can partially (or sometimes fully) breakdown during a catastrophe. At the same time, unusual peaks in traffic load could lead to much higher blocking probability for critical communication. A possible solution for such scenarios is through the use of mobile infrastructures commonly referred to as Cells on Wheels (COW) or Cells on Light Trucks (COLT). These mobile cells can effectively complement the existing undamaged infrastructure or enable a temporary emergency network by themselves. Given the limited capacity of each cell, variable and spatially non-uniform traffic across the disaster area can make a big impact on the network performance. Not only judicious deployment of the cells can help to meet the coverage and capacity demands across the area, but also intelligent relocation strategies can optimally match the network resources to potentially changing traffic demands. Assuming that each cell can autonomously change its location, in this paper, we propose a decentralized relocation algorithm that adapts network coverage in order to increase the supported users traffic.

Public safety organizations increasingly rely on wireless technology for their mission critical communication during disaster response operations. In such situations, a communication network could face much higher traffic demands compared to its normal operation. Given the limited capacity of base stations in the network, such peak traffic scenarios could lead to high blocking probability or equivalently service interruptions during critical communications. At the same time, networking infrastructure can breakdown during a disaster. Proper deployment of mobile cells - Cells on Wheels - can help to enhance the network coverage or accommodate excess traffic in areas with high concentration of users. In addition, an intelligent relocation strategy can be used to efficiently adapt the cell locations to match variations in the spatial distribution of the traffic. In practical scenarios, these mobile base stations may not be able to relocate to all positions within the target field. Such prohibited areas introduce additional constraints on designing an intelligent relocation strategy. In this paper, we propose a decentralized relocation algorithm that enables mobile cells to adapt their positions in response to potentially changing traffic patterns in a field with prohibited areas. Extensive simulations show considerable improvement in supporting spatially variable traffic throughout the target field.

We present a framework for fast target detection in real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target during execution, our goal is to detect the object efficiently. We propose the concept of early recognition, which influences the candidate proposal process to achieve fast and reliable detection performance. To check the target constraints efficiently, we put forward a novel policy which generates a sub-optimal checking order, and we prove that it has bounded time cost compared to the optimal checking sequence, which is not achievable in polynomial time. Experiments on two different scenarios: 1) rigid object and 2) non-rigid body part detection validate our pipeline. To show that our method is widely applicable, we further present a human-robot interaction system based on our non-rigid body part detection.

In this paper, the problem of joint users' uplink transmission power and rate allocation in NOMA wireless networks is studied, under the scenario that each user is simultaneously requesting multiple services. Each user is associated with a two-variable utility function that represents his satisfaction from his allocated resources (i.e., power and rate). In order to appropriately reflect the combined needs of the user for bundling different types of services under the same common umbrella, user's utility function consists of two parts while different percentages are adopted by each user for each part in order to express his need for real and non-real time services. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of S-modular multivariable games is adopted towards determining the Nash equilibrium point of the game. A distributed, iterative and low complexity algorithm for computing game's Nash equilibrium is introduced, while updating user's uplink transmission power and rate at the same step. Detailed numerical results exhibit the ability of the proposed framework to simultaneously satisfy diverse multiple services requested by the same user.

Swipe fingerprint scanners (sensors) can be distinguished based on their scanner pattern-a sufficiently unique, persistent, and unalterable intrinsic characteristic even to scanners of the same technology, manufacturer, and model. We propose a method to extract the scanner pattern from a single image acquired by a widely-used capacitive swipe fingerprint scanner and compare it with a similarly extracted pattern from another image acquired by the same or by another scanner. The method is extremely simple and computationally efficient as it based on moving-average filtering, yet it is very accurate and achieves an equal error rate below 0.1% for 27 swipe fingerprint scanners of exactly the same model. We also show the receiver operating characteristic for different decision thresholds of two modes of the method. The method can enhance the security of a biometric system by detecting an attack on the scanner in which an image containing the fingerprint pattern of the legitimate user and acquired by the authentic fingerprint scanner has been replaced by another image that may still contain the fingerprint pattern of the legitimate user but has been acquired by another, unauthentic fingerprint scanner, i.e., for scanner authentication.

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become increasingly mobile, little is done to consider the constraints of the wireless environment. In this paper, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver and how. We propose a scheduling framework incorporating content deliverability. Results indicate this novel joint optimization approach outperforms existing systems, which separates recommendation and delivery, especially when the wireless network is operating at maximum capacity.

Over the last decade, the success of social networks has significantly reshaped how information is disseminated among human beings in terms of scale, intensity and speed. Much effort has been dedicated to learning the patterns of such dissemination. However, as users become dominantly mobile, little is done to consider the impact of the wireless environment, in particular, how to optimize the system in the presence of capacity shortage, to better disseminate contents. In this paper, we propose a novel method, utilizing: 1) a hybrid system to handle active dissemination requests; and 2) predictions of dissemination dynamics obtained from the social network applications. This method could mitigate the performance degradation for content dissemination caused by wireless delivery delay. Results indicate that utilizing predictions (even coarse ones) of the dissemination process would benefit the process itself. This is due to improved scheduling prioritization at the wireless layer, which in turn improves the accuracy of the predictions with the help of feedback.

Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.

Over the past decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile in content consumption, little is done to consider the optimization regarding the wireless environment. In this paper, we investigate a centralized wireless content delivery system with heterogeneous base stations, aiming to optimize overall user rewards given the capacity constraints of the wireless networks. We propose a scalable two-phase scheduling framework, consisting of: 1) distributed delivery decisions by each base station, and 2) resource consolidation by the system. Results indicate this novel joint optimization approach is both efficient and scalable.

In this paper, we study, analyze, and evaluate a performance-aware cross-layer design approach for wireless multihop networks. Through network utility maximization (NUM) and weighted network graph modeling, a cross-layer algorithm for performing jointly routing, scheduling, and congestion control is introduced. The performance awareness is achieved by both the appropriate definition of the link weights for the corresponding application's requirements and the introduction of a weighted backpressure (BP) routing/scheduling. Contrary to the conventional BP, the proposed algorithm scales the congestion gradients with the appropriately defined per-pair (link, destination) weights. We analytically prove the queue stability achieved by the proposed cross-layer scheme, while its convergence to a close neighborhood of the optimal source rates' values is proven via an ε-subgradient approach. The issue of the weights' assignment based on various quality-of-service (QoS) metrics is also investigated. Through modeling and simulation, we demonstrate the performance improvements that can be achieved by the proposed approach-when compared against existing methodologies in the literature-for two different examples with diverse application requirements, emphasizing respectively on delay and trustworthiness.

Advertising activity on SNS has grown rapidly and is now a billion dollar business. In the SNS advertising model, the SNS serves as the advertising agent, and takes the advantage of network diffusion to attract advertisers and charges for the cascading impressions. The optimal ad allocation task is to choose the ad allocation plan that maximizes the revenue. Each user has different diffusion ability, limited daily impressions and the advertisers have various bidding prices and budget concerns. A feasible plan that obeys the constraints is difficult to find. The solution of this problem lies in the space of N |Ads|× |user|, which makes direct optimization unattractive. In this paper, we study SNS advertising business models, formulate the SNS ad allocation problem and show their connections with hyperbolic embedding. We develop a new embedding algorithm Hyper Cube Map that allows for dimension reduction. Our proposed method reduces the dimensionality of the original problem significantly, runs two to four orders of magnitude faster, and reaches 95% of the optimum.

As social network services (SNS) are gaining popularity, establishing trust relationships among users in SNS is necessary for user interaction and decision making. However, the evaluation of global trust values (i.e. reputation) of users suffers from the curse of opinion divergence in the network. In this paper, we consider the scenario of opinion divergence within social networks, and propose a method to reach different global trust values between groups of users with controversial opinions in the network. We model the global trust opinion formation in discrete-time dynamics and introduce bipartite consensus as the approach to establish global trust in such circumstances. Such approach works upon the property of structural balance, and can be extended to more general situations where eventual positivity applies. Via our approach, non-trivial global trust can be reached within the network, which can guide users' social behaviors, and support many SNS-based applications.

Crowdsourcing has been extensively used for aggregating data from a large pool of workers. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this paper, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraint. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms. Our algorithm outperforms state-of-the-art by up to 30% on real data.

With an increasing number of wireless devices available, there is a tremendous need for designing new efficient protocols, which take into account resource constraints and at the same time provide adequate Quality of Service (QoS) performance guarantees (e.g. throughput, latency etc.). Most wireless protocols currently used perform well under specific environmental conditions or in particular applications. In this work, we propose a novel methodology for designing routing protocols for resource-constrained wireless multi-hop networks by separating the protocol into distinct components, which specify particular functionalities. Different QoS requirements can be guaranteed by configuring the different components without the need to modify or develop the protocol from scratch. An initial study for energy-constrained environments indicated that our approach is effective. In our ongoing work we consider adversarial environments and we develop techniques to mitigate network-layer attacks. Finally we are investigating the design of a decision-theoretic module for dynamic protocol configuration.

Several applications are built around sharing information by leveraging social network connections. For example, in social buying sites like Groupon, a deal is usually forwarded to interested recipients through their social graph. A primary goal is to improve user satisfaction by maximizing the relevance of the shared message to the target audience. In order to suggest more personalized products, one should consider offering not only accurate but also diverse recommendations, since diversification plays an important factor in increasing the users's satisfaction. In this work, we address this problem by proposing a social network hyperbolic embedding that exploits both social connections and user preferences aiming at increasing both the accuracy and the diversity of recommendations.

In heterogeneous cellular networks, the network capacity can be significantly enhanced via user offloading between macro- and pico-tier. Due to the strong cross-tier interference from macrocells to picocells in co-channel deployment, the user offloading needs to be jointly optimized with macrocells' transmit power nulling, i.e., almost blank subframes (ABSs) in 3GPP LTE-A systems. In this paper, we discuss the network-wide utility maximization problem where the cell association and the number of ABSs are jointly optimized. Due to the NP-hardness of the formulated problem, an online heuristic algorithm is proposed where (i) user load is re-distributed based on users' expected data rate by handover for the current number of ABSs, and (ii) the current number of ABSs is changed (increased or decreased) by estimating the possible users to be offloaded based on users' expected data rate by handover and ABS change in a gradient descent manner. Through simulations, we demonstrate that the proposed algorithm not only improves the average data rate of users, but also achieves better fairness among users.

Heterogeneous cellular networks are expected to provide capacity enhancement and user offloading with cell range expansion. Under this circumstance, downlink cross-tier interference from macro base stations to pico mobile stations especially in the expanded range is a challenging problem. To mitigate this problem, the use of almost blank subframes (ABSs) has been proposed in 3GPP LTE-A systems. The issues are how many ABSs are configured, and how pico base stations schedule their pico mobile stations to configured ABSs. In this paper, we study the optimal number of ABSs along with pico mobile station scheduling in ABSs by formulating a utility maximization problem with constraints of minimum required data rate of pico mobile stations in the expanded range. To solve the optimization problem, we first propose an algorithm for each pico base station to schedule pico mobile stations onto ABSs and non-ABSs, in a way that if their local utility is maximized for a given number of ABSs, then the optimal number of ABSs is obtained by maximizing the network-wide utility. Through simulations, we demonstrate that the proposed scheme not only can improve the network-wide utility, can also provide fairness among mobile stations.

The utility of smartphones has been limited to a great extent by their short battery life. In this work, we propose a new approach to prolonging smartphone battery life. We introduce the notions of valueless and valued battery, as being the available battery when the user does or does not have access to a power source, respectively. We propose a cooperative system where users with high battery level help carry the traffic of users with low battery level. Our scheme helps increase the amount of valued battery in the network, thus it reduces the chance of users running out of battery early. Our system can be realized in the form of a proximity service (ProSe) which utilizes a device-to-device (D2D) communication architecture underlaying LTE. We show through simulations that our system reduces the probability of cellular users running out of battery before their target usage time (probability of outage). Our simulator source code is made available to the public.

Cyber-physical systems (CPS) involve communication networks, computation algorithms, control systems and physical systems. Many CPS, such as Smart Buildings, are subject to very expensive deployment costs and complex network interactions. Thus comprehensive modeling and simulation of such systems are crucial to ensure that they function as intended before deployment. Given the multi-domain nature of CPS, it is more appropriate to use a heterogeneous simulation environment to study system dynamics. In this paper, we design and implement an integrated modeling and co-simulation tool chain, called HybridSim, for the design and simulation of CPS. Firstly, HybridSim can transform and import existing system components from multi-domains into SysML, which enables systems engineers to design CPS with only these imported SysML blocks. Secondly, HybridSim can generate Functional Mock-up Units (FMUs) and configuration scripts directly from SysML designs. Finally, HybridSim can co-simulate these FMUs according to the Functional Mock-up Interface standard to synchronize their corresponding simulators and exchange information between them. We demonstrate the convenience and efficiency of HybridSim using a comprehensive hydronic heating system model for Smart Buildings as the case study to investigate the impact of packet loss and sampling rate introduced by the communication network.

In heterogeneous cellular networks, the deployment of low-powered picocells provides user offloading and capacity enhancement. The expansion of a picocell's coverage by adding a positive bias for cell association can maximize these effects. Under this circumstance, downlink cross-tier interference from a macro base station to pico mobile stations in the expanded picocell range deteriorates those pico mobile stations' performance significantly. In this paper, a coordinated scheduling and power control algorithm is proposed, whereby the macro base station reduces its transmission power for those victim pico mobile stations in the expanded picocell range only on a set of resource blocks to minimize performance degradation at the macro base station. First, the transmission power level is calculated based on the mobile stations' channel condition and QoS requirements. Then, a set of resource blocks is determined by solving a binary integer programming to minimize the sum of transmission power reduction subject to victim pico mobile stations' QoS constraints. To reduce computational complexity, we utilize a heuristic algorithm, i.e., max-min greedy method, to solve the problem. Through system level simulations, we show that average and 5%-ile throughputs of victim pico mobile stations are significantly improved.

Achieving high Quality of Service (QoS) over wireless multihop networks calls for enhanced routing/scheduling algorithms. Towards this direction it has been shown in the literature that the Greedy Backpressure algorithm which combines routing based on greedy hyperbolic embedding with backpressure scheduling, achieves to improve delay while remains throughput optimal. However, the performance of such an approach is significantly affected by the selection of the corresponding spanning tree used for greedily embedding the network into the hyperbolic space. Our work aims exactly at addressing this issue, that is the construction of an appropriate spanning tree that improves the cost of the paths used by the Greedy Backpressure approach, when considering a more generic weighted network graph modeling. The latter allows us to take into consideration the link costs in the routing process, which in turn may result in the simultaneous improvement of multiple performance metrics. To address the problem under consideration, we propose a coalition formation game framework among the network nodes, so that they can decide cooperatively for the spanning tree, via trading their value functions designed to depend on the link weights. We prove that the stable outcome of the coalitional game is a spanning tree of the network, and study through simulations the induced improvement in the network performance. Furthermore, we extend the framework for a scenario with multiple costs on each link through multi-tree hyperbolic embedding.

In this paper, we study a distributed opportunistic scheduling problem to exploit the channel fluctuations in wireless ad-hoc networks. In this problem, channel probing is followed by a transmission scheduling procedure that is executed independently within each link in the network. We study this problem for the popular block-fading channel model, where channel dependencies are inevitable between different time instances during the channel probing phase. Different from existing works, we explicitly consider this type of channel dependencies and its impact on the transmission scheduling and hence the system performance. We use optimal stopping theory to formulate this problem, but at carefully chosen time instances at which effective decisions are made. The problem can then be solved by a new stopping rule problem where the observations are independent between different time instances. Since the stopping rule problem has an implicit horizon determined by the network size, we first characterize the system performance using backward induction. We develop one recursive approach to solve the problem and show that the computational complexity is linear with respect to the network size. Due to its computational complexity, we present an approximation for performance analysis and develop a metric to check how good the approximation is. We characterize the achievable system performance if we ignore the finite horizon constraint and apply the stopping rules based on the infinite horizon analysis nevertheless. We present an improved protocol to reduce the probing costs which requires no additional cost. We characterize the performance improvement and the energy savings in terms of the probing signals. We show numerical results based on our mathematical analysis with various settings of parameters.

In this article, we provide convergence results for an Ant-based Routing Algorithm (ARA) for wireline, packet-switched communication networks, that are acyclic. Such algorithms are inspired by the foraging behavior of ants in nature. We consider an ARA algorithm proposed earlier by Bean and Costa [2005]. The algorithm has the virtues of being adaptive and distributed, and can provide a multipath routing solution. We consider a scenario where there are multiple incoming data traffic streams that are to be routed to their respective destinations via the network. Ant packets, which are nothing but probe packets, are introduced to estimate the path delays in the network. The node routing tables, which consist of routing probabilities for the outgoing links, are updated based on these delay estimates. In contrast to the available analytical studies in the literature, the link delays in our model are stochastic, time-varying, and dependent on the link traffic. The evolution of the delay estimates and the routing probabilities are described by a set of stochastic iterative equations. In doing so, we take into account the distributed and asynchronous nature of the algorithm operation. Using methods from the theory of stochastic approximations, we show that the evolution of the delay estimates can be closely tracked by a deterministic ODE (Ordinary Differential Equation) system, when the step size of the delay estimation scheme is small. We study the equilibrium behavior of the ODE system in order to obtain the equilibrium behavior of the routing algorithm. We also explore properties of the equilibrium routing probabilities, and provide illustrative simulation results.

Alzheimer's disease (AD) is currently the most common form of dementia affecting the elderly, and its occurrence rate is only expected to increase over the next several decades. Though there is a vast array of knowledge about individual molecules and genetics involved with the disease, there is no clear understanding of the mechanism of pathogenesis. To help better understand the disease process, a graph theoretic model was developed that studies both the concentration of molecules thought to be involved in pathogenesis (Aβ, interleukin-1, tumor necrosis factor alpha, cholesterol, ATP levels), as well as the cell number in a small location in the brain. Particular emphasis was put on the role of the inflammatory process in AD progression. This represents one of the first models that uses graph theory combined with a systems biology approach to study AD.

The topic of opportunistic scheduling for wireless ad-hoc networks has been studied for single-channel networks in several recent works. Since now many wireless systems provide multiple channels for data transmission, this problem is of practical interest for networks with multiple channels. In this paper, we study the problem of opportunistic scheduling for one type of ad-hoc networks where the wireless spectrum can be divided into multiple independent sub-channels for better efficiency. We start with a naive multi-channel protocol where the scheduling scheme is working independently from sub-channel to sub-channel. We show that the naive protocol can only marginally improve the system throughput. We then develop a protocol to jointly consider the opportunistic scheduling behavior across multiple sub-channels. We characterize the optimal stopping rule and present several bounds for the system throughput of the multi-channel protocol. We show that by joint optimization of the scheduling scheme across multiple sub-channels, the proposed protocol improves the system throughput considerably in contrast to that of single-channel systems.

The potential applications and pervasive nature of mobile ad-hoc networks (MANETs) has made them an attractive target for attackers. The wireless medium of communication coupled with constrained resources enable attacks which can be executed by a weak adversary. A wormhole is one such attack which poses considerable threat, particularly to routing protocols. In this paper, we devise a novel scheme for detecting a wormhole by utilizing the inherent symmetry of electromagnetic wave propagation in the wireless medium. We demonstrate the loss of this symmetry in case of a wormhole attack and propose a method to detect and flag the adversary. We modify the insecure neighborhood discovery to incorporate authentication. We further extend this scheme to a trust system with low overhead.

Future communications consist of an increasing number of wireless parts, while simultaneously need to support the widespread multimedia applications imposed by social networks. These human-machine systems, driven by both real time social interactions and the challenges of the wireless networks' design, call for efficient and easy to implement, distributed cross-layer algorithms for their operation. Performance metrics such as throughput, delay, trust, energy consumption, need to be improved and optimized aiming at high quality communications. We investigate the coveted throughput-delay trade-off in static wireless multihop networks based on a "computer-aided" design of the backpressure scheduling/routing algorithm for networks embedded in hyperbolic space. Both routing and scheduling exploit the hyperbolic distances to orient the packets to the destination and prioritize the transmissions correspondingly. The proposed design provides us with the freedom of controlling its theoretical throughput optimality and of counterbalancing its practical performance through simulations, leading to significant improvements of the throughput-delay trade-off.

Mobile ad-hoc networks (MANETs) are a key enabler of pervasive computing. Constrained resources in mobile stations make it critical for nodes to be able to cooperate to enhance communication and computation capabilities. However, the wireless and dynamic nature of the links presents easy attack vectors for adversaries. The ability to securely discover and identify neighboring nodes (secure ND) is a fundamental building block for such networks. Even a relatively weak adversarial relay has the capability of distorting the network view and diverting significant amount of traffic. This can cause significant performance degradation. In this paper, we utilize the physical layer authentication scheme introduced by Yu, Baras and Sadler [1] to secure neighborhood discovery against adversarial relays. The proposed method incurs little performance overhead and requires no additional hardware. We provide analytical and simulation based performance evaluation of the security of our scheme.

System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

In this paper, we study the problem of medium access and distributed opportunistic scheduling to exploit channel fluctuations for wireless ad-hoc networks. This work focuses on the Constant Access Time (CAT) problem, where the total time duration of channel probing and data transmission is slotted into fixed block length. In particular, we consider the dependence between channel rates at different time instances during the channel probing phase and its impact on the overall system throughput. We first analyze the system performance of the CAT problem under independent channel rate assumption and compare our result to the existing work on the Constant Data Time (CDT) problem. We then propose a protocol to reduce the channel probing costs based on this analysis. We show analytical results of the proposed protocol for systems with a sufficiently large number of users. We compare the system performance of the proposed protocol with that of the original protocol under independent channel rate assumption. We mathematically prove that the proposed protocol improves the system performance.

Fingerprint scanners have unique patterns that can be used to distinguish one scanner from another one. The pattern, which we call scanner pattern, stems from the variability of device characteristics at silicon level and is caused by imperfections of the conversion from the input to the scanner (i.e., the object applied to it) to its output (i.e., the digital image). The scanner pattern is a sufficiently unique and persistent intrinsic characteristic of the fingerprint scanners even to those of the same technology, manufacturer, and model. We propose a simple and extremely accurate algorithm that is able to distinguish the pattern of one scanner from the pattern of another scanner of exactly the same model by extracting the pattern from a single image, acquired with each scanner. In this way, the scanner pattern can be used to enhance the security of a bio metric system by authenticating the scanner, used to acquire a particular fingerprint image, and thus detect attacks on the scanner. Combining the biometric authentication with a scanner authentication leads to a two-part authentication, which we call bipartite authentication, that verifies both the identity of the user and the "identity" of the fingerprint scanner.

Existing works on the performance analysis of IEEE 802.15.4 networks with peer-to-peer (P2P) topologies assume the non-beacon unslotted mode in the MAC layer, which is not suitable for time-critical communications required by many applications, such as control, actuation and monitoring applications. In this paper, we introduce an enhanced guaranteed time slot (GTS) mechanism which can support time-critical P2P communications in wireless sensor networks (WSNs). An efficient Markov chain that takes into account retransmission limits, acknowledgements, unsaturated traffic and packet delivery ratios of links is proposed to model P2P communications using this enhanced GTS mechanism. Based on this model, we analyze the expected reliability and energy consumptions under various traffic conditions. In addition, the impact of MAC parameters on these performance indexes is analyzed. Monte Carlo simulations show that our theoretical analysis is quite accurate, and thus can be used as guidance for networks configuration in WSNs.

Ad hoc networks rely on the mutual cooperation among individual nodes to achieve network-wide objectives. However, individual nodes may behave selfishly in order to maximize their own benefits without considering the global benefits of the network. One approach to incentivize nodes cooperation for better global benefits is to establish trust relations among nodes to guide their decision making. In this paper, we present a game theoretic analysis for the efficiency of establishing trust for improving node cooperation. The trust relations among nodes are modeled as a trust-weighted network, and we study a graphical game in this network where the nodes' payoffs are affected by their trust relations. We characterize the Nash equilibrium and the social optimum of this game and show that the game efficiency has a close relationship to the Bonacich centralities of nodes in the trust-weighted network. Furthermore, we propose to improve game efficiency by introducing heterogeneous resources to nodes according to their centralities. We provide both experimental and theoretical analysis on the improvement of the game efficiency.

The Internet offers tremendous opportunities for information sharing and content distribution. However, without proper filtering, the large amount of information may likely swarm the users rather than benefit them. Collaborative filtering is a technique for extracting useful information from the large information pool generated by interconnected online communities. In this paper, we develop a probabilistic collaborative filtering algorithm, which is based on ordered logistic regression and takes into account both similarities among the users and similarities among the items. We make inference with maximum likelihood and Bayesian frameworks, and propose a Markov Chain Monte Carlo based Expectation Maximization algorithm to optimize model parameters. The power of our proposed algorithm is its extensibility. We show that it can incorporate content and contextual information. More importantly, it can be easily extended to include the trustworthiness of users, thus being more robust to malicious data manipulation. The experimental results on a real world data set show that our proposed algorithm with the trust extension is robust under different types of attacks in recommendation systems.

Fast anomaly detection and localization is critical to ensure effective functioning of wireless sensor networks. The low bandwidth and power constraints in wireless sensor networks are the main challenges for achieving this task, especially for large scale networks. In this paper, we propose a trust-assisted framework for detecting and localizing network anomalies in a hierarchical sensor network. The proposed method makes inference based on end-to-end measurements collected by a set of measurement nodes. Network heterogeneity is exploited for better bandwidth and energy efficiency. The trustworthiness of network links is utilized to design an efficient two-phase probing strategy that can achieve a flexible tradeoff between inference accuracy and probing overhead. We performed experiments with different network settings and demonstrated the effectiveness of our proposed algorithms.

Distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by distributed state estimation, where nodes only exchange information locally to achieve enhanced scalability and adaptivity to network dynamics. One important property of state estimation is robustness against false data injection from sensors compromised by attackers. Different from most existing works in the literature that focus on centralized state estimation, we propose two novel robust distributed state estimation algorithms against false data injection. They are built upon an existing distributed Kalman filtering algorithm. In the first algorithm, we use variational Bayesian learning to estimate attack parameters and achieve performance similar to a centralized majority voting rule, without causing extra communication overhead. In the second algorithm, we introduce heterogeneity into the network by utilizing a subset of pre-trusted nodes to achieve performance better than majority voting. We show that as long as there is a path connecting each node to some of the pre-trusted nodes, the attackers can not subvert the network. Experimental results demonstrate the effectiveness of our proposed schemes.

In this paper, we provide convergence results for an Ant Routing (ARA) Algorithm for wireline, packet switched communication networks, that are acyclic. Such algorithms are inspired by the foraging behavior of ants in nature. We consider an ARA algorithm proposed by Bean and Costa [2]. The algorithm has the virtues of being adaptive and distributed, and can provide a multipath routing solution. We consider a scenario where there are multiple incoming data traffic streams that are to be routed to their destinations via the network. Ant packets, which are nothing but probe packets, are used to estimate the path delays in the network. The node routing tables, which consist of routing probabilities for the outgoing links, are updated based on these delay estimates. In contrast to the available analytical studies in the literature, the link delays in our model are stochastic, time-varying, and dependent on the link traffic. The evolution of the delay estimates and the routing probabilities are described by a set of stochastic iterative equations. In doing so, we take into account the distributed and asynchronous nature of the algorithm operation. Using methods from the theory of stochastic approximations, we show that the evolution of the delay estimates can be closely tracked by a deterministic ODE (Ordinary Differential Equation) system, when the step-size of the delay estimation scheme is small. We study the equilibrium behavior of the ODE in order to obtain the equilibrium behavior of the algorithm. We also provide illustrative simulation results.

We propose a component based methodology for modelling and design of wireless routing protocols. Componentization is a standard methodology for analysis and synthesis of complex systems, or software. The feasibility of the component based design relies heavily on the compositionality property (i.e. system-level properties can be computed from properties of components). To provide a component based design methodology and to test compositionality for routing protocols, we have to develop a component based model of the wireless network. We present the main components of the routing protocol that should be modelled and focus on three main components: neighborhood discovery, selector of topology information to disseminate, and the path selection components. For each component, we identify the inputs, outputs, and a generic methodology for modelling. Throughout the paper, we use the Optimized Link State Routing (OLSR) protocol as a case study to demonstrate the effectiveness of our approach. Using the neighborhood discovery component, we present our design methodology and design a modified enhanced version of this component, and compare its performance to the original OLSR design.

In this work, we address the performance problems that arise when unicast security protocols IPSEC and SSL are applied for securing the end-to-end communication in hybrid satellite networks. Satellite networks use TCP and HTTP performance-enhancing proxy servers to overcome the adverse effect of the large delay-bandwidth product of the satellite channel. However, the proxy servers cannot function when IPSEC and SSL are used for secure unicast communication in hybrid satellite networks. We therefore propose the use of the layered IPSEC (LES) protocol as an alternative to IPSEC for network-layer security. We describe a modification to the Internet key exchange protocol if dynamic key establishment is needed for layered IPSEC. For application-level security of Web browsing with acceptable end-to-end delay, we propose the dual-mode SSL protocol (DSSL) to be used instead of SSL. We describe how LES and DSSL protocols achieve the desired end-to-end communication security while allowing the TCP and HTTP proxy servers to function correctly. Through simulation studies, we quantify the improvement in performance that is achieved using our proposed protocols, compared to traditional IPSEC and SSL.

We develop and evaluate a new method for estimating and optimizing various performance metrics for multi- hop wireless networks, including MANETs. We introduce an approximate (throughput) loss model that couples the physical, MAC and routing layers effects. The model provides quantitative statistical relations between the loss parameters that are used to characterize multiuser interference and physical path conditions on the one hand and the traffic rates between origin-destination pairs on the other. The model takes into account effects of the hidden nodes, scheduling algorithms, IEEE 802.11 MAC and PHY layer transmission failures and finite packet transmission retries at the MAC layer in arbitrary network topologies where multiple paths share nodes. We apply automatic differentiation (AD) to these implicit performance models, and develop a methodology for sensitivity analysis, parameter optimization and trade-off analysis for key wireless protocols. Finally, we provide simulation experiments to evaluate the effectiveness and performance estimation accuracy of the proposed models and methodologies.

In this paper, we describe an efficient authentication protocol for group communication in hybrid wireless networks with a satellite overlay. The proposed protocol uses a new class of lightweight, symmetric-key certificates called TESLA certificate. The certificates bind the identities of the senders to the anchor elements of their key chains; messages from the senders are authenticated by MACs computed with keys from the chain. The satellite is used as the certificate authority to generate the certificates. The satellite also acts as the proxy for the senders in disclosing the MAC keys to the receivers in the network. Due to the use of symmetric MAC functions, the proposed protocol is much less expensive in terms of node processing power and energy compared to digital signatures. The use of the satellite as the CA and the proxy allows strong security mechanisms and fast message verification. Through analysis, we show that the protocol is secure against malicious adversaries. We also estimate of the performance of the protocol in comparison to public key-based digital signatures.

Autonomic networks are networks that are self- organized with decentralized control and management. Accurate trust establishment and maintenance is essential for secure and reliable message transmissions in autonomic networks. Because of the mobility and dynamics, trust management in autonomic networks is a much more dynamic problem than in traditional server-based networks. In traditional networks, centralized trusted servers provide necessary trust credentials for trust establishment. However, such servers are not available in autonomic networks. Trust credentials are distributed among users in the network. In this paper, we develop and analyze distributed schemes for efficiently and securely distributing trust credentials, so that users are able to establish reliable trust relations with their neighbors. Our approach is inspired from network coding, where trust documents are combined during distribution.

In this paper, we address the problem of establishing full connectivity and satisfying required traffic capacity between disconnected clusters in large wireless ad-hoc ground networks by placing a minimum number of advantaged high flying aerial platforms (APs) as relay nodes at appropriate places. We formulate the problem of providing both connectivity and required capacity between disconnected ground clusters as a constrained clustering problem with complexity costs. The basic requirement for connectivity between the ground clusters and APs is converted into a summation form distortion function. The additional requirement for connectivity between the various APs is encoded by adding a new (summation form) constraint to the distortion function. In order to satisfy the required capacity out of each cluster to all other clusters, we add a cost function that depends on the assignment probabilities of the APs and relate the source (prior) probabilities of each cluster to the required capacity out of this cluster. The cost function produces solutions which are load balanced, i.e., the capacities supported through each AP are nearly equal. We solve the resultant clustering problem using deterministic annealing in order to find (near) globally optimal solutions for the minimum number and locations of the APs to establish full connectivity and provide required traffic capacity between disconnected clusters. We establish the validity of our algorithm by comparing it with optimal exhaustive search algorithms and show that our algorithm is near-optimal for the problem of establishing connectivity and satisfying capacity requirements between disconnected clusters.

In this paper, we investigate the design of optimal state estimators for Markovian jump linear systems. We consider that the state has two components: the first component is finite valued and is denoted as mode, while the second (continuous) component is in a finite dimensional Euclidean space. The continuous state is driven by a zero mean, white and Gaussian process noise. The observation output has two components: the first is the mode and the second is a linear combination of the continuous state observed and zero mean, white Gaussian noise. Both output components are affected by delays, not necessarily equal. Our paradigm is to design optimal estimators for the current state, given the current output observation. We provide a solution to this paradigm by giving a recursive estimator for the continuous state, in the minimum mean square sense, and a finitely parameterized recursive scheme for computing the probability mass function of the current mode conditioned on the observed output. We show that when the mode is observed with a greater delay then the continuous output component, the optimal estimator nonlinear in the observed outputs.

We describe new methods for multi-hop mobile wireless network design, so as to meet performance specifications. We introduce an implicitly defined approximate loss model that couples the physical, MAC and routing layers' effects. The model provides quantitative statistical relations between the loss parameters that are used to characterize multiuser interference and physical path conditions on the one hand and the traffic rates and types between origin-destination pairs on the other. The model considers effects of hidden nodes, node scheduling algorithms, MAC and PHY layer failures and unsuccessful packet transmission attempts at the MAC layer in arbitrary network topologies where multiple paths share nodes. We consider both contention-based as well as scheduling-based MAC protocols. The new methods are based on implicit loss network models developed using fixed point methods. We describe the application of Automatic Differentiation (AD) to these implicit performance models, and develop a methodology for sensitivity analysis and parameter optimization for wireless protocols. We also introduce new methods that utilize on-line feedback from the PHY layer to simplify the joint MAC and routing protocol design. We provide several examples, with realistic scenarios, that demonstrate the benefits of applying these new methods in an integrated manner. We close with current and future work that extends these methods and algorithms towards a Component-Based design, performance analysis and validation of distributed protocols for mobile wireless networks. We argue that this set of ideas provides a very promising approach to 'clean slate' design for such networks and the resulting hybrid broadband Internet.

Temporal-Angular channel sounding measurements of an indoor millimeter wave channel (60 GHz) is analyzed to determine whether ray arrivals at the receiver form clusters in the two-dimensional time-angle space. The channel behavior and cluster energy distribution for directional antennas (with various 3 dB beamwidths) at the transmitter are discussed. Geometric interpretation of the results is also provided.

Authentication is the process where claims of identity are verified. Though authentication mechanisms typically exist above the physical layer, physical layer methods have recently been introduced that do not require extra bandwidth. In this paper we propose a multi-carrier extension to the work and consider the stealth and robustness tradeoffs. We conclude by discussing the power-reliability tradeoff and the applicability to cross-layer security.

If a network is to operate successfully, its users need to collaborate. Collaboration takes the form of following a network protocol and involves some resource expenditure on the part of the user. Therefore, users cannot automatically be expected to follow the protocol if they are not forced to. The situation is exacerbated by the presence of malicious users whose objective is to damage the network and increase the cost incurred by the legitimate users. The legitimate users are, at least initially, unaware of the type (legitimate or malicious) of the other users. Our contribution is a model for the strategic interaction of legitimate and malicious users as described above. The model is based on repeated graphical games with incomplete information. We describe and analyze two specific instantiations, aiming to demonstrate the modeliquests expressive power and tractability. The main benefit we see from using game theory for this essentially security problem is the ability to bound the damage caused by the malicious users.

Authentication is the process where claims of identity are verified. Most mechanisms of authentication (e.g., digital signatures and certificates) exist above the physical layer, though some (e.g., spread-spectrum communications) exist at the physical layer often with an additional cost in bandwidth. This paper introduces a general analysis and design framework for authentication at the physical layer where the authentication information is transmitted concurrently with the data. By superimposing a carefully designed secret modulation on the waveforms, authentication is added to the signal without requiring additional bandwidth, as do spread-spectrum methods. The authentication is designed to be stealthy to the uninformed user, robust to interference, and secure for identity verification. The tradeoffs between these three goals are identified and analyzed in block fading channels. The use of the authentication for channel estimation is also considered, and an improved bit-error rate is demonstrated for time-varying channels. Finally, simulation results are given that demonstrate the potential application of this authentication technique.

The widespread deployment of wireless networks and hot spots that employ the IEEE 802.11 technology has forced network designers to put emphasis on the importance of ensuring efficient and fair use of network resources. In this work we propose a novel framework for detection of intelligent adaptive adversaries in the IEEE 802.11 MAC by addressing the problem of detection of the worst-case scenario attacks. Utilizing the nature of this protocol we employ sequential detection methods for detecting greedy behavior and illustrate their performance for detection of least favorable attacks. By using robust statistics in our problem formulation, we attempt to utilize the precision given by parametric tests, while avoiding the specification of the adversarial distribution. This approach establishes the lowest performance bound of a given Intrusion Detection System (IDS) in terms of detection delay and is applicable in online detection systems where users who pay for their services want to obtain the information about the best and the worst case scenarios and performance bounds of the system. This framework is meaningful for studying misbehavior due to the fact that it does not focus on specific adversarial strategies and therefore is applicable to a wide class of adversarial strategies.

This paper describes the analytical derivation of a new watermarking algorithm satisfying optimality properties when the distortion of the watermarked signal is caused by a Gaussian process. We also extend previous work under the same assumptions and obtain more general solutions.

This paper revisits the problem of detecting greedy behavior in the IEEE 802.11 MAC protocol by evaluating the performance of two previously proposed schemes: DOMINO and the sequential probability ratio test (SPRT). The evaluation is carried out in four steps. We first derive a new analytical formulation of the SPRT that takes into account the discrete nature of the problem. Then we develop a new tractable analytical model for DOMINO. As a third step, we evaluate the theoretical performance of SPRT and DOMINO with newly introduced metrics that take into account the repeated nature of the tests. This theoretical comparison provides two major insights into the problem: it confirms the optimality of SPRT and motivates us to define yet another test, a nonparametric CUSUM statistic that shares the same intuition as DOMINO but gives better performance. We finalize the paper with experimental results, confirming our theoretical analysis and validating the introduction of the new nonparametric CUSUM statistic.

Unstructured networks (like ad-hoc or peer-to-peer networks) are networks without centralized control of their operation. Users make local decisions regarding whether to follow the network protocol or not. While providing scalability benefits, this degrades the performance, which is compounded by the potential presence of Malicious Users. In general, these users are trying to disrupt the operation of the network, and prevent the legitimate users from achieving their objectives. More specifically, they could try to break the connectivity of the network, or waste the resources of the legitimate users. In this work we use game theory to examine the effect of malicious users. All users are modeled as payoff-maximizing strategic agents. A simple model, fictitious play, is used for the legitimate user behavior, but no limits are imposed on the Malicious Users strategies. We look for the worst case equilibrium: the one that gives Malicious Users the highest payoff. We identify the importance of the network topology.

Temporal-Angular channel sounding measurements of an indoor millimeter wave channel (60 GHz) is analyzed to determine the location of two dimensional clusters of arrivals at the receiver. The measurement scenarios are also emulated by a ray tracing tool. The results are similarly analyzed to verify possible agreements and determine the effectiveness of such tools in predicting cluster locations as well as ray arrival statistics within clusters in millimeter wave indoor channel.

Selfish behavior at the Medium Access (MAC) Layer can have devastating side effects on the performance of wireless networks, with effects similar to those of Denial of Service (DoS) attacks. In this paper we consider the problem of detection and prevention of node misbehavior at the MAC layer, focu sing on the back-off manipulation by selfish nodes. We first propose an algorithm that ensures honest behavior of non-colluding participants. Furthermore, we analyze the problem of colluding selfish nodes, casting the problem within a minimax robust detection framework and providing an optimal detection rule for the worst-case attack scenarios. Finally, we evaluate the performance of single and colluding attackers in terms of detection delay. Although our approach is general and can be used with any probabilistic distributed MAC protocol, we focus our analysis on the IEEE 802.11 MAC.

Ad hoc network users are resource constrained: Before transmitting data, they have to take into account the energy expenditure involved. Even if a user is, in principle, willing to spend energy to improve network connectivity, his actual decision will be heavily influenced by the decisions of his neighboring users, since they act as relay nodes for him. Moreover, some of the neighbors may not be as benign as he is; in fact, they could be outright malicious. We are abstracting the tradeoff between spending energy and increasing connectivity by modeling the interaction of a user with his one-hop neighbors in a game theoretic fashion. Two types of users exist: Good users willingly trade energy for connectivity, but only if they expect their neighbors to do the same; Bad users try to destroy connectivity, but also lure the Good users to waste energy. Within our model for user behavior and sophistication, we explore outcomes that can arise in this graphical game.

We address issues related to efficiency, fairness, end- to-end delay minimization and Quality-of-Service in order to enable a flexible access and dynamic mission operation capability in the next generation NASA space-to-ground communication infrastructure. To provide efficient and fair utilization while guaranteeing specific service requirements for different traffic classes, we propose a two-level (long-term and short-term) sharing of a slotted high data rate satellite communication link. The implemented long-term optimal bandwidth allocation provides per-user/per-flow service guarantee and gives the inputs to the next level. In our short-term bandwidth allocation with threshold regulation, a small portion of bandwidth is still assigned to all active spacecraft in advance, but most bandwidth is dynamically allocated per frame by solving an optimal timeslot scheduling problem. We finally discuss simulation results and our design optimization approach.

Securing multicast communications in Mobile Ad Hoc Networks (MANET) is considered among the most challenging research directions in the areas of wireless networking and security. MANET are emerging as the desired environment for an increasing number of commercial and military applications, addressing also a growing number of users. Security on the other hand, is now an indispensable requirement for these applications. However, the limitations of the dynamic, infrastructure-less nature of MANET impose major difficulties in establishing a secure framework suitable for such services. The design of efficient key management (KM) schemes for MANET is of paramount importance, since the performance of the KM functions imposes an upper limit on the efficiency and scalability of the whole secure group communication system. In this work, we contribute towards this direction by extending TGDH to a novel distributed scheme: DS-TGDH. Our aim is to modify TGDH to: a) be feasible in the most general resource-constrained MANET where no nodes with special capabilities exist, b) produce considerably lower overhead for the network nodes involved, c) handle disruptions with low cost. We consider the underlying routing protocol in our design, and we apply a distributed TGDH version over a robust schedule, optimizing parameters of interest. We focus on the design and analysis of the "stealthy" TGDH and compare it with the original.

Abstract — With the explosive growth of network techniques, in particular wireless communications, the traditional centralized, fixed networks can no longer satisfy the enormous demands on network connectivity, data storage and information exchanges. New types of networks, such as pervasive computing networks, mobile ad hoc networks and P2P networks, emerged in recent years in order to provide solutions for the increasing requirements on networked services. All those networks are autonomous networks, because they are distributed and self-organized. As a case study, we employ a specific application – distributed trust management – to understand and analyze the behavior and properties of these “anarchical ” autonomous networks. We propose a statistical trust evaluation rule, prove its convergence and investigate its characteristics when the system is at the steady state. Our investigation results in several conclusions for the design of trust evaluation rules, some of which are unexpected if we do not have the stationary distribution at hand. Our study shows the importance and necessity of applying theoretical analyses to understand the complex characteristics of distributed, self-organized, autonomous networks. I.

Securing multicast communications in ad hoc networks has become one of the most challenging research directions in the areas of wireless networking and security. This is especially true as ad hoc networks are emerging as the desired environment for an increasing number of civilian, commercial and military applications, also addressing an increasingly large number of users. In this paper we study a very basic security question for Ad Hoc Networks: Key Agreement against passive adversaries. Despite being a widely studied area in wired networks, the problem becomes significantly more challenging for ad hoc networks, and even more for sensor networks, due to lack of trusted entities, infrastructures, full connectivity, routing structures, and due to severe limitations on the resources and capabilities of network nodes. In this paper we perform a comprehensive investigation of Key Agreement over resource constrained ad hoc networks. First, we formally model the key agreement problem over multi-hop ad hop networks, and we directly extend known key agreement protocols for wired networks, and evaluate the efficiency of such approaches. We then go beyond natural extensions of such protocols, by proposing non-trivial extensions based on efficient topology-driven simulations of logical networks over an arbitrary physical network, in order to optimize the most significant metrics of interest for such networks: i.e. bandwidth, latency, processing cost. Indeed, the resulting protocols are significantly more efficient in some or all of the above metrics, as our analytical results indicate.

While BGP routing datasets, consisting of raw routing data, are freely available and easy to obtain, extracting any useful information is tedious. Currently, researcher and network operators implement their own custom data processing tools and scripts. A single tool that provides easy access to the information within large raw BGP datasets could be used by both communities to avoid re-writing these tools each time. Moreover, providing not just raw BGP messages, but some commonly used summary statistics as well can help guide deeper custom analyses. Based on these observations this paper describes the first steps towards building a scalable tool. We describe the various techniques and algorithms we have used to build an efficient generic tool called BGP-Inspect. When dealing with large datasets, dataset size, lookup speed, and data processing time are the most challenging issues. We describe our implementations of chunked compressed files and B+ tree indices that attempt to address these issues. We then provide an evaluation of our implementations. Finally, we provide some example scenarios and case studies where BGP-Inspect can provide useful insight into the management and operation of complex BGP based networks. An efficient and flexible back-end custom BGP message database, coupled with an intuitive and easy to use Web-based query front-end makes BGP-Inspect a unique and powerful tool

Classification accuracy in intrusion detection systems (IDSs) deals with such fundamental problems as how to compare two or more IDSs, how to evaluate the performance of an IDS, and how to determine the best configuration of the IDS. In an effort to analyze and solve these related problems, evaluation metrics such as the Bayesian detection rate, the expected cost, the sensitivity and the intrusion detection capability have been introduced. In this paper, we study the advantages and disadvantages of each of these performance metrics and analyze them in a unified framework. Additionally, we introduce the intrusion detection operating characteristic (IDOC) curves as a new IDS performance tradeoff which combines in an intuitive way the variables that are more relevant to the intrusion detection evaluation problem. We also introduce a formal framework for reasoning about the performance of an IDS and the proposed metrics against adaptive adversaries. We provide simulations and experimental results to illustrate the benefits of the proposed framework

Within the realm of network security, we interpret the concept of trust as a relation among entities that participate in various protocols. Trust relations are based on evidence created by the previous interactions of entities within a protocol. In this work, we are focusing on the evaluation of trust evidence in ad hoc networks. Because of the dynamic nature of ad hoc networks, trust evidence may be uncertain and incomplete. Also, no preestablished infrastructure can be assumed. The evaluation process is modeled as a path problem on a directed graph, where nodes represent entities, and edges represent trust relations. We give intuitive requirements and discuss design issues for any trust evaluation algorithm. Using the theory of semirings, we show how two nodes can establish an indirect trust relation without previous direct interaction. We show that our semiring framework is flexible enough to express other trust models, most notably PGP's Web of Trust. Our scheme is shown to be robust in the presence of attackers.

In this paper, we present a feedback implosion suppression (FIS) algorithm that reduces the volume of feedback information transmitted through the network without relying on any collaboration between users, or on any infrastructure other than the satellite network. Next generation satellite systems that utilize the Ka frequency band are likely to rely on various fade mitigation techniques, in order to guarantee a service quality that is comparable to other broadband technologies. User feedback would be a valuable input for a number of such components, however, collecting periodic feedback from a large number of users would result in the well-known feedback implosion problem. Feedback implosion is identified as a major problem when a large number of users try to transmit their feedback messages through the network, holding up a significant portion of the uplink resources and clogging the shared uplink medium. In this paper, we look at a system where uplink channel access is organized in time-slots. The goal of the FIS algorithm is to reduce the number of uplink time-slots hold up for the purpose of feedback transmission. Our analysis show that the FIS algorithm effectively suppresses the feedback messages of 95% of all active users, but still achieves acceptable performance results when the ratio of available time-slots to number of users is equal to or higher than 5%

Buffer overflows constitute by far the most frequently encountered class of attacks against computer systems. In this paper we introduce a tool, termed bufSTAT that achieves a low probability of false alarm and issues early attack warnings. BufSTAT relies on Finite State Machines (FSM) for attack modeling and can detect every stage of an ongoing attack and can thus prevent its execution by issuing early warning in a progressive manner. It can also detect sophisticated multi-stage attacks that are executed over long periods of time. A significant attribute of our approach is that it is amenable to detecting unknown attacks as well after appropriate modification of bufSTAT.

Effective bandwidth represents the resource demand for network traffic to achieve its QoS goal. However, so far, the estimation of the effective bandwidth for modern Internet traffic has been difficult and inaccurate. We present a novel scheme using measurement and simulation techniques to estimate the effective bandwidth. The advantages of the scheme lie in its accuracy, its applicability to various traffic environments, and its efficiency. We describe the simulation procedure, the efficient search algorithm to lock the effective bandwidth quickly, the sample size determination algorithm, and the performance judgment policy in the simulation to guarantee the estimation accuracy. We also examine the issue of estimating the effective bandwidth of aggregate traffic from sub-aggregates, and provide an empirical formula to achieve very good accuracy.

We focus on the design of key management (KM) schemes tailored for the environment of Mobile Ad Hoc Networks (MANETs). A MANET is a collection of wireless mobile nodes, communicating among themselves over possibly multi-hop paths, without the help of any infrastructure such as base stations or access points. The fact that no central authorization entity is assumed at all times for all nodes makes the task of network operations more difficult and indicates the need for distributed algorithms to provide the functions of centralized entities. KM ensures communication security among nodes and the capability of their cooperation as a secure group. It consists of key generation, user authentication and key distribution services. In this work we address key distribution, group key generation, entity authentication: we emphasize that entity authentication should be designed with key distribution algorithms in mind and vice versa, to achieve efficient and scalable KM schemes for MANETs. We present an entity authentication scheme based on the Merkle Tree algorithm, applied on a key generation protocol recently developed - MOT- to produce an efficient, scalable and secure KM scheme.

We evaluate Web browsing performance over a multiple access satellite channel for three different MAC layer protocols. A Web user behavior model is used to generate realistic source traffic. A new transport layer protocol called RWBP is proposed to solve the TCP problems inside satellite networks. RWBP uses per-flow queuing, round robin scheduling and receiver window backpressure for congestion control. We compare its performance with TCP over the three multiple access protocols. Our simulation results show that RWBP can reduce the multiple access channel load and at the same time improve the Web page response time when driven by realistic Web traffic. For the MAC layer protocols, combined free demand assignment multiple access (CFDAMA) always performs better than the other two protocols.

In this paper we describe the design and implementation of ATEMU, a fine grained sensor network simulator. ATEMU is intended to bridge the gap between actual sensor network deployments and sensor network simulations. We adopt a hybrid strategy, where the operation of individual sensor nodes is emulated in an instruction by instruction manner, and their interactions with each other via wireless transmissions are simulated in a realistic manner. A unique feature of ATEMU is its ability to simulate a heterogeneous sensor network. Using ATEMU it is possible to not only accurately simulate the operation of different application on the MICA2 platform but also a complete sensor network where the sensor nodes themselves maybe based on different hardware platforms. In addition we also describe our implementation of XATDB, our front-end debugger/GUI for ATEMU. XATDB provides an excellent educational tool for people to start learning about the operation of sensor nodes and sensor networks, without requiring the purchase of actual sensor node hardware. The accuracy and emulation capabilities provided by ATEMU ensure that when and if actual hardware is used, the software will already have undergone rigorous testing and debugging on an accurate platform. This would provide the sensor network deployment community with a much more accurate estimate of the performance of various algorithms and protocols in realistic scenarios and platforms.

In this paper we consider a class of loss networks that have arbitrary topologies and routes of arbitrary length. Multiple traffic classes are present, each with different bandwidth requirement, and each routed according to a state-dependent routing scheme. In particular, we consider the least loaded routing method generalized to routes of arbitrary number of hops. The connection level performance metric of interest is the end-to-end blocking probability. We are interested in developing fast evaluation methods to provide reasonably accurate estimates of the blocking probability, especially under heavy traffic load. Our algorithms are based on the fixed-point method framework, also known as the reduced load approximation. In addition to what commonly examined by previous work, two more factors contribute to the complexity of the computation in the scenario under consideration in this paper. One is the state-dependent nature of the routing mechanism, the other is the possible overlapping between routes due to the general multihop topology of the network. We present two fast approximation algorithms to evaluate the blocking probability with state-dependent routing by simplifying the route overlapping computation. We discuss the computational complexity of our algorithms as well as sources of approximation error. We then compare the numerical results with that of simulation and show that our algorithms provide fairly accurate blocking probability estimates especially under heavy traffic load.

In this paper, we propose a knapsack-based feedback suppressionalgorithm for reliable multicast transport protocols operating over asatellite network. A reliable transport protocol needs to identify thepackets which failed to reach a given destination. This is achievedthrough feedback packets returned to the source. For multicastservices, receiver feedback has been shown to lead to thefeedback implosion problem. Feedback implosion is awell-studied problem and various solutions exist in the literature.However, these solutions mainly focus on wireline terrestrial networksand do not take into account the inherent characteristics of thesatellite channel and the architecture of the deployed network.Therefore, we need to revisit the problem and provide a new set ofsolutions for efficient integration to next generation satellitesystems. In this paper, we introduce a feedback implosion suppressionalgorithm, which effectively suppresses the amount of feedback relayedthrough the satellite channel, while ensuring that the criticalinformation is conveyed in a timely fashion. The performance of thealgorithm is evaluated through simulations.

In this paper we propose a new statistical model for multiscale traffic, and present an exact queueing analysis for the model. The model is based on the central moments and the marginal distributions of the cumulative traffic loads in different time scales. Only the first two moments are needed to characterize the traffic process, which greatly simplifies the representation and estimation. The queueing analysis uses a very general approach and can evaluate not only the steady state performance but also the transient queueing behavior. The analysis reveals that there exist two classes of packet losses, the absolute loss and the opportunistic loss, both of which can be examined exactly with the method. Based on the statistical model and the queueing analysis method, a compound model is constructed for the practical multiscale traffic, and its performance is evaluated from various aspects. This work provides a good basis for practical application of the multiscale traffic characterizations in network dimensioning and resource management.

This paper addresses data-aided (DA) synchronization, in which the reference parameter acquisition is aided by a training sequence known to the receiver. The Cramer-Rao lower bound (CRB) for the DA timing and/or carrier phase recovery is presented. For DA parameter estimation, the CRB typically varies with the training sequence. This indicates that different training sequences offer fundamentally different performance. In the literature, the widely cited closed-form CRB for timing and carrier phase recovery was derived under the assumption that the training sequence is independent and identically distributed (i.i.d.) and sufficiently long. We derive a closed-form CRB for timing and carrier phase recovery with respect to an arbitrary training sequence and pulse shaping function for the over and under sampling cases. It turns out that the CRB is a weighted summation of the aperiodic correlation of the training sequence and the weighting factor is determined by the pulse shaping filter. Therefore, this paper reveals the fundamental link between a training sequence and its corresponding performance limit.

Many issues in signal processing involve the inverses of Toeplitz matrices. One widely used technique is to replace Toeplitz matrices with their associated circulant matrices, based on the well-known fact that Toeplitz matrices asymptotically converge to their associated circulant matrices in the weak sense. This often leads to considerable simplification. However, it is well known that such a weak convergence cannot be strengthened into strong convergence. It is this fact that severely limits the usefulness of the close relation between Toeplitz matrices and circulant matrices. Observing that communication receiver design often needs to seek optimality in regard to a data sequence transmitted within finite duration, we define the finite-term strong convergence regarding two families of matrices. We present a condition under which the inverses of a Toeplitz matrix converges in the strong sense to a circulant matrix for finite-term quadratic forms. This builds a critical link in the application of the convergence theorems for the inverses of Toeplitz matrices since the weak convergence generally finds its usefulness in issues associated with minimum mean squared error and the finite-term strong convergence is useful in issues associated with the maximum-likelihood or maximum a posteriori principles.

Information broadcasting is an effective method to deliver popular information packages to a large number of users in wireless and satellite networks. We address the problem of broadcast scheduling in the pull environment and try to solve this problem by formulating it as a dynamic optimization problem. This approach allows us to find a near-optimal scheduling policy, which we use as a benchmark to evaluate a number of other existing heuristic policies. Also, in addition to providing a solution for the usual case with Poisson arrivals and equal priority pages, our approach enables us to address extended versions of this problem with other arrival processes and with distinct weights assigned to different pages.

We outline the first steps of an effort to start defining a communications architecture for supporting broadband data communications from the International Space Station. We focus on a direct-to-ground architecture, which could serve as an intermediate solution to satisfy the near term communications needs of commercial experiments and payloads on the ISS and overcome certain limitations of the current ISS communications infrastructure. We address three communications options and evaluate an architecture for the direct-to-ground option, focusing on a particular user's requirements, communications links, and coverage availability. We also discuss system, mobility support and protocol issues that need to be addressed for this solution to be a feasible alternative.

The paper analyzes how using a longer memory of the past queue occupancy in computing the average queue occupancy affects the stability and variability of a RED queue. Extensive simulation studies with both persistent and Web TCP sources are used to study the variance of the RED queue as a function of the memory of the averaging process. Our results show that there is very little performance improvement (and in fact, possibly significant performance degradation) if the length of memory is increased beyond a very small value. Contrary to current practice, our results show that a longer memory reduces the negative correlation typically observed among the windows of the constituent TCP flows, and hence, suggest the use of the instantaneous queue occupancy in practical RED queues.

We present system designs for adaptive, distributed network monitoring and control. The ideas are to distribute some processing intelligence to network elements, and to design a dynamic interface such that the delegated agents could be managed remotely by the manager. The functionality of the delegated agents, and even that of the native software processes, could be extended dynamically without recompilation. The creation, deployment, operation, and management of the delegated agents require a standard infrastructure on each system where these agents need to be hosted. We use Java technology and C/C++ dynamic linkage mechanism to fulfil such a requirement under different situations and our system designs span a wide scope of applications.

Previous literature presents several seemingly different approaches to rooted-tree-based multicast key distribution schemes that try to minimize the user key storage while providing efficient member deletion. In this paper, we show that the user key storage on rooted trees can be systematically studied using basic concepts from information theory. We show that the rooted-tree-based multicast key distribution problem can be posed as an optimization problem that is abstractly identical to the optimal codeword length selection problem in information theory. In particular, we show that the entropy of member deletion statistics quantifies the optimal value of the average number of keys to be assigned to a member. We relate the sustainable key length to statistics of member deletion event and the hardware bit generation rate. We then demonstrate the difference between the key distribution on rooted trees and the optimal codeword length selection problem with an example of a key distribution scheme that attains optimality but fails to prevent user collusion.

This paper addresses the channel estimation problem for slow frequency-selective fading channels using training sequences and the maximum likelihood (ML) approach. In the literature people usually assume a symbol period spaced delay-tapped-line model and additive white Gaussian noise (AWGN). Due to the pre-filtering in the receiver front end, if the sampling rate is larger than one sample per symbol or sampling epoch is unknown (i.e., the timing information is unavailable), the AWGN model is not valid anymore. A more general ML channel estimation method using the discrete Fourier transform (DFT) is derived for colored Gaussian noise and over-sampling. A similar idea can be adopted to derive the ML joint carrier phase and timing offsets estimation algorithm.

We present a hierarchical loss network model for estimating the end-to end blocking probabilities of large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so far all work being done in this area is for flat networks with flat routing schemes. We aim at developing a more efficient approximation method for networks that have a natural hierarchy and/or when some form of hierarchical routing policy is used. We present two hierarchical models in detail for fixed hierarchical routing and dynamic hierarchical routing policies, respectively, via the notion of network abstraction, route segmentation, traffic segregation and aggregation. Computation is done separately within each cluster (local) and among clusters (global), and the fixed point is obtained by iteration between local and global computations. We present results from both numerical experiments and discrete event simulations.

To model possible suggested changes in TCP window adaptation in response to randomized feedback, such as ECN (explicit congestion notification), we formulate a generalized version of the TCP congestion avoidance algorithm. We first consider multiple such generalized TCP flows sharing a bottleneck buffer under the assured service model and use a fixed point technique to obtain the mean window sizes and throughputs for the TCP flows, To further study how changes in the adaptation algorithm affect the variability in the throughput, we use an analytical-cum-numerical technique to derive the window distribution (and related statistics) of a single generalized flow under state-dependent randomized congestion feedback.

This paper concerns the Cramer-Rao lower bound (CRB) for the data-aided (DA) timing and/or phase recovery, i.e., the synchronization parameter acquisition is aided by a training sequence known to the receiver. For the DA parameter estimation, the CRB typically varies with the training sequence. This indicates that different training sequences offer fundamental different performance. We derive a closed-form formula of the CRB for timing and phase recovery with respect to any particular training sequence. The bound illustrates the close relation between the training sequence and the fundamental limit on timing and phase synchronization. It provides additional insights on the training sequence design.

This paper presents a theoretical framework based on Bayesian decision theory for analyzing recently reported results on implicit coscheduling of parallel applications on clusters of workstations. Using probabilistic modeling, We show that the approach presented can be applied for processes with arbitrary communication mixes. We also note that our approach can be used for deciding the additional spin times in the case of spin-yield. Finally, we present arguments for the use of a different notion of fairness than assumed by prior work.

A high performance ASIC supporting multiple modulation, error correction, and frame formats is under development at Hughes Network Systems, Inc. Powerful and generic data-aided (DA) estimators are needed to accommodate operation in the required modes. A simplified DA maximum likelihood (ML) joint estimator for carrier phase and symbol timing offset for QPSK/OQPSK burst modems and a sample systolic VLSI implementation for the estimator are presented. Furthermore, the Cramer-Rao lower bound (CRLB) for DA case is investigated. The performance of the estimator is shown through simulation to meet the CRLB even at low signal-to-noise ratios (SNR). Compared with theoretical solutions, the proposed estimator is less computationally intensive and is therefore easier to implement using current VLSI technology.

Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from automatic target recognition (ATR) to medical diagnosis, speech recognition, and fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for combined compression and classification. We show convergence of the algorithm using the ODE method from stochastic approximation. We illustrate the performance of the algorithm with some examples.

Summary form only given, as follows. Recent advances in wireless communications have created tremendous opportunities for the design and deployment of broadband information infrastructures, rapidly and inexpensively. These new infrastructures can be deployed even in parts of the world where currently there exist very poor information or telephone networks. The relevant technologies include direct broadcast systems (DBS), local multi-point distribution systems (LMDS) and multi-channel multi-point distribution systems (MMDS). These technologies couple in a synergistic way with wire-line technologies such as hybrid fiber coaxial (HFC) networks and fiber-optic (FO) networks, as well as wireless local area networks (WLAN). In this paper, we describe several network architectures that combine these technologies and evaluate their promise from the point of view of providing high-quality video distribution and fast broadband Internet services through the same physical network. We then present several technical developments which describe asymmetric Internet services that we have developed for these networks. In particular, we present several results on network operations center algorithms for interactive and broadcast Internet services. Finally, we present results for the integrated network management of these innovative hybrid communication networks.

The performance degradation as a result of acoustical environment mismatch remains an important practical problem in speech recognition. The problem carries a greater significance in applications over telecommunication channels, especially with the wider use of personal communications systems such as cellular phones which invariably present challenging acoustical conditions. In this work, we introduce a vector quantization (VQ) based compensation technique which both makes use of a priori information about likely acoustical environments and adapts to the test environment to improve recognition. The technique is progressive and requires neither simultaneously recorded speech from the training and the testing environments nor EM-type batch iterations. Instead of using simultaneously recorded data, the integrity of the updated VQ codebooks with respect to acoustical classes is maintained by endowing the codebooks with a topology and using transformations which preserve the topology of the reference environment. We report results on the McCaw Cellular Corpus where the technique decreases the word error for continuous ten digit recognition of cellular hands free microphone speech with land line trained models from 23.8% to 13.6% and the speaker dependent voice calling sentence error from 16.5% to 10.6%.

We study the problem of choosing an image based optimal wavelet basis with compact support for image data compression and provide a general algorithm for computing the optimal wavelet basis. We parameterize the mother wavelet and scaling function of wavelet systems through a set of real coefficients of the relevant quadrature mirror filter (QMF) banks. We further introduce decomposition entropy as an information measure to describe the distance between a given digital image and its projection into the subspace spanned by the wavelet basis. The optimal basis for the given image is obtained through minimizing this information measure. The resulting subspace is used for image analysis and synthesis. Experiments show improved compression ratios due to the application of the optimal wavelet basis and demonstrate the potential applications of our methodology in image compression. This method is also useful for constructing efficient wavelet based image coding systems.

In many modern database applications, there is an emerging need for interactive environments where users directly manipulate the contents of the database. Graphical user interfaces (GUIs) display images of the database which must reflect a consistent up-to-date state of the data with minimum perceivable delay to the user. Moreover, the possibility of several applications concurrently displaying different views of the same database increases the overall system complexity. In this paper, we show how design, performance and concurrency issues can be addressed by adapting existing database techniques. We propose the use of suitable display schemas whose instances compose active views of the database, an extended client caching scheme which is expected to yield significant performance benefits, and a locking mechanism that maintains consistency between the GUIs and the database.

Morphological openings and closings can be viewed as consistent MAP estimators of smooth random binary image signals immersed in i.i.d. clutter, or suffering from i.i.d. random dropouts. We revisit this viewpoint under much more general assumptions and show that, quite surprisingly, the above interpretation is still valid.

We derive estimators for the multiple lag process, a generalization of the lag process, via spectral representations of stationary processes by complex random spectral measures. We present estimators of transfer functions for the multiple lag model with a given vector of lags and derive a multiple-lag (quadratic) coherence which can be maximized to choose the best vector of lags in the minimum mean squared error sense from a given set of lag vectors. We also demonstrate the estimation scheme by a simulation example and point out possible applications for the multiple-lag model in speech processing.

We have shown previously that morphological openings and closings can be viewed as consistent MAP estimators of morphologically smooth binary image signals immersed in i.i.d. union (clutter) noise, or suffering from i.i.d. random dropouts. We revisit this viewpoint under a different set of assumptions, which allows the explicit incorporation of geometric and morphological constraints into the noise model, i.e., the noise may now exhibit a geometric structure; surprisingly, it turns out that this affects neither the optimality nor the consistency of these filters.

We model digital binary image data as realizations of a uniformly bounded discrete random set (or discrete random set, for short), which is a mathematical object that can be directly defined on a finite lattice. We consider the problem of estimating realizations of discrete random sets distorted by a degradation process that can be described by a union/intersection noise model. Two distinct optimal filtering approaches are pursued. The first involves a class of "mask" filters, which arises quite naturally from the set-theoretic analysis of optimal filters. The second approach involves a class of morphological filters. We prove that under i.i.d noise morphological openings, closings, unions of openings, and intersections of closings can be viewed as MAP estimators of morphologically smooth signals. Then, we show that by using an appropriate (under a given degradation model) expansion of the optimal filter, we can obtain universal characterizations of optimality that do not rely on strong assumptions regarding the spatial interaction of geometrical primitives of the signal and the noise. The results generalize to gray-level images in a fairly straightforward manner.

There has been a growing demand for the development of tools to manage enterprise communication networks. A management information database is the heart of a network management system-it provides the interface between all functions of the network management system and, therefore, has to provide sophisticated functionality allied with high performance. The authors introduce the design of MANDATE (MAnaging Networks using DAtabase TEchnology), a proposed database system for effectively supporting the management of large enterprise networks. The MANDATE design makes a conscious attempt to take advantage of the special characteristics of network data and transactions, and of advances in database technology, to efficiently derive some of the required management functionality.>

Experimental investigation of a free-text speaker identification method based on long-term statistics was conducted on a widely used long-distance telephone database. On a 26-speaker subset, an average correct identification of 93.3% was obtained; on the complete 51-speaker set, 67.6% correct identification is obtained. Speaker verification experiments on the database provided receiver operating characteristics (ROCs) comparable to or better than the ones available in open literature.>

It is shown that the learning vector quantization (LVQ) algorithm (T. Kohonen, 1986), converges to locally asymptotic stable equilibria of an ordinary differential equation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. Also presented is a modification to the learning algorithm which results in more robust convergence. With this modification, it is possible to show that as the appropriate parameters go to infinity, the decision regions associated with the modified LVQ algorithm approach the Bayesian optimal

The problem of estimating a member of a discrete-time vector process from past and present quantum mechanical measurements is considered; specifically, the minimum-variance linear estimator based on optimal present measurements is studied. Necessary and sufficient conditions that characterize the optimal processing matrix coefficients and the optimal measurements are discussed and interpreted. The optimal linear filter is compared to the optimal quantum estimator without postprocessing of past data. When the signal sequence is pairwise Gaussian and the optimal quantum measurement without postproccssing has the properties that it is linear in a specific sense and that its outcome and the corresponding element of the signal sequence are jointly Gaussian, then the optimal linear filter separates. That is, the optimal measurement can be taken to be the same as thc optimal measurement without regard to past data, and the past and present data are processed classically. Thc results are illustrated by considering the filtering of the in-phase and quadrature amplitudes of a laser field received in a single-mode cavity along with thermal noise. In this case, when the random signal sequence satisfies a linear recursion, the estimate can be computed recursively in a very efficient manner.

The problem of estimating a member of a scalar random signal sequence with quantum-mechanical measurements is considered. The minimum variance linear estimator based on an optimal present quantum measurement and optimal linear processing of past measurements is found. When the average optimal measurement without postprocessing, for a fixed signal, is linear in the random signal and the signal sequence is pairwise Gaussian, the optimal processing separates: the optimal measurement is the same as the optimal measurement without regard to past data, and the past and present data are processed classically. The results are illustrated by considering the estimator of the real amplitude of a laser signal received in a single-mode cavity along with thermal noise; when the random signal sequence satisfies a linear recursion, the estimate can be computed recursively. For a one-step memory signal sequence it is shown that the optimal observable generally differs from the optimal observable disregarding the past; the optimal measurement can be computed recursively.