This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation. © 2013 IEEE.

The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches need thus to incorporate specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an Eigen-patches reconstruction method based on PCA Eigentransformation of local image patches. The structure of the iris is exploited by building a patch-position dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators, which were used to carry out biometric verification and identification experiments. Experimental results show that the proposed method significantly outperforms both bilinear and bicubic interpolation at very low-resolution. The performance of a number of comparators attain an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching. © 2018, Emerald Publishing Limited.

This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods.

In recent years, the use of remote operation has been proposed as a bridge towards driverless mobility by providing human assistance remotely when an automated driving system finds a situation that is ambiguous and requires input from a remote operator. The remote operation of road vehicles has also been proposed as a way to enable drivers to operate vehicles from safer and more comfortable locations. While commercial solutions for remote operation exist, remaining challenges are being tackled by the research community, who is continuously testing and validating the feasibility of deploying remote operation of road vehicles on public roads. These tests range from the technological scope to social aspects such as acceptability and usability that affect human performance. This survey presents a compilation of works that approach the remote operation of road vehicles. We start by describing the basic architecture of remote operation systems and classify their modes of operation depending on the level of human intervention. We use this classification to organize and present recent and relevant work on the field from industry and academia. Finally, we identify the challenges in the deployment of remote operation systems in the technological, regulatory, and commercial scopes.

Exchanging status information between closely located mobile agents is an underlying process in numerous future Cyber Physical Systems (CPS). Real-time updates including positions of neighboring nodes is performed when, for example, autonomous vehicles execute a cooperative maneuver, industrial robots collaborate with each other on a task, or Unmanned Aerial Vehicles (UAVs) execute a mission in a swarm. For the design of networked automatic control strategies in these scenarios, it is essential to understand the performance of such Machine-to-Machine (M2M) communications from the information freshness perspective. To this end, we introduce a mathematical framework which allows characterizing the Age of Information (AoI) in networks governed by the Carrier-Sense Multiple Access (CSMA) protocol. Differently from existing work, we take into account the fact that update packets sent by mobile nodes are not necessarily periodic, since packet triggering is often coupled with agents’ mobility. Our approach is based on the assumption that diverse mobility-triggered message generation patterns can be modeled by a wide class of update traffic arrival processes. We apply Discrete Markovian Arrival Process (DMAP), which is a versatile arrival model able to fit arrival patterns that are modulated by a finite state machine, including bursty traffic. We develop an accurate and efficient analytical model of nodes exchanging one-hop broadcast update messages with bursty arrivals to evaluate the moments as well as entire probability distribution of several performance metrics, including AoI. An asymptotic analysis for large networks suggests a simple way to control the update message rate to minimize the AoI. We show that the optimal update rate that minimizes the mean AoI coincides with the optimum of the wireless channel utilization. Numerical examples point out that the asymptotic theory provides accurate predictions also for small values of the number of nodes. © 2013 IEEE.

In an application-layer distributed denial of service (App-DDoS) attack, zombie computers bring down the victim server with valid requests. Intrusion detection systems (IDS) cannot identify these requests since they have legal forms of standard TCP connections. Researchers have suggested several techniques for detecting App-DDoS traffic. There is, however, no clear distinction between legitimate and attack traffic. In this paper, we go a step further and propose a Machine Learning (ML) solution by combining the Radial Basis Function (RBF) neural network with the cuckoo search algorithm to detect App-DDoS traffic. We begin by collecting training data and cleaning them, then applying data normalizing and finding an optimal subset of features using the Genetic Algorithm (GA). Next, an RBF neural network is trained by the optimal subset of features and the optimizer algorithm of cuckoo search. Finally, we compare our proposed technique to the well-known k-nearest neighbor (k-NN), Bootstrap Aggregation (Bagging), Support Vector Machine (SVM), Multi-layer Perceptron) MLP, and (Recurrent Neural Network) RNN methods. Our technique outperforms previous standard and well-known ML techniques as it has the lowest error rate according to error metrics. Moreover, according to standard performance metrics, the results of the experiments demonstrate that our proposed technique detects App-DDoS traffic more accurately than previous techniques. © 2013 IEEE.

In classification problems, as the number of classes increases, correctly classifying a new instance into one of them is assumed to be more challenging than making the same decision in the presence of fewer classes. The essence of the problem is that using the learning algorithm on each decision boundary individually is better than using the same learning algorithm on several of them simultaneously. However, why and when it happens is still not well-understood today. This work’s main contribution is to introduce the concept of heterogeneity of decision boundaries as an explanation of this phenomenon. Based on the definition of heterogeneity of decision boundaries, we analyze and explain the differences in the performance of state of the art approaches to solve multi-class classification. We demonstrate that as the heterogeneity increases, the performances of all approaches, except one-vs-one, decrease. We show that by correctly encoding the knowledge of the heterogeneity of decision boundaries in a decomposition of the multi-class problem, we can obtain better results than state of the art decompositions. The benefits can be an increase in classification performance or a decrease in the time it takes to train and evaluate the models. We first provide intuitions and illustrate the effects of the heterogeneity of decision boundaries using synthetic datasets and a simplistic classifier. Then, we demonstrate how a real dataset exhibits these same principles, also under realistic learning algorithms. In this setting, we devise a method to quantify the heterogeneity of different decision boundaries, and use it to decompose the multi-class problem. The results show significant improvements over state-of-the-art decompositions that do not take the heterogeneity of decision boundaries into account. © 2013 IEEE.

Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed. Author

Emerging surveillance applications of UAV teams rely on secure communication to exchange information, coordinate their movements, and fulfill mission objectives. Protecting the network by identifying malicious nodes that are trying to disturb the system is an important task, particularly in the military domain. This paper presents the design and evaluation of UAVouch, an identity and location validation scheme that combines a public-key based authentication mechanism with a movement plausibility check for groups of UAVs. The key idea of UAVouch is to supplement the authentication mechanism by periodically checking the plausibility of the locations of neighboring UAVs, allowing the detection of intruders that are unable to follow expected trajectories. The proposed solution was evaluated in a simulated military surveillance scenario in which it detected malicious nodes’ position falsification attacks with an average accuracy of above 85%. © Copyright 2021 IEEE

The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective. © 2013 IEEE

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers’ output for unseen data and evaluate the method’s robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70%and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.

This paper presents a high-performance and energy efficient processor exploiting a Magnetoresistive-based Computing-in-Memory array architecture (so-called MagCiM processor), to perform Boolean logic functions on operands stored in a memory array. The proposed processor efficiently addresses the memory wall and the leakage power consumption problems in conventional processors. The MagCiM processor utilizes mCell memory, a class of Magnetoresistive memory employing only Magnetic Tunnel Junction (MTJ) devices, to realize both computation-in-memory and on-chip instruction and data memories. The mCell memory is characterized by almost zero leakage power, high integration density, high level of reliability, and compatibility with the CMOS VLSI fabrication process. The circuit-level simulation results through comparisons with the previous work reveal that the MagCiM processor provides low occupation area, low power, and energy consumption and offers Normally-off instant-on computing capability, which makes it very suitable for embedded system applications. Based on our evaluations, a conventional processor based on the well-known MIPS architecture consumes about 13 times more energy while having 1.5 times more delay than the MagCiM processor. © 2013 IEEE.

An important use case for Vehicular Ad-hoc Networks (VANETs) is its application in the warning systems of emergency vehicles (EV). VANET-based vehicle-to-infrastructure (V2I) communication can be used to exchange important data and information between traffic lights and EVs, by means of transceivers at both ends. This communication helps in reducing the risks of accidents and also saves valuable time through an optimized orchestration of the traffic lights. This paper outlines the system design of an EV warning system that makes use of V2I communication. The system has been extensively studied in state-of-the-art simulators, such as SUMO and OMNeT++, in a huge variety of scenarios, where metrics for both time and safety have been collected. The results show that SafeSmart is highly effective in reducing trip times as well as increasing the overall safety of EVs in emergency scenarios. © 2013 IEEE.

With the facilitation of the fifth generation (5G) New Radio (NR), Vehicle-to-Everything (V2X) applications have entered a brand new era to sustain the next generation driving use cases of advanced driving, vehicle platooning, extended sensors and remote driving. To deploy these driving use cases, the service requirements however include low latency, high reliability, and high data rates, which thus render utilizing millimeter wave (mmWave) carriers (spectrum above 6 GHz) as a remedy to empower the next generation driving use cases. However, suffering from severe signal attenuation, transmission range of mmWave carriers may be very limited, which is unfavorable in mobile network deployment to offer seamless services, and compel directional transmission/reception using beamforming mandatory. For this purpose, both a transmitter and a receiver should sweep their beams toward different directions over time, and a communication link can be established only if a transmitter and a receiver arrange their beam directions toward each other at the same time (known as beam alignment). Unfortunately, latency of performing beam sweeping to achieve beam alignment turns out the be a dominating challenge to exploit mmWave, especially for the next generation driving use cases. In this paper, we consequently derive essential principles and designs for beam sweeping at the transmitter side and receivers side, which not only guarantee the occurrence of beam alignment but also optimize the latency to achieve beam alignment. Based on the availabilities of a common geographic reference and the knowledge of beam sweeping scheme at the transmitter side, we derive corresponding performance bounds in terms of latency to achieve beam alignment, and device corresponding latency-optimal beam sweeping schemes. The provided engineering insights therefore pave inevitable foundations to practice the next generation driving use cases using mmWave carriers.

Smart vehicles are emerging as a possible solution for multiple concerns in road traffic, such as mobility and safety. This work presents radio localization methods based on simultaneous direction of arrival (DOA), time-delay, and range estimation using the SAGE algorithm. The proposed methods do not rely on external sources of information, such as global navigation satellite systems (GNSS). The proposed methods take advantage of signals of opportunity and do not require the transmission of location-specific signals; therefore, they do not increase the network load. A set of simulations using synthetic and measured data is provided to validate the proposed methods, and the results show that it is possible to achieve accuracy down to decimeter and centimeter-level. © 2013 IEEE.

It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed brain emotional learning-based fuzzy inference system (BELFIS) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on the performance evaluation of BELFIS as a predictor by forecasting solar cycles 16-24. The performance of BELFIS is compared with other computational models used for this purpose, in particular with the adaptive neuro-fuzzy inference system. © 2018 IEEE.

Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties have arisen because RNN requires high computational capability and a large memory space. In this paper, we review existing implementations of RNN models on embedded platforms and discuss the methods adopted to overcome the limitations of embedded systems. We will define the objectives of mapping RNN algorithms on embedded platforms and the challenges facing their realization. Then, we explain the components of RNN models from an implementation perspective. We also discuss the optimizations applied to RNNs to run efficiently on embedded platforms. Finally, we compare the defined objectives with the implementations and highlight some open research questions and aspects currently not addressed for embedded RNNs. Overall, applying algorithmic optimizations to RNN models and decreasing the memory access overhead is vital to obtain high efficiency. To further increase the implementation efficiency, we point up the more promising optimizations that could be applied in future research. Additionally, this article observes that high performance has been targeted by many implementations, while flexibility has, as yet, been attempted less often. Thus, the article provides some guidelines for RNN hardware designers to support flexibility in a better manner. © 2020 IEEE.

Distributed Denial of Service (DDoS) attacks are a growing threat to online services, and various methods have been developed to detect them. However, past research has mainly focused on identifying attack patterns and types, without specifically addressing the role of freely available DDoS attack tools in the escalation of these attacks. This study aims to fill this gap by investigating the impact of the easy availability of DDoS attack tools on the frequency and severity of attacks. In this paper, a machine learning solution to detect DDoS attacks is proposed, which employs a feature selection technique to enhance its speed and efficiency, resulting in a substantial reduction in the feature subset. The provided evaluation metrics demonstrate that the model has a high accuracy level of 99.9%, a precision score of 96%, a recall score of 98%, and an F1 score of 97%. Moreover, the examination found that by utilizing a deliberate approach for feature selection, our model's efficacy was massively raised. © 2013 IEEE.

Cooperative driving is a promising paradigm to improve traffic efficiency and safety. In congested traffic scenarios, such cooperation allows for safe maneuvering and driving with small inter-vehicle spatial gaps. The vehicles involved coordinate their movements in real-time and continuously update each other about their maneuver execution status by means of Vehicle-to-Everything (V2X) communication. However, unreliable V2X communication increases the Age of Information (AoI) of vehicles' status updates, posing a challenge in situations where emergency braking is required during cooperative maneuvering. To address the interplay between unreliable V2X communication and the resulting impact on traffic safety, we introduce a so-called safety time function, specifically designed for cooperative driving use-cases. The safety time function provides the time available for a vehicle to react to an unexpected event of another vehicle - such as emergency braking to avoid a collision. We provide a computationally efficient algorithm for the computation of safety time functions, which allows for efficient and safe cooperative maneuver planning - even in dense traffic scenarios with many vehicles involved. We show the applicability of our proposed safety time function based on the assessed communication quality for IEEE 802.11p-based V2X communication to meet safety constraints in dense vehicular traffic. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

This paper presents a unique antenna that is designed to be efficient, with improved gain and partial flexibility, for use in wearable biomedical telemetry applications. The antenna design utilizes a semi-flexible RO5880 substrate material (dielectric constant, epsilon(r) = 2.2, loss tangent, ( tan delta ) = 0.0009) with physical dimensions measuring 0.47 lambda(g)x 0.47 lambda(g) . The model involves the incorporation of rectangular inverted C slots, which effectively results in a reduction of the resonant frequency. Additionally, a distributed rectangular slot is introduced on the ground plane, contributing to the augmentation of the operational bandwidth. The operational frequency of the proposed antenna design is 2.40 GHz, accompanied by a bandwidth (BW) of 320 MHz at a -10 dB level. This equates to a fractional percentage bandwidth (FBW) of 13.33% centered around the frequency of 2.40 GHz. The antenna design presented in this work demonstrates the preservation of improved gain and efficiency, achieving values of 3.67 dBi and 94%, respectively, at a frequency of 2.40 GHz. The work demonstrates through simulation and experimental outcomes that the antenna exhibits minimal impact on parameters such as gain reflection coefficient (|S-11|) , BW, and bending efficiency. Furthermore, the antenna underwent simulation and experimental testing in close proximity to the human body, revealing favorable operational characteristics. The proposed antenna exhibits substantial potential as a viable option for wearable biomedical instruments. Thus, the proposed wearable antenna design in this study offers a wideband antenna for ISM band applications, expanding bandwidth without compromising performance. Bending the antenna minimally affects gain, bandwidth, and efficiency when worn on the body, making it suitable for wearables. It also maintains a reasonably low Specific Absorption Rate (SAR), reducing wave absorption by the body. Unique features like rectangular inverted C slots and a distributed rectangular slot on the ground plane enhance bandwidth while maintaining performance during bending. © 2013 IEEE.

Increased energy consumption becomes a major issue in 5G cellular networks, which inspires the network operators to deploy renewable energy sources. However, due to the fluctuating nature of renewable energy sources, the energy harvested by base stations (BSs) may not fit for their load conditions. The transmit power of the BS needs to be redesigned again. Hence, this paper considers power control in energy cooperation enabled millimeter wave (mmWave) networks, to alleviate the harvested energy imbalance problem and reduce the energy waste. Each BS is solely powered by renewable energy sources and the harvested energy is allowed to be transferred between BSs. Each BS needs to determine whether the energy should be stored in the battery or transferred to others at each time slot. In this work, power control is formulated as a stochastic optimization problem, aiming at maximizing the time average network utility while keeping the network stable. An online algorithm called Dynamic Energy-aware Power Allocation (DEPA) is proposed based on Lyapunov optimization, which does not need to acquire any statistical knowledge of channels and traffic arrivals. Simulation results show that compared with the power control scheme without energy cooperation, the proposed algorithm with energy cooperation can achieve higher network sum rate while reducing the delay and the required battery capacity.

The smart grid is widely considered as an efficient and intelligent power system. With the aid of communication technologies, the smart grid can enhance the efficiency and reliability of the grid system through intelligent energy management. However, with the development of new energy sources, storage and transmission technologies together with the heterogeneous architecture of the grid network, several new features have been incorporated into the smart grid. These features make the energy trading more complex and pose a significant challenge on designing efficient trading schemes. Based on this motivation, in this paper, we present a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms. We focus on the technologies that address the challenges specific to the new features of the smart grid. Furthermore, we investigate the energy trading in a new cloud-based vehicle-to-vehicle energy exchange scenario. We propose an optimal contract-based electricity trading scheme, which efficiently increases the generated profit. © 2013 IEEE.