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We discuss and extend two approaches to target detection and motion parameter estimation in a very low observable (VLO) underwater multipath environment. The maximum likelihood probabilistic data association (ML-PDA) algorithm considers all possible path–measurement hypotheses, whereas the maximum likelihood probabilistic multiple hypothesis tracker (ML-PMHT) assumes independence among the returns’ origins leading to a simpler likelihood formulation. In particular, we present a recursive form of the multipath ML-PDA algorithm for improved computational efficiency and a generative multipath ML-PMHT algorithm that accounts for the prior probability distribution of the number of received measurements. By simulation of an active sonar tracking scenario, we show that while the two algorithms perform similarly for few returns, the added fidelity of the ML-PDA outperforms the ML-PMHT in parameter estimation as returns accumulate over multiple scans and dimensions. However, the computational cost of the ML-PDA discourages its use beyond simple scenarios. This cost, coupled with the comparable detection performance of the two algorithms, suggests that the simpler ML-PMHT is the better choice for practical use. We also show how both the ML-PDA and ML-PMHT can be applied to realistic ocean environments using ocean acoustic modeling software. This article is an extension of previous work on comparing the standard versions of the ML-PDA and ML-PMHT algorithms. [ABSTRACT FROM AUTHOR]

Power electronics (PE) and high-frequency switching circuits are key to superior performance of electric vehicles. It is vital to monitor the condition of the PE components in real-time for safety and reliability. In this article, we propose two anomaly detection methods based on a combination of data preprocessing to suppress noise and outliers, multivariate regression models to predict signals of interest under nominal operation, and sequential analysis of residuals. In particular, the methods utilize median filtering to extract on-state medians in each switching cycle in nonlinear autoregressive exogenous neural network models or filtered on-state data in partial least squares-based models to represent the nominal circuit behavior. Optimal and approximate dynamic programming-based feature selection methods are developed to select the most informative signals or their transformations. Predictions from the learned models are used to generate the residuals for anomaly detection by Page’s cumulative sum test. The proposed models and anomaly detection methods are validated on three accelerated aging experimental datasets, comprised of 60 power mosfet devices with low-frequency and high-frequency switching under disparate operating conditions. Due to the simplicity and efficiency of the data-driven anomaly detection schemes, the proposed methods can potentially be embedded in real-time digital platforms. [ABSTRACT FROM AUTHOR]

Invoice financing has been a steadily growing component of the financing market as a whole for the last few years, and, in 2016, it became the third largest financing market. Nonetheless, the risk of frauds is still very high, and most solutions proposed so far are based on private, proprietary platforms that cannot match the global nature of such a market. Even the most recent proposals based on blockchain are mainly adopting a private, permissioned blockchain due to the lack of confidentiality in public blockchain. In this article, we propose an Invoice financing platform based on a public blockchain supporting both fully open and group-restricted auctioning of invoices. We addressed the confidentiality issue by storing the confidential data encrypted in IPFS and the corresponding hash in the smart contract hosted on Ethereum blockchain. Our blockchain-based solution ensures data confidentiality and benefits from the main properties of the public blockchain required in Invoice financing systems, such as transparency, immutability, trustworthiness, and security. Furthermore, our platform introduces a reputation system based on the past behavior of entities, computed using the blockchain global ledger. Such a reputation system allows insurance companies to modulate the cost of the insurance contracts they offer. This combination guarantees the complete transparency and tamperproofness of a public blockchain, while it allows reducing insurance costs and fraud possibilities. [ABSTRACT FROM AUTHOR]

This paper considers measurement extraction for two closely-spaced objects with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accuracy given by the Cramér Rao lower bound (CRLB). The second part of the measurement extractor, target detector, selects among the hypotheses of two resolved targets and a single one using information-theoretic criteria and hypothesis tests. Simulation results have been conducted to evaluate the measurement extraction performance including the probability of resolving the two hypotheses, and the efficiency and unbiasedness of the target location estimates for the selected hypothesis using different hypothesis detection schemes. The generalized likelihood ratio test (GLRT) based on linearized observation model using second order Taylor series expansion is most appealing as it provides an explicit expression of the probability of detecting two targets as a function of the target separations, the signal-to-noise ratio at a given false resolution probability. It is shown that the simulation-based resolution performance for the GLRT using the estimated center location of the two targets matches well with the analytic performance assuming known center. [ABSTRACT FROM AUTHOR]

A novel pairwise comparison algorithm that determines whether a pair of ellipsoids is overlapping, touching, or separated by exploiting two new Mahalanobis distance conditions is presented. These conditions are filtering steps prior to a previously proposed method that transforms the intersection query problem into a least squares minimization over a sphere. The algorithm introduced here is shown to be more computationally efficient than previous methods, which either solve the least squares minimization over a sphere or check the signs of the roots of a fourth order polynomial. The proposed method may be applied in cases when an ellipsoid is an appropriate safety region. The resulting algorithm is useful in applications including, but not limited to, collision detection and avoidance. [ABSTRACT FROM AUTHOR]

We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein–Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system. [ABSTRACT FROM AUTHOR]

In the last years, several academic research efforts have focused on security requirements, threat models, and attack taxonomies concerning the application of the Internet of Things (IoT) in critical systems. Since such systems are strongly data intensive, it is of pivotal importance to provide integrity for the messages moving throughout the IoT infrastructure by means of publish/subscribe services. Integrity provisioning in industrial IoT scenarios has received marginal attention with respect to other primary security features. The existing solutions are lacking the needed focus on the peculiarities of the event notification and on the demand introduced by resource-constrained devices. This work contributes by applying group signatures so as to avoid managing certificates, violating the spatial decoupling, or implying an excessive resource usage. A proof-of-concept prototype of the proposed solution has been realized for platforms based on TinyOS, and simulations with TOSSIM have been conducted in order to empirically assess its performance and effectiveness. [ABSTRACT FROM AUTHOR]

Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy. [ABSTRACT FROM PUBLISHER]

In modern optical networks, infrastructure management is faced with the challenge of using expensive equipment and communication resources as efficiently as possible. This now includes keeping power consumption costs at a minimum and using the available optical links in a balanced way, in addition to the traditional goals of providing the best possible performance to the end customers while meeting their quality requirements. Accordingly, this paper presents a heuristic single-step lightpath routing and wavelength assignment algorithm, handling online dynamic connection requests within a fully distributed network control plane. By using shortest path routing, the presented scheme determines the best compromise solution between the users' and carrier's objectives. The former can be mainly expressed in terms of connection QoS requirements, while the latter comprises network engineering (distributing the load in order to achieve near-optimum resource usage) and containing energy consumption. This approach is able to find, in a polynomial computing time, a multiobjective optimization solution that maximizes the carriers' return of investment and supports a high number of users' request while drastically reducing the network operational expenditures, as extensively demonstrated through a simulation. [ABSTRACT FROM PUBLISHER]

Currently, our society is undergoing a radical change due to the increasing pervasive use of ICT within all of its processes. Such an evolution has been triggered by the advent of the Internet of Things vision, where smart sensing devices can be integrated in the daily objects surrounding any human being. The increasing demand of dealing with the big data generated, managed, and stored by the applications built on top of such novel sensory networks has called for innovative architectures, such as edge and fog computing, which have not yet been fully standardized. Considering the large scale and complexity of these architectures, testing a software artifact to be deployed in the nodes composing an edge-fog ecosystem could be extremely challenging. Pure simulated environments and real testbeds could be not representative enough of realistic scenarios or unacceptably expensive. We explore such issues and present a pseudo-dynamic testing approach, where a portion of the experimental scenario is simulated, while the edge and fog nodes under test are emulated or executed in a real environment. [ABSTRACT FROM PUBLISHER]

One regularity condition for the classical Cramér–Rao lower bound (CRLB) of an unbiased estimator to hold—that the support of the likelihood function (LF) should be independent of the parameter to be estimated—has recently been relaxed to the case of parameter-dependent support as long as the LF is continuous at the boundary of its support. For the case where the LF is not continuous on the boundary of its support, a new modified CRLB—designated the Cramér–Rao–Leibniz lower bound (CRLLB) as it relies on the Leibniz integral rule—has also been presented for the scalar parameter case. The present work derives the multidimensional CRLLB for the case of LF with parameter-dependent support by applying the general Leibniz integral rule to complete the framework of the CRLLB. [ABSTRACT FROM PUBLISHER]

Currently, cloud computing is the target business environment for many enterprises and government organizations. However, despite the huge potential gains that can be achieved, security represents a fundamental issue, which prevents the massive cloud adoption in mission-critical Information Technology sectors. The most common security issues are amplified in the cloud environment since new complex features, with their inherent weaknesses, enter into the problem space, particularly those associated to multitenancy and elasticity. Thus, new threats, such as the energy-related denial-of-service attacks against large-scale cloud infrastructures, may involve not only the quality of the delivered services but also their operational costs in terms of energy bill. The longer is the time necessary to identify such attacks, the heavier is the impact on the overall energy consumption and, consequently, on the associated expenses. This work presents a detailed analysis of such new sophisticated menaces, by focusing on those that are specifically tailored to originate the worst-case energy demands by leveraging properly crafted low-rate traffic patterns in order to ensure stealth operations. We present some strategies exploiting the cloud flexibility in order to increase in a fraudulent way the overall energy consumption and analyze their impact within large-scale cloud infrastructures. This should help cloud providers in understanding such weaknesses and highlighting their root causes, as well as in providing some hints on how they can counter these subtle security issues. [ABSTRACT FROM PUBLISHER]