Street Smart in 5G : Vehicular Applications, Communication, and Computing

Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.Peer reviewe

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe

Chasing the identification of ASCA Galactic Objects (ChIcAGO): An X-ray survey of unidentified sources in the galactic plane. I : Source sample and initial results

We present the Chasing the Identification of ASCA Galactic Objects (ChIcAGO) survey, which is designed to identify the unknown X-ray sources discovered during the ASCA Galactic Plane Survey (AGPS). Little is known about most of the AGPS sources, especially those that emit primarily in hard X-rays (2-10 keV) within the Fx 10-13 to 10-11 erg cm -2 s-1 X-ray flux range. In ChIcAGO, the subarcsecond localization capabilities of Chandra have been combined with a detailed multiwavelength follow-up program, with the ultimate goal of classifying the >100 unidentified sources in the AGPS. Overall to date, 93 unidentified AGPS sources have been observed with Chandra as part of the ChIcAGO survey. A total of 253 X-ray point sources have been detected in these Chandra observations within 3′ of the original ASCA positions. We have identified infrared and optical counterparts to the majority of these sources, using both new observations and catalogs from existing Galactic plane surveys. X-ray and infrared population statistics for the X-ray point sources detected in the Chandra observations reveal that the primary populations of Galactic plane X-ray sources that emit in the Fx 10-13 to 10-11 erg cm -2 s-1 flux range are active stellar coronae, massive stars with strong stellar winds that are possibly in colliding wind binaries, X-ray binaries, and magnetars. There is also another primary population that is still unidentified but, on the basis of its X-ray and infrared properties, likely comprises partly Galactic sources and partly active galactic nuclei.Peer reviewedSubmitted Versio

The Search for Anisotropy in the Arrival Directions of Ultra-High Energy Cosmic Rays Observed by the High Resolution Fly's Eye Detector in Monocular Mode

The High Resolution Fly's Eye HiRes-I detector has now been in operation in monocular mode for over six years. During that time span, HiRes-I has accumulated a larger exposure to Ultra-High Energy Cosmic Rays (UHECRs) above 10^19 eV than any other experiment built to date. This presents an unprecedented opportunity to search for anisotropy in the arrival directions of UHECRs. We present results of a search for dipole distributions oriented towards major astrophysical landmarks and a search for small-scale clustering. We conclude that the HiRes-I data set is, in fact, consistent with an isotropic source model.Comment: 6 pages, 5 figures; to appear in the proceedings of CRIS 2004, Catania, Italy, 31 May - 4 June 2004 (Nuclear Phys. B