Driver distraction could result in safety compromises attributable to distractions from in-vehicle equipment usage [1]. The effective design of driver-vehicle interfaces (DVIs) and other human-machine interfaces (HMIs) together with their usability, and accessibility while driving become important [2]. Driving distractions can be classified as: visual distractions (any activity that takes your eyes away from the road), cognitive distraction (any activity that takes your mind away from the course of driving), and manual distractions (any activity that takes your hands away from the steering wheel [2]). Besides, multitasking during driving is a distractive activity that can increase the risks of vehicular accidents. To study the driver’s behaviors on the safety of transportation system, using an in-vehicle driver notification application, we examined the effects of increasing driver distraction levels on the evaluation metrics of traffic efficiency and safety by using two types of driver models: young drivers (ages 16-25 years) and middle-age drivers (ages 30-45 years). Our evaluation data demonstrates that as a drivers distraction level is increased, less heed is given to change route directives from the in-vehicle on-board unit (OBU) using textual, visual, audio, and haptic notifications. Interestingly, middle-age drivers proved more effective/resilient in mitigating the negative effects of driver distraction over young drivers [2].
An intelligent transportation system (ITS) is one typical cyber-physical system (CPS) that aims to provide efficient,
effective, reliable, and safe driving experiences with minimal congestion and effective traffic flow management. In order
to achieve these goals, various ITS technologies need to work synergistically. Nonetheless, ITS’s reliance on wireless
connectivity makes it vulnerable to cyber threats. Thus, it is critical to understand the impact of cyber threats on ITS. In
this paper, using real-world transportation dataset, we evaluated the consequences of cyber threats – attacks against service
availability by jamming the communication channel of ITS. In this way, we can have a better understanding of the
importance of ensuring adequate security respecting safety and life-critical ITS applications before full and expensive real-world
deployments. Our experimental data shows that cyber threats against service availability could adversely affect
traffic efficiency and safety performances evidenced by exacerbated travel time, fuel consumed, and other evaluated
performance metrics as the communication network is compromised. Finally, we discuss a framework to make ITS secure
and more resilient against cyber threats.
Accurate and timely knowledge is critical in intelligent transportation system (ITS) as it leads to improved traffic flow
management. The knowledge of the past can be useful for the future as traffic patterns normally follow a predictable pattern
with respect to time of day, and day of week. In this paper, we systematically evaluated the prediction accuracy and speed
of several supervised machine learning algorithms towards congestion identification based on six weeks real-world traffic
data from August 1st, 2012 to September 12th, 2012 in the Maryland (MD)/Washington DC, and Virginia (VA) area. Our
dataset consists of six months traffic data pattern from July 1, 2012 to December 31, 2012, of which 6 weeks was used as
a representative sample for the purposes of this study on our reference roadway – I-270. Our experimental data shows
that with respect to classification, classification tree (Ctree) could provide the best prediction accuracy with an accuracy
rate of 100% and prediction speed of 0.34 seconds. It is pertinent to note that variations exist respecting prediction accuracy
and prediction speed; hence, a tradeoff is often necessary respecting the priority of the applications in question. It is also
imperative to note from the outset that, algorithm design and calibration are important factors in determining their
effectiveness.
Most enterprise networks are built to operate in a static configuration (e.g., static software stacks, network configurations, and application deployments). Nonetheless, static systems make it easy for a cyber adversary to plan and launch successful attacks. To address static vulnerability, moving target defense (MTD) has been proposed to increase the difficulty for the adversary to launch successful attacks. In this paper, we first present a literature review of existing MTD techniques. We then propose a generic defense framework, which can provision an incentive-compatible MTD mechanism through dynamically migrating server locations. We also present a user-server mapping mechanism, which not only improves system resiliency, but also ensures network performance. We demonstrate a MTD with a multi-user network communication and our data shows that the proposed framework can effectively improve the resiliency and agility of the system while achieving good network timeliness and throughput performance.
To date, Unmanned Aerial Vehicles (UAVs) have been widely used for numerous applications. UAVs can directly connect to ground stations or satellites to transfer data. Multiple UAVs can communicate and cooperate with each other and then construct an ad-hoc network. Multi-UAV systems have the potential to provide reliable and timely services for end users in addition to satellite networks. In this paper, we conduct a simulation study for evaluating the network performance of multi-UAV systems and satellite networks using the ns-2 networking simulation tool. Our simulation results show that UAV communication networks can achieve better network performance than satellite networks and with a lower cost and increased timeliness. We also investigate security resiliency of UAV networks. As a case study, we simulate false data injection attacks against UAV communication networks in ns-2 and demonstrate the impact of false data injection attacks on network performance.
Intelligent transportation system (ITS) applications are expected to provide a more efficient, effective, reliable, and
safe driving experience, which can minimize road traffic congestion resulting in a better traffic flow management. To
efficiently manage traffic flows, in this paper, we compare the effectiveness of two well-known vehicle routing
algorithms: the Dijkstra's shortest path algorithm and the A* (Astar) algorithm in terms of the total travel time and the
travel distance. To this end, we built a generic ITS test-bed and created several real-world driving scenarios using field
and simulation data to evaluate the performance of these two routing algorithms. The dataset used in our simulation is six
weeks traffic volume data from 08/01/2012 to 09/27/2012 in the Maryland (MD)/Washington DC and Virginia (VA)
area. Our simulation data shows that an increase in network size results in scalability problems as the efficiency and
effectiveness of these algorithms diminishes in larger road networks with greater traffic volume densities, flow rates, and
congested conditions. In addition, the imprecision of the road network increases as the network size and the traffic
volume density increases. Our study shows that the ability of these vehicular routing algorithms to adaptively route
traffic depends on the size and type of road networks, and the current roadway conditions.
Networking technologies are exponentially increasing to meet worldwide communication requirements. The rapid
growth of network technologies and perversity of communications pose serious security issues. In this paper, we aim to
developing an integrated network defense system with situation awareness capabilities to present the useful information
for human analysts. In particular, we implement a prototypical system that includes both the distributed passive and active
network sensors and traffic visualization features, such as 1D, 2D and 3D based network traffic displays. To effectively
detect attacks, we also implement algorithms to transform real-world data of IP addresses into images and study the pattern
of attacks and use both the discrete wavelet transform (DWT) based scheme and the statistical based scheme to detect
attacks. Through an extensive simulation study, our data validate the effectiveness of our implemented defense system.
KEYWORDS: Satellites, Satellite communications, Data transmission, Data communications, Bismuth, Silicon, Space operations, Analytical research, Video, Algorithm development
For worldwide, a satellite communication network is an integral component of the global networking infrastructure. In
this paper, we focus on developing effective routing techniques that consider both user preferences and network dynamic
conditions. In particular, we develop a weighted-based route selection scheme for the core satellite communication network.
Unlike the shortest path routing scheme, our scheme chooses the route from multiple matched entries based on the
assigned weights that reflect the dynamic condition of networks. We also discuss how to derive the optimal weights for
route assignment. To further meet user’s preference, we implement the multiple path routing scheme to achieve the high
rate of data transmission and the preemption based routing scheme to guarantee the data transmission for high priority
users. Through extensive simulation studies, our data validates the effectiveness of our proposed routing schemes.
Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities
with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring,
detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling
network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness
on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework,
we also implement the statistical-based detection and sequential testing-based detection techniques and compare their
respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed
framework for combating cyber attacks.
In this paper, we summarize our efforts of using three different radars (impulse radar, swept frequency radar,
and continuous-wave radar) for through-the-wall sensing. The purpose is to understand the pros and cons of each
of the three radars. Through extensive experiments, it was found that the radars are complementary and multiple
radars are needed for different scenarios of through-the-wall target detection and tracking.
In this paper, human motion model and RCS (radar cross section) simulation of radar returns from human are investigated.
Micro-Doppler signatures [1-6] induced by human motions are studied. It shows that the time-frequency representation
of micro-Doppler signature provides distinctive time-varying features for human motions. Motion of different body part
has different micro-Doppler signature. Thus, micro-Doppler can be a promising method for classifying human activities.
Measurement data using an experimental X-band micro-Doppler radar were collected and the results are compared with
the corresponding simulation results. The classification of human motions based on micro-Doppler signatures is also discussed.
In this paper, we present a fast block time-recursive algorithm for the computation of short-time Fourier transform, and apply the algorithm to inverse SAR image processing. Simulation results are given and the computational complexity is discussed. The block time- recursive algorithm provides a fast vehicle for processing ISAR images on a real-time basis.
De8cription of boundary curves (shapes) is an important problem in image processing and pattern recognition. During the last two decades there have been a variety of approaches to the problem. Among these approaches the Fourier description (FD) techniques seem to be the most promising in extracting the features of an object. But the problem of the FD techniques that have been practised is the difficulty in describing local information. A modified FD technique is suggested in this paper which use a combined I requencyposition space as the shape descriptor domain. This new set of position-dependent Fourier descriptors provides the best spectral information along the boundary curve. Experimental results are presented in this paper. 1.
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