The Transmission Control Protocol.

Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable [3]. Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. [4]) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection [1]. Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime [2]. This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: fstann@usc.edu, johnh@isi.edu. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.

RMST: Reliable Data Transport in Sensor Networks

With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

Mean opinion score (MOS) has become a very popular indicator of perceived media quality. While there is a clear benefit to such a "reference quality indicator" and its widespread acceptance, MOS is often applied without sufficient consideration of its scope or limitations. In this paper, we critically examine MOS and the various ways it is being used today. We highlight common issues with both subjective and objective MOS and discuss a variety of alternative approaches that have been proposed for media quality measurement.

Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives

Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Inferring the subjective perception of a video stream in real time continues to be a stiff problem. This article presents MintMOS: a lightweight, no-reference, loadable kernel module to infer the QoE of a video stream in transit and offer suggestions to improve it. MintMOS revolves around one-time offline construction of a k-dimensional space, which we call the QoE space. A QoE space is a known characterization of subjective perception for any k parameters (network dependent/ independent) that affect it. We create N partitions of the QoE space by generating N video samples for various values of the k parameters and conducting subjective surveys using them. Every partition then has an expected QoE associated with it. Instantaneous parameters of a real-time video stream are compared to the precomputed QoE space to both infer and offer suggestions to improve QoE. Inferring QoE is a lightweight algorithm that runs in linear time. We implemented MintMOS by creating an actual QoE space using three parameters and 27 partitions by conducting surveys with 77 human subjects. In a second set of surveys using 13 video clips, MintMOS's predictions were compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions.

Inferring video QoE in real time

We present a scalable, lightweight, no-reference framework to infer video QoE. Our framework revolves around a one time offline construction of a k-dimensional space, which we call the QoE space. The k-dimensions accommodate k parameters (network-dependent/independent) that potentially affect video quality. The k-dimensional space is partitioned to N representative zones, each with a QoE index. Instantaneous parameter values are matched with the indices to infer QoE. To validate our framework, we construct a 3-dimensional QoE space with bit-rate, loss, and delay as the principal components. We create 18 video samples with unique combinations of the 3 parameters. 77 human subjects rated these video samples on a scale of 1 to 5 to create the QoE space. In a second set of survey, our predicted MOS was compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions. An implementation of our framework on standard Linux PC shows we can compute 20 MOS calculations per second with 3 parameters and 18 partitions of the QoE space.

Evaluating Quality of Experience for Streaming Video in Real Time

Most resource allocation decisions in converged multi-service networks are presently driven by QoS parameters and service level agreements. Network operators are realizing that such policy decisions ought to be based on quality of experience (QoE), which is a better measure of subjective video perception. Video QoE has two components: (a) zapping and (b) video Quality or video mean opinion score (MOS). Zapping anomalies are well understood and easily characterized, but there is no consensus on what the definition of a video-MOS should be. This is largely because there is little understanding of qualitative and quantitative subjective degradations caused by networking events like loss, delay, jitter, and error. In this work we conduct extensive experiments on a simulation testbed in a search for a good video-MOS definition, with specific emphasis on quality degradations due to network transmission. We deploy "collector" nodes that can gather various statistics of a given flow. We also identify key parameters that such collector nodes must maintain such that an overlay of such nodes can identity impairment points. Lightweight video-MOS definition evolving out this work can be used in collector overlay networks to solve problems like root-cause analysis, capacity-planning and various network optimization problems.

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Towards a Video QoE Definition in Converged Networks

This paper presents large scale Internet measurements to understand and improve the effects of Internet path selection on perceived video quality. We systematically study a large number of Internet paths between popular video destinations and clients to create an empirical understanding of location, persistence and recurrence of failures. We map these failures to perceptual quality by reconstructing video clips obtained from the trace to quantify both the perceptual degradations from these failures as well as the fraction of such failures that can be recovered.We then investigate ways to recover from QoE degradation by choosing one-hop detour paths that preserve application specific policies. We seek simple, scalable path selection strategies without the need for background path monitoring or apriori path knowledge of any kind. To do this, we deployed five measurement overlays: one each in the US, Europe, Asia-Pacific, and two spread across the globe. We used these to stream IP-traces of a variety of clips between source-destination pairs while probing alternate paths for an entire week. Our results indicate that a source can recover from upto 90% of the degradations by attempting to restore QoE with any five randomly chosen nodes in an overlay. We argue that our results are robust across datasets.Finally, we design and implement a prototype packet forwarding module called source initiated frame restoration (SIFR). We deployed SIFR on PlanetLab nodes, and compared the performance of SIFR with the default Internet routing. We show that SIFR outperforms IP-path selection by providing higher on-screen perceptual quality.

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Effects of internet path selection on video-QoE

With the proliferation of multimedia content on the Internet, there is an increasing demand for video streams with high perceptual quality. The capability of present-day Internet links in delivering high-perceptual-quality streaming services, however, is not completely understood. Link-level degradations caused by intradomain routing policies and inter-ISP peering policies are hard to obtain, as Internet service providers often consider such information proprietary. Understanding link-level degradations will enable us in designing future protocols, policies, and architectures to meet the rising multimedia demands. This paper presents a trace-driven study to understand quality-of-experience (QoE) capabilities of present-day Internet links using 51 diverse ISPs with a major presence in the US, Europe, and Asia-Pacific. We study their links from 38 vantage points in the Internet using both passive tracing and active probing for six days. We provide the first measurements of link-level degradations and case studies of intra-ISP and inter-ISP peering links from a multimedia standpoint. Our study offers surprising insights into intradomain traffic engineering, peering link loading, BGP, and the inefficiencies of using autonomous system (AS)-path lengths as a routing metric. Though our results indicate that Internet routing policies are not optimized for delivering high-perceptual-quality streaming services, we argue that alternative strategies such as overlay networks can help meet QoE demands over the Internet. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

Mukundan Venkataraman

Papers

Inferring video QoE in real time

Evaluating Quality of Experience for Streaming Video in Real Time

Towards a Video QoE Definition in Converged Networks

Effects of internet path selection on video-QoE

Quantifying video-QoE degradations of internet links