1.
Rosenstatter, Thomas, et al.
(author)
Modelling the Level of Trust in a Cooperative Automated Vehicle Control System
2018
In: IEEE transactions on intelligent transportation systems (Print). - Piscataway, NJ : IEEE Press. - 1524-9050 .- 1558-0016. ; 19:4, s. 1267-1247
Journal article (peer-reviewed) abstract
Vehicle-to-vehicle communication is a key technology for achieving increased perception for automated vehicles, where the communication enables virtual sensing by means of sensors in other vehicles. In addition, this technology also allows detection and recognition of objects that are out-of-sight. This paper presents a trust system that allows a cooperative and automated vehicle to make more reliable and safe decisions. The system evaluates the current situation and generates a trust index indicating the level of trust in the environment, the ego vehicle, and the surrounding vehicles. This research goes beyond secure communication and concerns the verification of the received data on a system level. The results show that the proposed method is capable of correctly identifying various traffic situations and how the trust index is used while manoeuvring in a platoon merge scenario.
2.
Shafiq, ur Réhman, 1978-, et al.
(author)
Using Vibrotactile Language for Multimodal Human Animals Communication and Interaction
2014
In: Proceedings of the 2014 Workshops on Advances in Computer Entertainment Conference, ACE '14. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450333146 ; , s. 1:1-1:5
Conference paper (peer-reviewed) abstract
In this work we aim to facilitate computer mediated multimodal communication and interaction between human and animal based on vibrotactile stimuli. To study and influence the behavior of animals, usually researchers use 2D/3D visual stimuli. However we use vibrotactile pattern based language which provides the opportunity to communicate and interact with animals. We have performed experiment with a vibrotactile based human-animal multimodal communication system to study the effectiveness of vibratory stimuli applied to the animal skin along with audio and visual stimuli. The preliminary results are encouraging and indicate that low-resolution tactual displays are effective in transmitting information.
3.
Lian, Mengke, et al.
(author)
What Can Machine Learning Teach Us about Communications
2018
In: IEEE International Symposium on Information Theory - Proceedings. - 2157-8095. ; 15 January 2019
Conference paper (peer-reviewed) abstract
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.
4.
Zhang, Kewei, et al.
(author)
Protecting GNSS Open Service-Navigation Message Authentication against Distance-Decreasing Attacks
Other publication (other academic/artistic) abstract
As the security of global navigation satellite systems (GNSS) for civilian usage is increasingly important, navigation message authentication (NMA) significantly improves resilience to spoofing attacks. However, not all attacks can be effectively countered: a strong variant of replay/relay attacks, distance-decreasing (DD) attacks, can shorten pseudorange measurements, without manipulating the cryptographically protected navigation message, thus manipulating the position, velocity, and time solution undetected. First, we discuss how DD attacks can tamper with GNSS signals, demonstrating the attack effectiveness on a recorded Galileo signal. DD attacks might introduce bit errors to the forged signals, but the adversary can keep this error rate very low with proper attack parameter settings. Then, based on our mathematical model of the prompt correlator output of the tracking phase at the victim receiver, we find that the correlator output distribution changes in the presence of DD attacks. This leads us to apply hypothesis testing to detect DD attacks, notably a Goodness of Fit (GoF) test and a generalized likelihood ratio test (GLRT), depending on the victim’s knowledge on the DD attacks. Monte Carlo simulations are used to evaluate the detection probability and the receiver operating characteristic (ROC) curves for two tests, for different adversary configuration and noise settings. Then, we evaluate the effectiveness of the GoF and GLRT tests with a synthesized DD signal. Both tests can detect DD attacks with similar performance in high signal-to-noise ratio (SNR) environments. The GLRT detection probability is approximately 20% higher than that of the GoF test in low SNR environments.
5.
Jiang, W., et al.
(author)
Implementation of an internet-based remote controller with guaranteed exponential stabilization
2008
In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA). - 9781424421145 ; , s. 4063-4068
Conference paper (peer-reviewed) abstract
In this article, an Internet-based remote control system is designed and implemented. The communication is based on the Master-Slave structure. The Master PC communicates with the Slave from about 40km away by UDP protocol. In order to guarantee the Master and Slave clocks to be synchronized, the NTP (Network Time Protocol) is used in both sides. The packets are sent together with time-stamps. The controller design (master) relies on a remote observer that achieves a state prediction of the application (slave), despite the variable communication delays. The Slave comprises a PC and a robot Miabot of Merlin company. The protocol Bluetooth is used between the local PC and the robot. Internet-based remote systems are subject to variable time delays (including communication and data-sampling delays) and data packets losses (due to the unstable Internet network). We have continuously tested the RTT (round-trip-time) between the two PCs in the daytime and nighttime by the protocol ICMP (Internet Control Message). From these tests, an evaluation of the maximal time delay is obtained. Our structure allows one to guarantee an exponential stabilization performance, which is proven via a Lyapunov-Krassovski functional technique and involves the estimated delay upperbound. This means that the guaranteed decay rate is computed (via some LMI optimization) in relation to some maximal value of the communication delays. Of course, for greater delay values, the performance cannot be guaranteed anymore and an alternative solution has to be considered. In our system, we give a command for the robot to stop until the communication comes back to a sufficient quality.
6.
Ivanov, Mikhail, 1986, et al.
(author)
Unequal Error Protection in Coded Slotted ALOHA
2016
In: IEEE Wireless Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2345 .- 2162-2337. ; 5:5, s. 536-539
Journal article (peer-reviewed) abstract
We analyze the performance of coded slotted ALOHA systems for a scenario where users have different error protection requirements and correspondingly can be divided into user classes. The main goal is to design the system so that the requirements for each class are satisfied. To that end, we derive analytical error floor approximations of the packet loss rate for each class in the finite frame length regime, as well as the density evolution in the asymptotic case. Based on this analysis, we propose a heuristic approach for the optimization of the degree distributions to provide the required unequal error protection. In addition, we analyze the decoding delay for users in different classes and show that better protected users experience a smaller average decoding delay.
7.
Yanggratoke, Rerngvit, 1983-, et al.
(author)
Predicting real-time service-level metrics from device statistics
2015
In: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015. - : Institute of Electrical and Electronics Engineers Inc.. - 9783901882760 ; , s. 414-422
Conference paper (peer-reviewed) abstract
While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service-independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.
8.
Nopchinda, Dhecha, 1991
(author)
mm-Wave Data Transmission and Measurement Techniques: A Holistic Approach
2019
Doctoral thesis (other academic/artistic) abstract
The ever-increasing demand on data services places unprecedented technical requirements on networks capacity. With wireless systems having significant roles in broadband delivery, innovative approaches to their development are imperative. By leveraging new spectral resources available at millimeter-wave (mm-wave) frequencies, future systems can utilize new signal structures and new system architectures in order to achieve long-term sustainable solutions. This thesis proposes the holistic development of efficient and cost-effective techniques and systems which make high-speed data transmission at mm-wave feasible. In this paradigm, system designs, signal processing, and measurement techniques work toward a single goal; to achieve satisfactory system level key performance indicators (KPIs). Two intimately-related objectives are simultaneously addressed: the realization of efficient mm-wave data transmission and the development of measurement techniques to enable and assist the design and evaluation of mm-wave circuits. The standard approach to increase spectral efficiency is to increase the modulation order at the cost of higher transmission power. To improve upon this, a signal structure called spectrally efficient frequency division multiplexing (SEFDM) is utilized. SEFDM adds an additional dimension of continuously tunable spectral efficiency enhancement. Two new variants of SEFDM are implemented and experimentally demonstrated, where both variants are shown to outperform standard signals. A low-cost low-complexity mm-wave transmitter architecture is proposed and experimentally demonstrated. A simple phase retarder predistorter and a frequency multiplier are utilized to successfully generate spectrally efficient mm-wave signals while simultaneously mitigating various issues found in conventional mm-wave systems. A measurement technique to characterize circuits and components under antenna array mutual coupling effects is proposed and demonstrated. With minimal setup requirement, the technique effectively and conveniently maps prescribed transmission scenarios to the measurement environment and offers evaluations of the components in terms of relevant KPIs in addition to conventional metrics. Finally, a technique to estimate transmission and reflection coefficients is proposed and demonstrated. In one variant, the technique enables the coefficients to be estimated using wideband modulated signals, suitable for implementation in measurements performed under real usage scenarios. In another variant, the technique enhances the precision of noisy S-parameter measurements, suitable for characterizations of wideband mm-wave components.
9.
Yanggratoke, Rerngvit, 1983-
(author)
Data-driven Performance Prediction and Resource Allocation for Cloud Services
2016
Doctoral thesis (other academic/artistic) abstract
Cloud services, which provide online entertainment, enterprise resource management, tax filing, etc., are becoming essential for consumers, businesses, and governments. The key functionalities of such services are provided by backend systems in data centers. This thesis focuses on three fundamental problems related to management of backend systems. We address these problems using data-driven approaches: triggering dynamic allocation by changes in the environment, obtaining configuration parameters from measurements, and learning from observations. The first problem relates to resource allocation for large clouds with potentially hundreds of thousands of machines and services. We developed and evaluated a generic gossip protocol for distributed resource allocation. Extensive simulation studies suggest that the quality of the allocation is independent of the system size for the management objectives considered.The second problem focuses on performance modeling of a distributed key-value store, and we study specifically the Spotify backend for streaming music. We developed analytical models for system capacity under different data allocation policies and for response time distribution. We evaluated the models by comparing model predictions with measurements from our lab testbed and from the Spotify operational environment. We found the prediction error to be below 12% for all investigated scenarios.The third problem relates to real-time prediction of service metrics, which we address through statistical learning. Service metrics are learned from observing device and network statistics. We performed experiments on a server cluster running video streaming and key-value store services. We showed that feature set reduction significantly improves the prediction accuracy, while simultaneously reducing model computation time. Finally, we designed and implemented a real-time analytics engine, which produces model predictions through online learning.
10.
Homem, Irvin, 1985-
(author)
Advancing Automation in Digital Forensic Investigations
2018
Doctoral thesis (other academic/artistic) abstract
Digital Forensics is used to aid traditional preventive security mechanisms when they fail to curtail sophisticated and stealthy cybercrime events. The Digital Forensic Investigation process is largely manual in nature, or at best quasi-automated, requiring a highly skilled labour force and involving a sizeable time investment. Industry standard tools are evidence-centric, automate only a few precursory tasks (E.g. Parsing and Indexing) and have limited capabilities of integration from multiple evidence sources. Furthermore, these tools are always human-driven.These challenges are exacerbated in the increasingly computerized and highly networked environment of today. Volumes of digital evidence to be collected and analyzed have increased, and so has the diversity of digital evidence sources involved in a typical case. This further handicaps digital forensics practitioners, labs and law enforcement agencies, causing delays in investigations and legal systems due to backlogs of cases. Improved efficiency of the digital investigation process is needed, in terms of increasing the speed and reducing the human effort expended. This study aims at achieving this time and effort reduction, by advancing automation within the digital forensic investigation process.Using a Design Science research approach, artifacts are designed and developed to address these practical problems. Summarily, the requirements, and architecture of a system for automating digital investigations in highly networked environments are designed. The architecture initially focuses on automation of the identification and acquisition of digital evidence, while later versions focus on full automation and self-organization of devices for all phases of the digital investigation process. Part of the remote evidence acquisition capability of this system architecture is implemented as a proof of concept. The speed and reliability of capturing digital evidence from remote mobile devices over a client-server paradigm is evaluated. A method for the uniform representation and integration of multiple diverse evidence sources for enabling automated correlation, simple reasoning and querying is developed and tested. This method is aimed at automating the analysis phase of digital investigations. Machine Learning (ML)-based triage methods are developed and tested to evaluate the feasibility and performance of using such techniques to automate the identification of priority digital evidence fragments. Models from these ML methods are evaluated in identifying network protocols within DNS tunneled network traffic. A large dataset is also created for future research in ML-based triage for identifying suspicious processes for memory forensics.From an ex ante evaluation, the designed system architecture enables individual devices to participate in the entire digital investigation process, contributing their processing power towards alleviating the burden on the human analyst. Experiments show that remote evidence acquisition of mobile devices over networks is feasible, however a single-TCP-connection paradigm scales poorly. A proof of concept experiment demonstrates the viability of the automated integration, correlation and reasoning over multiple diverse evidence sources using semantic web technologies. Experimentation also shows that ML-based triage methods can enable prioritization of certain digital evidence sources, for acquisition or analysis, with up to 95% accuracy.The artifacts developed in this study provide concrete ways to enhance automation in the digital forensic investigation process to increase the investigation speed and reduce the amount of costly human intervention needed.
