Lessons learned from the design of a mobile multimedia system in the Moby Dick project

Recent advances in wireless networking technology and the exponential development of semiconductor technology have engendered a new paradigm of computing, called personal mobile computing or ubiquitous computing. This offers a vision of the future with a much richer and more exciting set of architecture research challenges than extrapolations of the current desktop architectures. In particular, these devices will have limited battery resources, will handle diverse data types, and will operate in environments that are insecure, dynamic and which vary significantly in time and location. The research performed in the MOBY DICK project is about designing such a mobile multimedia system. This paper discusses the approach made in the MOBY DICK project to solve some of these problems, discusses its contributions, and accesses what was learned from the project

Network Latency in Teleoperation of Connected and Autonomous Vehicles:A Review of Trends, Challenges, and Mitigation Strategies

With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs

Dense Moving Fog for Intelligent IoT: Key Challenges and Opportunities

As the ratification of 5G New Radio technology is being completed, enabling network architectures are expected to undertake a matching effort. Conventional cloud and edge computing paradigms may thus become insufficient in supporting the increasingly stringent operating requirements of \emph{intelligent~Internet-of-Things (IoT) devices} that can move unpredictably and at high speeds. Complementing these, the concept of fog emerges to deploy cooperative cloud-like functions in the immediate vicinity of various moving devices, such as connected and autonomous vehicles, on the road and in the air. Envisioning gradual evolution of these infrastructures toward the increasingly denser geographical distribution of fog functionality, we in this work put forward the vision of dense moving fog for intelligent IoT applications. To this aim, we review the recent powerful enablers, outline the main challenges and opportunities, and corroborate the performance benefits of collaborative dense fog operation in a characteristic use case featuring a connected fleet of autonomous vehicles.Comment: 7 pages, 5 figures, 1 table. The work has been accepted for publication in IEEE Communications Magazine, 2019. Copyright may be transferred without notice, after which this version may no longer be accessibl

Priority Preemption for Real-time Application QoS Guarantees in Cooperative Vehicular Networks

Inter-vehicle and roadside-to-vehicle communications can contribute to a safer and more efficient driving experience by providing time-sensitive and location-aware information. However, its performance suffers from vehicle mobility, intermittent user connectivity, and wireless channel unreliability. In this paper, we propose a novel cross-layer optimization approach based on our Adaptive Distributed Cooperative Medium Access Control (ADC-MAC) protocol to guarantee the quality-of-service (QoS) of real-time applications. Markov chain based theoretical analysis show that our proposed priority preemption approach can improve the quality of a real-time application by guaranteeing its bandwidth and reducing its transmission latency

A deep tech architecture for intelligent IoT systems

[EN]The increase in the number of connected devices on the Internet of Things (IoT), interactions and the amount of data raises a number of issues. Two major problems are limitations in terms of network latency and bandwidth. While cloud-based infrastructures give us access to scalable, on-demand storage and processing services that can scale to the requirements of the Internet of Things (IoT), these centralized resources can create unacceptable delays and performance problems for devices that have latency-sensitive applications, such as health monitoring and emergency response applications. This article has been created for the PhD thesis that aims to create a deep tech architecture for intelligent IoT systems

Millimeter-wave Communication and Radar Sensing — Opportunities, Challenges, and Solutions

With the development of communication and radar sensing technology, people are able to seek for a more convenient life and better experiences. The fifth generation (5G) mobile network provides high speed communication and internet services with a data rate up to several gigabit per second (Gbps). In addition, 5G offers great opportunities of emerging applications, for example, manufacture automation with the help of precise wireless sensing. For future communication and sensing systems, increasing capacity and accuracy is desired, which can be realized at millimeter-wave spectrum from 30 GHz to 300 GHz with several tens of GHz available bandwidth. Wavelength reduces at higher frequency, this implies more compact transceivers and antennas, and high sensing accuracy and imaging resolution. Challenges arise with these application opportunities when it comes to realizing prototype or demonstrators in practice. This thesis proposes some of the solutions addressing such challenges in a laboratory environment.High data rate millimeter-wave transmission experiments have been demonstrated with the help of advanced instrumentations. These demonstrations show the potential of transceiver chipsets. On the other hand, the real-time communication demonstrations are limited to either low modulation order signals or low symbol rate transmissions. The reason for that is the lack of commercially available high-speed analog-to-digital converters (ADCs); therefore, conventional digital synchronization methods are difficult to implement in real-time systems at very high data rates. In this thesis, two synchronous baseband receivers are proposed with carrier recovery subsystems which only require low-speed ADCs [A][B].Besides synchronization, high-frequency signal generation is also a challenge in millimeter-wave communications. The frequency divider is a critical component of a millimeter-wave frequency synthesizer. Having both wide locking range and high working frequencies is a challenge. In this thesis, a tunable delay gated ring oscillator topology is proposed for dual-mode operation and bandwidth extension [C]. Millimeter-wave radar offers advantages for high accuracy sensing. Traditional millimeter-wave radar with frequency-modulated continuous-wave (FMCW), or continuous-wave (CW), all have their disadvantages. Typically, the FMCW radar cannot share the spectrum with other FMCW radars.\ua0 With limited bandwidth, the number of FMCW radars that could coexist in the same area is limited. CW radars have a limited ambiguous distance of a wavelength. In this thesis, a phase-modulated radar with micrometer accuracy is presented [D]. It is applicable in a multi-radar scenario without occupying more bandwidth, and its ambiguous distance is also much larger than the CW radar. Orthogonal frequency-division multiplexing (OFDM) radar has similar properties. However, its traditional fast calculation method, fast Fourier transform (FFT), limits its measurement accuracy. In this thesis, an accuracy enhancement technique is introduced to increase the measurement accuracy up to the micrometer level [E]

Enhancing User Experience by Extracting Application Intelligence from Network Traffic

Internet Service Providers (ISPs) continue to get complaints from users on poor experience for diverse Internet applications ranging from video streaming and gaming to social media and teleconferencing. Identifying and rectifying the root cause of these experience events requires the ISP to know more than just coarse-grained measures like link utilizations and packet losses. Application classification and experience measurement using traditional deep packet inspection (DPI) techniques is starting to fail with the increasing adoption of traffic encryption and is not cost-effective with the explosive growth in traffic rates. This thesis leverages the emerging paradigms of machine learning and programmable networks to design and develop systems that can deliver application-level intelligence to ISPs at scale, cost, and accuracy that has hitherto not been achieved before. This thesis makes four new contributions. Our first contribution develops a novel transformer-based neural network model that classifies applications based on their traffic shape, agnostic to encryption. We show that this approach has over 97% f1-score for diverse application classes such as video streaming and gaming. Our second contribution builds and validates algorithmic and machine learning models to estimate user experience metrics for on-demand and live video streaming applications such as bitrate, resolution, buffer states, and stalls. For our third contribution, we analyse ten popular latency-sensitive online multiplayer games and develop data structures and algorithms to rapidly and accurately detect each game using automatically generated signatures. By combining this with active latency measurement and geolocation analysis of the game servers, we help ISPs determine better routing paths to reduce game latency. Our fourth and final contribution develops a prototype of a self-driving network that autonomously intervenes just-in-time to alleviate the suffering of applications that are being impacted by transient congestion. We design and build a complete system that extracts application-aware network telemetry from programmable switches and dynamically adapts the QoS policies to manage the bottleneck resources in an application-fair manner. We show that it outperforms known queue management techniques in various traffic scenarios. Taken together, our contributions allow ISPs to measure and tune their networks in an application-aware manner to offer their users the best possible experience

Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds