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This article presents a comprehensive survey of the state-of-the-art for vehicle ad hoc networks. We start by reviewing the possible applications that can be used in VANETs, namely, safety and user applications, and by identifying their requirements. Then, we classify the solutions proposed in the literature according to their location in the open system interconnection reference model and their relationship to safety or user applications. We analyze their advantages and shortcomings and provide our suggestions for a better approach. We also describe the different methods used to simulate and evaluate the proposed solutions. Finally, we conclude with suggestions for a general architecture that can form the basis for a practical VANET.

Traditional power grids are being transformed into Smart Grids (SGs) to\naddress the issues in existing power system due to uni-directional information\nflow, energy wastage, growing energy demand, reliability and security. SGs\noffer bi-directional energy flow between service providers and consumers,\ninvolving power generation, transmission, distribution and utilization systems.\nSGs employ various devices for the monitoring, analysis and control of the\ngrid, deployed at power plants, distribution centers and in consumers' premises\nin a very large number. Hence, an SG requires connectivity, automation and the\ntracking of such devices. This is achieved with the help of Internet of Things\n(IoT). IoT helps SG systems to support various network functions throughout the\ngeneration, transmission, distribution and consumption of energy by\nincorporating IoT devices (such as sensors, actuators and smart meters), as\nwell as by providing the connectivity, automation and tracking for such\ndevices. In this paper, we provide a comprehensive survey on IoT-aided SG\nsystems, which includes the existing architectures, applications and prototypes\nof IoT-aided SG systems. This survey also highlights the open issues,\nchallenges and future research directions for IoT-aided SG systems.\n

With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.

Recently the problem of determining the best, in the least-squares sense, rank-1 approximation to a higher-order tensor was studied and an iterative method that extends the well-known power method for matrices was proposed for its solution. This higher-order power method is also proposed for the special but important class of supersymmetric tensors, with no change. A simplified version, adapted to the special structure of the supersymmetric problem, is deemed unreliable, as its convergence is not guaranteed. The aim of this paper is to show that a symmetric version of the above method converges under assumptions of convexity (or concavity) for the functional induced by the tensor in question, assumptions that are very often satisfied in practical applications. The use of this version entails significant savings in computational complexity as compared to the unconstrained higher-order power method. Furthermore, a novel method for initializing the iterative process is developed which has been observed to yield an estimate that lies closer to the global optimum than the initialization suggested before. Moreover, its proximity to the global optimum is a priori quantifiable. In the course of the analysis, some important properties that the supersymmetry of a tensor implies for its square matrix unfolding are also studied.

The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.

Information-centric networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, named data networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is intelligent transportation system realized through vehicular ad hoc network (VANET) where vehicles exchange information and content with each other and with the infrastructure. Up to date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community.

Kombucha, historically an Asian tea-based fermented drink, has recently become trendy in Western countries. Producers claim it bears health-enhancing properties that may come from the tea or metabolites produced by its microbiome. Despite its long history of production, microbial richness and dynamics have not been fully unraveled, especially at an industrial scale. Moreover, the impact of tea type (green or black) on microbial ecology was not studied. Here, we compared microbial communities from industrial-scale black and green tea fermentations, still traditionally carried out by a microbial biofilm, using culture-dependent and metabarcoding approaches. Dominant bacterial species belonged to Acetobacteraceae and to a lesser extent Lactobacteriaceae, while the main identified yeasts corresponded to Dekkera, Hanseniaspora and Zygosaccharomyces during all fermentations. Species richness decreased over the 8-day fermentation. Among acetic acid bacteria, Gluconacetobacter europaeus, Gluconobacter oxydans, G. saccharivorans and Acetobacter peroxydans emerged as dominant species. The main lactic acid bacteria, Oenococcus oeni, was strongly associated with green tea fermentations. Tea type did not influence yeast community, with Dekkera bruxellensis, D. anomala, Zygosaccharomyces bailii and Hanseniaspora valbyensis as most dominant. This study unraveled a distinctive core microbial community which is essential for fermentation control and could lead to Kombucha quality standardization.

GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection.

A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

This paper examines the asymptotic performance of MUSIC-like algorithms for estimating directions of arrival (DOA) of narrowband complex noncircular sources. Using closed-form expressions of the covariance of the asymptotic distribution of different projection matrices, it provides a unifying framework for investigating the asymptotic performance of arbitrary subspace-based algorithms valid for Gaussian or non-Gaussian and complex circular or noncircular sources. We also derive different robustness properties from the asymptotic covariance of the estimated DOA given by such algorithms. These results are successively applied to four algorithms: to two attractive MUSIC-like algorithms previously introduced in the literature, to an extension of these algorithms, and to an optimally weighted MUSIC algorithm proposed in this paper. Numerical examples illustrate the performance of the studied algorithms compared to the asymptotically minimum variance (AMV) algorithms introduced as benchmarks

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

Vehicular ad hoc networks (VANETs) have attracted a lot of attention in the research community in recent years due to their promising applications. VANETs help improve traffic safety and efficiency. Each vehicle can exchange information to inform other vehicles about the current status of the traffic flow or a dangerous situation such as an accident. Road safety and traffic management applications require a reliable communication scheme with minimal transmission collisions, which thus increase the need for an efficient medium access control (MAC) protocol. However, the design of the MAC in a vehicular network is a challenging task due to the high speed of the nodes, the frequent changes in topology, the lack of an infrastructure, and various QoS requirements. Recently, several time-division multiple-access (TDMA)-based MAC protocols have been proposed for VANETs in an attempt to ensure that all the vehicles have enough time to send safety messages without collisions and to reduce the end-to-end delay and the packet loss ratio. In this paper, we identify the reasons for using the collision-free MAC paradigm in VANETs. We then present a novel topology-based classification, and we provide an overview of TDMA-based MAC protocols that have been proposed for VANETs. We focus on the characteristics of these protocols, as well as on their benefits and limitations. Finally, we give a qualitative comparison, and we discuss some open issues that need to be tackled in future studies in order to improve the performance of TDMA-based MAC protocols for vehicle-to-vehicle communications.

Network visualization is a promising concept to diversify the future Internet architecture into separate virtual networks (VN) that can support simultaneously multiple network experiments, services and architectures over a shared substrate network. To take full advantage of this paradigm this paper addresses the challenge of assigning VNs to the underlying physical network in a distributed and efficient manner. A distributed algorithm responsible for load balancing and mapping virtual nodes and links to substrate nodes and links has been designed, implemented and evaluated. A VN mapping protocol is proposed to communicate and exchange messages between agent-based substrate nodes to achieve the mapping. Results of the implementation and a performance evaluation of the distributed VN mapping algorithm using a multi-agent approach are reported.

Recently, the European Medicines Agency approved the use of the vasopressin V2 receptor antagonist tolvaptan to slow the progression of cyst development and renal insufficiency of autosomal dominant polycystic kidney disease (ADPKD) in adult patients with chronic kidney disease stages 1-3 at initiation of treatment with evidence of rapidly progressing disease. In this paper, on behalf of the ERA-EDTA Working Groups of Inherited Kidney Disorders and European Renal Best Practice, we aim to provide guidance for making the decision as to which ADPKD patients to treat with tolvaptan. The present position statement includes a series of recommendations resulting in a hierarchical decision algorithm that encompasses a sequence of risk-factor assessments in a descending order of reliability. By examining the best-validated markers first, we aim to identify ADPKD patients who have documented rapid disease progression or are likely to have rapid disease progression. We believe that this procedure offers the best opportunity to select patients who are most likely to benefit from tolvaptan, thus improving the benefit-to-risk ratio and cost-effectiveness of this treatment. It is important to emphasize that the decision to initiate treatment requires the consideration of many factors besides eligibility, such as contraindications, potential adverse events, as well as patient motivation and lifestyle factors, and requires shared decision-making with the patient.

In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. Gathering and analyzing these large-scale real-world digital traces have provided us an unprecedented opportunity to understand the city dynamics and reveal the hidden social and economic “realities”. One innovative pervasive application is to provide correct driving strategies to taxi drivers according to time and location. In this paper, we aim to discover both efficient and inefficient passenger-finding strategies from a large-scale taxi GPS dataset, which was collected from 5350 taxis for one year in a large city of China. By representing the passenger-finding strategies in a Time-Location-Strategy feature triplet and constructing a train/test dataset containing both top- and ordinary-performance taxi features, we adopt a powerful feature selection tool, L1-Norm SVM, to select the most salient feature patterns determining the taxi performance. We find that the selected patterns can well interpret the empirical study results derived from raw data analysis and even reveal interesting hidden “facts”. Moreover, the taxi performance predictor built on the selected features can achieve a prediction accuracy of 85.3% on a new test dataset, and it also outperforms the one based on all the features, which implies that the selected features are indeed the right indicators of the passenger-finding strategies.

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes, using algorithms such as nonnegative matrix factorization (NMF). Prior training of the basis spectra is often infeasible due to the wide range of possible musical instruments. Appropriate spectra must then be adaptively estimated from the data, which may result in limited performance due to overfitting issues. In this paper, we model each basis spectrum as a weighted sum of narrowband spectra representing a few adjacent harmonic partials, thus enforcing harmonicity and spectral smoothness while adapting the spectral envelope to each instrument. We derive a NMF-like algorithm to estimate the model parameters and evaluate it on a database of piano recordings, considering several choices for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.

Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.

Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.