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- Genre: Doctoral Dissertation
Description
Emerging modular cable network architectures distribute some cable headend functions to remote nodes that are located close to the broadcast cable links reaching the cable modems (CMs) in the subscriber homes and businesses. In the Remote- PHY (R-PHY) architecture, a Remote PHY Device (RPD) conducts the physical layer processing for the analog cable transmissions, while the headend runs the DOCSIS medium access control (MAC) for the upstream transmissions of the distributed CMs over the shared cable link. In contrast, in the Remote MACPHY (R-MACPHY) ar- chitecture, a Remote MACPHY Device (RMD) conducts both the physical and MAC layer processing. The dissertation objective is to conduct a comprehensive perfor- mance comparison of the R-PHY and R-MACPHY architectures. Also, development of analytical delay models for the polling-based MAC with Gated bandwidth alloca- tion of Poisson traffic in the R-PHY and R-MACPHY architectures and conducting extensive simulations to assess the accuracy of the analytical model and to evaluate the delay-throughput performance of the R-PHY and R-MACPHY architectures for a wide range of deployment and operating scenarios. Performance evaluations ex- tend to the use of Ethernet Passive Optical Network (EPON) as transport network between remote nodes and headend. The results show that for long CIN distances above 100 miles, the R-MACPHY architecture achieves significantly shorter mean up- stream packet delays than the R-PHY architecture, especially for bursty traffic. The extensive comparative R-PHY and R-MACPHY comparative evaluation can serve as a basis for the planning of modular broadcast cable based access networks.
ContributorsAlharbi, Ziyad Ghazai (Author) / Reisslein, Martin (Thesis advisor) / Thyagaturu, Akhilesh (Committee member) / Zhang, Yanchao (Committee member) / McGarry, Michael (Committee member) / Arizona State University (Publisher)
Created2019
Description
In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the system. In the first part, I present a reconstruction-free approach to high-level inference in computer vision, wherein I consider the specific case of activity analysis, and show that using correlation filters, one can perform effective action recognition and localization directly from a class of spatial-multiplexing cameras, called compressive cameras, even at very low measurement rates of 1\%. In the second part, I outline a deep learning based non-iterative and real-time algorithm to reconstruct images from compressively sensed (CS) measurements, which can outperform the traditional iterative CS reconstruction algorithms in terms of reconstruction quality and time complexity, especially at low measurement rates. To overcome the limitations of compressive cameras, which are operated with random measurements and not particularly tuned to any task, in the third part of the dissertation, I propose a method to design spatial-multiplexing measurements, which are tuned to facilitate the easy extraction of features that are useful in computer vision tasks like object tracking. The work presented in the dissertation provides sufficient evidence to high-level inference in computer vision at extremely low measurement rates, and hence allows us to think about the possibility of revamping the current day computer systems.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Li, Baoxin (Committee member) / Chakrabarti, Chaitali (Committee member) / Sankaranarayanan, Aswin (Committee member) / LiKamWa, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
Efficient visual sensing plays a pivotal role in enabling high-precision applications in augmented reality and low-power Internet of Things (IoT) devices. This dissertation addresses the primary challenges that hinder energy efficiency in visual sensing: the bottleneck of pixel traffic across camera and memory interfaces and the energy-intensive analog readout process in image sensors. To overcome the bottleneck of pixel traffic, this dissertation proposes a visual sensing pipeline architecture that enables application developers to dynamically adapt the spatial resolution and update rates for specific regions within the scene. By selectively capturing and processing high-resolution frames only where necessary, the system significantly reduces energy consumption associated with memory traffic. This is achieved by encoding only the relevant pixels from the commercial image sensors with standard raster-scan pixel read-out patterns, thus minimizing the data stored in memory. The stored rhythmic pixel region stream is decoded into traditional frame-based representations, enabling seamless integration into existing video pipelines. Moreover, the system includes runtime support that allows flexible specification of the region labels, giving developers fine-grained control over the resolution adaptation process. Experimental evaluations conducted on a Xilinx Field Programmable Gate Array (FPGA) platform demonstrate substantial reductions of 43-64% in interface traffic, while maintaining controllable task accuracy. In addition to the pixel traffic bottleneck, the dissertation tackles the energy intensive analog readout process in image sensors. To address this, the dissertation proposes aggressive scaling of the analog voltage supplied to the camera. Extensive characterization on off-the-shelf sensors demonstrates that analog voltage scaling can significantly reduce sensor power, albeit at the expense of image quality. To mitigate this trade-off, this research develops a pipeline that allows application developers to adapt the sensor voltage on a frame-by-frame basis. A voltage controller is integrated into the existing Raspberry Pi (RPi) based video streaming pipeline, generating the sensor voltage. On top of that, the system provides a software interface for vision applications to specify the desired voltage levels. Evaluation of the system across a range of voltage scaling policies on popular vision tasks demonstrates that the technique can deliver up to 73% sensor power savings while maintaining reasonable task fidelity.
ContributorsKodukula, Venkatesh (Author) / LiKamWa, Robert (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Brunhaver, John (Committee member) / Nambi, Akshay (Committee member) / Arizona State University (Publisher)
Created2023
Description
Software Defined Networking has been the primary component for Quality of Service provisioning in the last decade. The key idea in such networks is producing independence between the control and the data-plane. The control plane essentially provides decision making logic to the data-plane, which in-turn is only responsible for moving the packets from source to destination based on the flow-table entries and actions. In this thesis an in-depth design and analysis of Software Defined Networking control plane architecture for Next Generation Networks is provided. Typically, Next Generation Networks are those that need to satisfy Quality of Service restrictions (like time bounds, priority, hops, to name a few) before the packets are in transit. For instance, applications that are dependent on prediction popularly known as ML/AI applications have heavy resource requirements and require completion of tasks within the time bounds otherwise the scheduling is rendered useless. The bottleneck could be essentially on any layer of the network stack, however in this thesis the focus is on layer-2 and layer-3 scheduling. To that end, the design of an intelligent control plane is proposed by paying attention to the scheduling, routing and admission strategies which are necessary to facilitate the aforementioned applications requirement. Simulation evaluation and comparisons with state of the art approaches is provided withreasons corroborating the design choices. Finally, quantitative metrics are defined and measured to justify the benefits of the designs.
ContributorsBalasubramanian, Venkatraman (Author) / Reisslein, Martin (Thesis advisor) / Suppappola, Antonia Papandreou (Committee member) / Zhang, Yanchao (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Existing radio access networks (RANs) allow only for very limited sharing of thecommunication and computation resources among wireless operators and heterogeneous
wireless technologies. The introduced LayBack architecture facilitates communication
and computation resource sharing among different wireless operators and technologies.
LayBack organizes the RAN communication and multiaccess edge computing (MEC)
resources into layers, including a devices layer, a radio node (enhanced Node B and
access point) layer, and a gateway layer. The layback optimization study addresses
the problem of how a central SDN orchestrator can flexibly share the total backhaul
capacity of the various wireless operators among their gateways and radio nodes
(e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to facilitate
flexible network service virtualization and migration, network functions (NFs) are increasingly
executed by software modules as so-called "softwarized NFs" on General-Purpose
Computing (GPC) platforms and infrastructures. GPC platforms are not specifically
designed to efficiently execute NFs with their typically intense Input/Output (I/O)
demands. Recently, numerous hardware-based accelerations have been developed to
augment GPC platforms and infrastructures, e.g., the central processing unit (CPU)
and memory, to efficiently execute NFs. The computing capabilities of client devices
are continuously increasing; at the same time, demands for ultra-low latency (ULL)
services are increasing. These ULL services can be provided by migrating some
micro-service container computations from the cloud and multi-access edge computing
(MEC) to the client devices.
ContributorsShantharama, Prateek (Author) / Reisslein, Martin (Thesis advisor) / McGarry, Michael (Committee member) / Thyagaturu, Akhilesh (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the rapid development of both hardware and software, mobile devices with their advantages in mobility, interactivity, and privacy have enabled various applications, including social networking, mixed reality, entertainment, authentication, and etc.In diverse forms such as smartphones, glasses, and watches, the number of mobile devices is expected to increase by 1 billion per year in the future.
These devices not only generate and exchange small data such as GPS data, but also large data including videos and point clouds.
Such massive visual data presents many challenges for processing on mobile devices.
First, continuously capturing and processing high resolution visual data is energy-intensive, which can drain the battery of a mobile device very quickly.
Second, data offloading for edge or cloud computing is helpful, but users are afraid that their privacy can be exposed to malicious developers.
Third, interactivity and user experience is degraded if mobile devices cannot process large scale visual data in real-time such as off-device high precision point clouds. To deal with these challenges, this work presents three solutions towards fine-grained control of visual data in mobile systems, revolving around two core ideas, enabling resolution-based tradeoffs and adopting split-process to protect visual data.In particular, this work introduces: (1) Banner media framework to remove resolution reconfiguration latency in the operating system for enabling seamless dynamic resolution-based tradeoffs;
(2) LesnCap split-process application development framework to protect user's visual privacy against malicious data collection in cloud-based Augmented Reality (AR) applications by isolating the visual processing in a distinct process;
(3) A novel voxel grid schema to enable adaptive sampling at the edge device that can sample point clouds flexibly for interactive 3D vision use cases across mobile devices and mobile networks. The evaluation in several mobile environments demonstrates that, by controlling visual data at a fine granularity, energy efficiency can be improved by 49% switching between resolutions, visual privacy can be protected through split-process with negligible overhead, and point clouds can be delivered at a high throughput meeting various requirements.Thus, this work can enable more continuous mobile vision applications for the future of a new reality.
ContributorsHu, Jinhan (Author) / LiKamWa, Robert (Thesis advisor) / Wu, Carole-Jean (Committee member) / Doupe, Adam (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ethernet based technologies are emerging as the ubiquitous de facto form of communication due to their interoperability, capacity, cost, and reliability. Traditional Ethernet is designed with the goal of delivering best effort services. However, several real time and control applications require more precise deterministic requirements and Ultra Low Latency (ULL), that Ethernet cannot be used for. Current Industrial Automation and Control Systems (IACS) applications use semi-proprietary technologies that provide deterministic communication behavior for sporadic and periodic traffic, but can lead to closed systems that do not interoperate effectively. The convergence between the informational and operational technologies in modern industrial control networks cannot be achieved using traditional Ethernet. Time Sensitive Networking (TSN) is a suite of IEEE standards designed by augmenting traditional Ethernet with real time deterministic properties ideal for Digital Signal Processing (DSP) applications. Similarly, Deterministic Networking (DetNet) is a Internet Engineering Task Force (IETF) standardization that enhances the network layer with the required deterministic properties needed for IACS applications. This dissertation provides an in-depth survey and literature review on both standards/research and 5G related material on ULL. Recognizing the limitations of several features of the standards, this dissertation provides an empirical evaluation of these approaches and presents novel enhancements to the shapers and schedulers involved in TSN. More specifically, this dissertation investigates Time Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), and Cyclic Queuing and Forwarding (CQF) schedulers. Moreover, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. Both the centralized network/distributed user model (hybrid model) and the fully-distributed (decentralized) IEEE 802.1Qcc model are examined on a typical industrial control network with the goal of maximizing scheduled traffic streams. Finally, since industrial applications and cyber-physical systems require timely delivery, any channel or node faults can cause severe disruption to the operational continuity of the application. Therefore, the IEEE 802.1CB, Frame Replication and Elimination for Reliability (FRER), is examined and tested using machine learning models to predict faulty scenarios and issue remedies seamlessly.
ContributorsNasrallah, Ahmed (Author) / Reisslein, Martin (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / LiKamWa, Robert (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Huge advancements have been made over the years in terms of modern image-sensing hardware and visual computing algorithms (e.g. computer vision, image processing, computational photography). However, to this day, there still exists a current gap between the hardware and software design in an imaging system, which silos one research domain from another. Bridging this gap is the key to unlocking new visual computing capabilities for end applications in commercial photography, industrial inspection, and robotics. This thesis explores avenues where hardware-software co-design of image sensors can be leveraged to replace conventional hardware components in an imaging system with software for enhanced reconfigurability. As a result, the user can program the image sensor in a way best suited to the end application. This is referred to as software-defined imaging (SDI), where image sensor behavior can be altered by the system software depending on the user's needs. The scope of this thesis covers the development and deployment of SDI algorithms for low-power computer vision. Strategies for sparse spatial sampling have been developed in this thesis for power optimization of the vision sensor. This dissertation shows how a hardware-compatible state-of-the-art object tracker can be coupled with a Kalman filter for energy gains at the sensor level. Extensive experiments reveal how adaptive spatial sampling of image frames with this hardware-friendly framework offers attractive energy-accuracy tradeoffs. Another thrust of this thesis is to demonstrate the benefits of reinforcement learning in this research avenue. A major finding reported in this dissertation shows how neural-network-based reinforcement learning can be exploited for the adaptive subsampling framework to achieve improved sampling performance, thereby optimizing the energy efficiency of the image sensor. The last thrust of this thesis is to leverage emerging event-based SDI technology for building a low-power navigation system. A homography estimation pipeline has been proposed in this thesis which couples the right data representation with a differential scale-invariant feature transform (SIFT) module to extract rich visual cues from event streams. Positional encoding is leveraged with a multilayer perceptron (MLP) network to get robust homography estimation from event data.
ContributorsIqbal, Odrika (Author) / Jayasuriya, Suren (Thesis advisor) / Spanias, Andreas (Thesis advisor) / LiKamWa, Robert (Committee member) / Owens, Chris (Committee member) / Arizona State University (Publisher)
Created2023