Performance Analysis of WebRTC-based Video Streaming over Power Constrained Platforms

This work analyses the use of the WebRTC framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of size, weight and power requirements, such as the use case of unmanned aerial vehicles delivering real-time video streams overWebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality

Real-time WebRTC-based design for a telepresence wheelchair

© 2017 IEEE. This paper presents a novel approach to the telepresence wheelchair system which is capable of real-time video communication and remote interaction. The investigation of this emerging technology aims at providing a low-cost and efficient way for assisted-living of people with disabilities. The proposed system has been designed and developed by deploying the JavaScript with Hyper Text Markup Language 5 (HTML5) and Web Real-time Communication (WebRTC) in which the adaptive rate control algorithm for video transmission is invoked. We conducted experiments in real-world environments, and the wheelchair was controlled from a distance using the Internet browser to compare with existing methods. The results show that the adaptively encoded video streaming rate matches the available bandwidth. The video streaming is high-quality with approximately 30 frames per second (fps) and round trip time less than 20 milliseconds (ms). These performance results confirm that the WebRTC approach is a potential method for developing a telepresence wheelchair system

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Web-Based Networked Music Performances via WebRTC: A Low-Latency PCM Audio Solution

Nowadays, widely used videoconferencing software has been diffused even further by the social distancing measures adopted during the SARS-CoV-2 pandemic. However, none of the Web-based solutions currently available support high-fidelity stereo audio streaming, which is a fundamental prerequisite for networked music applications. This is mainly because of the fact that the WebRTC RTCPeerConnection standard or Web-based audio streaming do not handle uncompressed audio formats. To overcome that limitation, an implementation of 16-bit pulse code modulation (PCM) stereo audio transmission on top of the WebRTC RTCDataChannel, leveraging Web Audio and AudioWorklets, is discussed. Results obtained with multiple configurations, browsers, and operating systems showthat the proposed approach outperforms theWebRTC RTCPeerConnection standard in terms of audio quality and latency, which in the authors' best case to date has been reduced to only 40 ms between twoMacBooks on a local area network

Smart hospital emergency system via mobile-based requesting services

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study

Метод оцінки часу затримки в процесі потокового мовлення

Розглянута задача мінімізації затримки медіаконтенту при онлайн-трансляції. Об’єктом дослідження є медіасерверні платформи, що використовуються для організації онлайн-трансляцій медіаконтенту. Метою роботи є дослідження часу затримки при доставці медіаконтенту в процесі онлайн-трансляції. В процесі проведення експериментів встановлено, що найбільші витрати часу на доставку зумовлені процесом обробки потоку в медіасервері. Затримка в медіасервері виникає за рахунок перетворень сигналу. Проаналізовано найбільш поширені на ринку медіапослуг медасервери, які дозволяють організувати онлайнтрансляцію на регіональному рівні. Це Ant Media Server 1.7.2, MistServer 2.14.1, Nimble Streamer Server 3.5.4, Red5 1.1.1,Wowza Streaming Engine 4.7. Запропоновано методику оцінки часу затримки доставки медіаконтенту в мережах потокового мовлення. Розроблена методика надає змогу визначити як загальний час затримки, так і його складові на кожному з етапів доставки

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud

We present a wireless real-time object detection system utilizing single-board devices, cloud computing platforms and web-streaming. Currently, most inference applications stat- ically perform tasks either on local machines or remote cloud servers. However, devices connected through cellular technolo- gies face volatile network conditions, compromising detection performance. Furthermore, while the limited computing power of single-board computers degrade detection correctness, exces- sive power consumption of machine learning models used for inference reduces operation time. In this paper, we propose a dynamic system that monitors embedded device’s wireless link quality and battery level to decide on detecting objects locally or remotely. The experimental results show that our dynamic offloading approach could reduce devices’ energy usage while achieving high accuracy, real-time object detection. Index Terms—Machine learning, WebRTC, object detection