QDE is an emerging paradigm in which an ancillary quantum system is used to remove entropy from a primary quantum system of interest. As a means for entropy reduction, quantum dissipation engineering (QDE) is useful across the domains of quantum sensing, computing, communication, and networking. Whereas most existing approaches concern QDE over an infinite time horizon, this paper studies the use of engineered environments to prepare the primary system in a pure state within finite time. This corresponds to completely transferring the primary system's initial entropy to the engineered environment in finite time. Necessary and sufficient conditions for performing this task are derived. These conditions elucidate the potential of finite-time QDE using real-world quantum systems.

Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishment (REE). REE typically consists of three types of quantum operations: entanglement generation, distillation, and swapping. By carefully designing the sequence describing the order of these operations, this paper investigates REE in a repeater chain under the requirement that the fidelity of the established entanglements be above a desired threshold. Specifically, the paper derives an asymptotically achievable upper bound on the maximum REE rate.

Entanglement is crucial for many applications such as quantum computing, quantum sensing, and quantum communication. Establishment of entanglement between remote nodes, referred to as remote entanglement establishment (REE), is a key element of the quantum internet. This paper develops a theoretical framework for establishing high-fidelity entanglement between two remote nodes of a quantum repeater chain via entanglement generation, distillation, and swapping operations. In particular, an upper bound on the optimal REE rate under minimum fidelity requirements is established, and an REE policy that achieves such a bound asymptotically is presented. Results in this paper provide guidelines for protocol design in the quantum internet.

Quickest detection problems are fairly common in surveillance applications, as framing surveillance alerts as a change in an observation sequence’s statistics is often apt. In this work, we consider the scenario where an appropriate statistical description of our observations is not available, neither before nor after the transient we are trying to detect. In this vein, we explore the use of the database Lempel-Ziv, or LZ77, procedure, to detect this transient in the observation data. This algorithm is known to have phrase lengths that are asymptotically distributed as Gaussian random variables, which allows us to form a quickest detection problem around statistics of the coded output. This work specifies procedures to perform source-agnostic transient detection using Locally Optimal (LO) statistic to augment a Page CUSUM test. The work also shows an application to acoustic data.

All practical sensing operations must work with quantized data. Along with measurements, each sensor is assumed to have some "label" value that is relevant to its stochastic measurement parameterization and must be communicated to the decision center. We are interested in cases that require very low communication cost, and thus require very "coarse" quantization of the measurements as well as the labels (2-bit values, for instance). Censoring is used to control the expected communication cost—each sensor decides locally whether or not to send its data to the decision center based on the value of its label as well as the value of its measurement. In this work we formalize the test statistic based on censored and quantized data.

Consider a test at a particular point in space for the existence of a point target using intensity measurements from passive sensors distributed uniformly around the test location. The distance from the test location of a particular sensor is relevant to the decision making, and is considered "labeling" on the sensor’s intensity data. This work considers the case where both the intensity data and the label (distance) values are coarsely quantized to decrease communication cost. It will be shown that, for a given per-measurement communication budget, there exists an ideal quantization rule. The results provide a method for choosing between possible apportionments of the communication budget between data (intensity) and labeling (distance).

This paper develops an easily-implementable version of Page's CUSUM quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics. The decision statistic can be cast in a recursive form and is particularly suited for on-line analysis. By back-testing our approach on publicly-available COVID-19 data we find reliable early warning of infection flare-ups, in fact sufficiently early that the tool may be of use to decision-makers on the timing of restrictive measures that may in the future need to be taken.

We study “active” decision making over sensor networks where the sensors' sequential probing actions are actively chosen by continuously learning from past observations. We consider two network settings: with and without central coordination. In the first case, the network nodes interact with each other through a central entity, which plays the role of a fusion center. In the second case, the network nodes interact in a fully distributed fashion. In both of these scenarios, we propose sequential and adaptive hypothesis tests extending the classic Chernoff test. We compare the performance of the proposed tests to the optimal sequential test. In the presence of a fusion center, our test achieves the same asymptotic optimality of the Chernoff test, minimizing the risk, expressed by the expected cost required to reach a decision plus the expected cost of making a wrong decision, when the observation cost per unit time tends to zero. The test is also asymptotically optimal in the higher moments of the time required to reach a decision. Additionally, the test is parsimonious in terms of communications, and the expected number of channel uses per network node tends to a small constant. In the distributed setup, our test achieves the same asymptotic optimality of Chernoff's test, up to a multiplicative constant in terms of both risk and the higher moments of the decision time. Additionally, the test is parsimonious in terms of communications in comparison to state-of-the-art schemes proposed in the literature. The analysis of these tests is also extended to account for message quantization and communication over channels with random erasures.

Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scenario where the state of nature may change arbitrarily. We propose a data diffusion scheme for tracking these changes in real time, and investigate by numerical simulations the corresponding steady-state decision performance. For the slow-adaptation regime, the complete analytical characterization of the agents' status is provided, under the simplifying assumption that the network connection matrix is correctly estimated.

Pansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-the-art pansharpening techniques.

Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling. Our focus here is on binary alphabets, and we ask: If our data have been shuffled in an unknown way, can a reliable decision about the underlying state of nature be made? Should the decision be made after an attempt to estimate the lost labels? And do there exist easily implementable decision rules? In answering these questions, we gain much insight: We show that two greedy algorithms previously introduced in the literature are equivalent to the GLRT, whose performance can be quite poor, and the detector known as ULR is equivalent to a simple counting rule. A new detector based on the central limit theorem is simply implementable and offers close-to-optimal performance in many scenarios of practical interest.

This paper studies the operation of multi-agent networks engaged in binary decision tasks, and derives performance expressions and performance operating curves under challenging conditions with some revealing insights. One of the main challenges in the analysis is that agents are only allowed to exchange one-bit messages, and the information at each agent therefore consists of both continuous and discrete components. Due to this mixed nature, the steady-state distribution of the state of each agent cannot be inferred from direct application of central limit arguments. Instead, the behavior of the continuous component is characterized in integral form by using a log-characteristic function, while the behavior of the discrete component is characterized by means of an asymmetric Bernoulli convolution. By exploiting these results, this paper derives reliable approximate performance expressions for the network nodes that match well with the simulated results for a wide range of system parameters. The results also reveal an important interplay between continuous adaptation under constant step-size learning and the binary nature of the messages exchanged with neighbors.

Suppose that the energy made available to a sensor network at the beginning of a time slot is proportional to the success of the network inferential task during the previous slot. And further, assume that such energy is to be apportioned to charge the individual sensors, such that the more energy one sensor receives, the better it does its job. Then, the information gathered by the network in the long run consequently obeys a multiplicative rule, which enables us to adapt some results from portfolio theory to design the optimal apportionment. Two regimes emerge, one in which the expected value of the long-run information is key and all the energy is assigned to the “best” sensor, and another - more tricky - where the expected logarithm of the long-run information matters, and the solution is given by Cover's log-optimal apportionment.

Consider a binary hypothesis where data are independent and identically distributed under the null hypothesis, and known only to be independent under the alternative. The statistician observes an n- vector Xn(n ≫ 1) and makes a decision using the optimal likelihood ratio test. This seems a widely-known detection problem, but: What if only the set of samples of Xnare made available to the statistician, while the positions of the individual samples inside the vector are not? Does there exist an optimal test in that case? What is the fundamental performance limit? Are there nicely-performing practical detectors with affordable computational complexity? Answers to these questions are in large part unknown, despite the fact that the problem - which is becoming known under the name of unlabeled detection - is very relevant in modern sensor network applications where the sample positions can be lost due to their means of delivery from the remote units, or because of network attacks.

We propose a decentralized, sequential and adaptive hypothesis test in sensor networks, which extends Chernoff's test to a decentralized setting. We show that the proposed test achieves the same asymptotic optimality of the original one, minimizing the expected cost required to reach a decision plus the expected cost of making a wrong decision, when the observation cost per unit time tends to zero. We also show that the proposed test is parsimonious in terms of communications. Namely, in the regime of vanishing observation cost per unit time, the expected number of channel uses required by each sensor to complete the test converges to four.

In modern heterogeneous sensor networks huge volumes of information rapidly flow across the system, and it is often too difficult or costly to associate data to the sensors that produced them. Then, the set of observations appears to be unlabeled: What comes from whom? We study the classical problem of detecting a known signal embedded in Gaussian noise, but under the peculiar assumption that the signal samples have been scrambled (e.g., in time or space) in an unknown way. Our study sheds light on questions like: How much detection performance is contained in the samples' values and how much in their ordering? Are there nicely-performing detectors with affordable computational complexity?

Parametric estimation for the generative social sensing model proposed in [19,20] is addressed. First, we provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the fundamental scaling laws as the number of network agents and/or the number of monitored agents' activities is large. Then, we examine two viable estimation procedures that can be useful even in such large dataset applications: the Expectation-Maximization and the Fisher scoring algorithms, which both achieve the aforementioned performance bounds.

We examine the interplay between learning and privacy over multiagent consensus networks. The learning objective of each individual agent consists of computing some global network statistic, and is accomplished by means of a consensus protocol. The privacy objective consists of preventing inference of the individual agents' data from the information exchanged during the consensus stages, and is accomplished by adding some artificial noise to the observations (obfuscation). An analytical characterization of the learning and privacy performance is provided, with reference to a consensus perturbing and to a consensus-preserving obfuscation strategy.

This paper examines the close interplay between cooperation and adaptation for distributed detection schemes over fully decentralized networks. The combined attributes of cooperation and adaptation are necessary to enable networks of detectors to continually learn from streaming data and to continually track drifts in the state of nature when deciding in favor of one hypothesis or another. The results in this paper establish a fundamental scaling law for the steady-state probabilities of miss detection and false alarm in the slow adaptation regime, when the agents interact with each other according to distributed strategies that employ small constant step-sizes. The latter are critical to enable continuous adaptation and learning. This paper establishes three key results. First, it is shown that the output of the collaborative process at each agent has a steady-state distribution. Second, it is shown that this distribution is asymptotically Gaussian in the slow adaptation regime of small step-sizes. Third, by carrying out a detailed large deviations analysis, closed-form expressions are derived for the decaying rates of the false-alarm and miss-detection probabilities. Interesting insights are gained from these expressions. In particular, it is verified that as the step-size μ decreases, the error probabilities are driven to zero exponentially fast as functions of 1μ, and that the exponents governing the decay increase linearly in the number of agents. It is also verified that the scaling laws governing the errors of detection and the errors of estimation over the network behave very differently, with the former having exponential decay proportional to 1μ, while the latter scales linearly with decay proportional to μ. Moreover, and interestingly, it is shown that the cooperative strategy allows each agent to reach the same detection performance, in terms of detection error exponents, of a centralized stochastic-gradient solution. The results of this paper are illustrated by applying them to canonical distributed detection problems.

In [1], an important step toward the characterization of distributed detection over adaptive networks has been made by establishing the fundamental scaling law of the error probabilities. However, empirical evidence reported in [1] revealed that a refined asymptotic analysis is necessary in order to capture the exact impact of network connectivity on the detection performance of each individual agent. Here we address this open issue by exploiting the framework of exact asymptotics.

In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The proposed tracking strategy is based on a Bayesian framework, in which the dynamic target state is augmented to include the sensor detection probabilities. The method is validated using computer simulations and real-world experiments conducted by the NATO Science and Technology Organization (STO) - Centre for Maritime Research and Experimentation (CMRE).

In distributed inference, local cooperation among network nodes can be exploited to enhance the performance of each individual agent, but a challenging requirement for networks operating in dynamic real-world environments is that of adaptation. The interplay between these two fundamental aspects of cooperation and adaptation has been investigated in recent years in the context of estimation problems. Less explored in the literature is the case of detection, which is our focus. Capitalizing on the powerful tool of large deviations analysis, we show how to design and characterize the performance of diffusion strategies that reconcile both needs of adaptation and detection in decentralized systems.

This paper presents a Bayesian multi-sensor tracking strategy for a network of autonomous underwater vehicles (AUVs) for the purpose of anti-submarine warfare (ASW). A bistatic configuration and the corresponding acoustic model for the bistatic signal-to-noise ratio (SNR) is used. The Bayesian posterior distribution of the target state based on all available information from sensors and on the acoustic model is reconstructed via particle filtering methods, taking into account the port-starboard ambiguity typical of horizontal line arrays. The posterior distribution is the optimal estimation procedure, the only approximation derives from the particle representation. The effectiveness of the proposed algorithm is demonstrated on a real data set collected by the NATO Centre for Maritime Research and Experimentation (CMRE) during the NATO Proud Manta 2012 exercise (ExPOMA12).

In one of his landmark papers, Cover established the fundamental scaling laws of learning with nearest-neighbor rules (T.M. Cover, 1968). With the recent advances on distributed nearest-neighbor learning in sensor networks novel trade-offs arise, involving the faithfulness of message representation (quantization bits) and the number of delivered messages (transmitting sensors). This is the main theme of this paper.

Nowadays, the most investigated concepts of physical layer security encompass asymptotic criteria of secrecy. Taking a different perspective, in this work we come back to Shannon's original formulation of perfect secrecy, amounting to impose exactly zero mutual information between the source message and the data gathered by the eavesdropper. By jamming the intruder with a special class of noise-that we call pdf-bandlimited-and adopting a novel “out of the (pdf) band” information encoding technique, it is shown that a perfectly secure communication can be in fact sustained over the main channel, leaving no chances to the eavesdropper.

Recently it has been shown that the Multi-Sensor Probability Hypothesis Density (MS-PHD) has some optimality properties in the regime of large number of sensors [1, 2], achieving the same performance of the Bayes multi-sensor/multi-target posterior in the Random Finite Set (RFS) framework [3]. However, when the number of sensors N is relatively large, the traditional PHD filter loses its computational efficiency, the complexity being exponential in N. On the other hand, the complexity of the full Bayes posterior is only linear in N, and this paper suggests an idea for its computation using Sequential Monte Carlo (SMC) methods. The MS-PHD is then evaluated, and numerical examples show that it is possible to deal with a scenario where the number of sensors is very large while targets, appearing and disappearing, evolve in time.

A wireless sensor network is engaged in a statistical learning task, to be accomplished in a decentralized fashion. The focus here is in distributed Nearest-Neighbor (NN) regression, in the presence of communication constraints. We first introduce a general channel access policy which allows the fusion center to recover training-set labels ordered according to the NN criterion, in the absence of any data exchange among sensors. Then, two different paradigms are considered, where the communication cost is measured as: i) the channel accesses; ii) the quantization bits. In the former scenario, we propose a distributed NN strategy reaching an asymptotic performance of twice the minimum achievable mean-square error, with only one sensor transmitting information. In the latter case, we achieve universally consistent distributed NN regression even with one-bit quantized labels.

Given two independent point processes and a certain rule for matching points between them, what is the fraction of matched points over infinitely long streams? In many application contexts, e.g., secure networking, a meaningful matching rule is that of a maximum causal delay, and the problem is related to embedding a flow of packets in cover traffic such that no timing analysis can detect it. We study the best undetectable embedding policy and the corresponding maximum flow rate-that we call the embedding capacity-under the assumption that the cover traffic can be modeled as an arbitrary renewal process. We find that computing the embedding capacity requires the inversion of a very structured linear system that, for a broad range of renewal models encountered in practice, admits a fully analytical expression in terms of the renewal function of the processes. This result enables us to explore the properties of the embedding capacity, obtaining closed-form solutions for selected distribution families and a suite of sufficient conditions on the capacity ordering. We test our solution on real network traces, which shows a remarkable match for tight delay constraints. A gap between the predicted and the actual embedding capacities appears for looser constraints, and further investigation reveals that it is caused by inaccuracy of the renewal traffic model rather than of the solution itself.

The secure networking problem of embedding information flows into cover traffic is addressed. When relayed packets must obey a causal delay constraint, this naturally remaps to a matching problem between point processes (here taken as arbitrary renewal processes). The best hiding policy is thus characterized in terms of the maximum fraction of matched points, which is accordingly referred to as embedding capacity. For a broad range of renewal models encountered in practice, we provide a simple analytical formula for the capacity, which depends only on the renewal function of the underlying processes, and further find conditions for capacity-ordering of different types of cover traffic. The results are also tested on real network traces, and a very good match is observed, especially for tight delay constraints.

A random parameter is estimated by a distributed network of sensors that communicate over a common MAC. The channel implies an enforced additive fusion rule, and the goal here is to design a power-constrained forwarding strategy and the post-processing by the fusion center. To get an explicit solution we appeal to asymptotics, meaning that we design the locally optimal scheme for the limiting case that the received power goes to zero.

Stochastic resonance (SR) is a phenomenon long investigated by physicists that has recently attracted some interest in the signal processing literature. In this paper, we explore the potential benefits of the SR effect for shift-in-mean detection problems, specifically focusing on sequential decision rules. Amenable formulas for the optimal distribution of the SR noise, as well as an asymptotic comparison with the traditional Neyman-Pearson approach are obtained.

We consider the problem of DOA (direction of arrival) estimation of an acoustic wavefront by a wireless sensor network (WSN) within the SENMA architecture (a mobile agent (MA) repeatedly polls the sensors lying inside its field of view). The sensors, which are random in number and location, are DOA-blind and simply emit a pulse train synchronized to the acoustic event's passage; taken in aggregate, however, the MA can exploit its non-isotropic field of view (FOV) to infer an accurate DOA. In this paper we (i) use a more realistic "soft" FOV; (ii) account for multiple sources; and (iii) suggest a strategy for the MA. We find that the presence of more than one source can improve DOA estimation, and also that an optimal strategy for the MA squints near the expected DOA, as opposed to directly at it.

We introduce a likelihood based multiple access (LBMA) communication/estimation scheme for nonrandom parameter estimation in wireless sensor networks with additive multiple access channels. Constraining the system in terms of energy and allowing the available number of degrees of freedom to scale as nα, 0.5 <; α <; 1, we prove that LBMA is asymptotically efficient. Thus, the new scheme is appropriate for large networks. LBMA is, in addition, simple to implement and relies upon an intuitive approach.

A successful mathematical description of natural landscapes relies upon a class of random processes known as fractional Brownian motions (fBms), which may exhibit correlation with long-range dependence (LRD). In remote sensing applications, the sensor observes a certain real sceneBand records dataIfor successive signal processing tasks. Assuming thatBis modeled as an fBm, does the recorded signalIpreserve the LRD character ofB? More in general, can we relate the Hurst coefficient (an index of LRD) of the real scene to that of the recorded data? We address the problem in a simplified setup in which the data are related to (the slope of) the original scene through a zero-memory mapping. A mathematical framework is presented in which the above questions can be answered in the asymptotic regime of infinite data size. The effect of the finite sample size is also investigated. The mathematical model is also validated by real data, which are collected by a synthetic aperture radar that is mounted onboard of ERS-1/2 satellites.

A new multiple access scheme based on energy discrimination is proposed. Such a scheme is based on the differences in the average power received by each user. It is particularly useful when no extra resources are available by means of existing schemes; moreover, it is useful when the system complexity must be kept very limited.

Quantization for estimation is explored for the case that it must be performed jointly with data association; that is, the case in which measurements are of uncertain origin. Data association requires some sort of gating of distributed observations, and a censoring strategy is proposed. Several quantization philosophies are explored, specifically uniform quantization, uniform quantization with measurement exchangeability incorporated (the "type" method), and uniform quantization of sorted measurements. It is shown, perhaps surprisingly, that the third scheme preserves more information that may be useful for estimation; and a simple procedure for optimal fused estimation based on this third scheme is given. Interestingly, when compared in terms of rate-distortion curves, the schemes two and three perform similarly; their censored versions offer further improvement in performances due to the uncertain-origin property of the measurements.

Following the SENMA concept, we consider a wireless network of very dumb and cheap sensors, polled by a travelling "rover". Sensors are randomly placed and isotropic: individually they have no ability to resolve the direction of arrival (DOA) of an acoustic wave. We assume that the communication load must be as limited as possible, so that these times cannot be communicated to the rover. Notwithstanding the lack of transmission of arrival times and the lack of DOA resolution ability of the individual sensors, DOA estimation is possible, and asymptotic efficiency becomes closely approximated after a reasonable number of rover snapshots. Key features are the directionality of the rover antenna, the area it surveys, and the average number of sensors inside that area, as accorded a Poisson distribution.

Following the SENMA concept, we consider a wireless network of very dumb and cheap sensors, polled by a travelling "rover". Sensors are randomly placed and isotropic: Individually, they have no ability to resolve the direction of arrival (DOA) of an acoustic wave. However, they do observe the wavefront at different times. We assume that the communication load must be as limited as possible, so that these times cannot be communicated to the rover. Notwithstanding the lack of transmission of arrival times and the lack of DOA resolution ability of the individual sensors, DOA estimation is possible and simple, and asymptotic efficiency becomes closely approximated after a reasonable number of rover snapshots. Key features are the directionality of the rover antenna, the area it surveys, and the average number of sensors inside that area, as accorded a Poisson distribution.

A self-similar natural scene, characterized by a Hurst coefficient HX>1/2, is remotely sensed. The physical process of data acquisition is modeled as a non-linear zero-memory function of the natural scene slope. Do the data collected by the sensor preserve the long-range-dependence feature? If they do, what about the relationship between the Hurst coefficient of the illuminated scene and that of the collected data? This is our first investigation of the issue; as such, we resort to an extremely simplified setup allowing us to highlight some interesting problem features