Digital Twin is a new concept that consists of creating an up-to-date virtual asset in the cyberspace which mimics the original physical asset in most of its aspects, ultimately to monitor, analyze, test, and optimize the physical asset. In this paper, we investigate and discuss the use of the digital twin concept of the roads as a step towards realizing the dream of smart cities. To this end, we propose the deployment of a Digital Twin Box to the roads that is composed of a 360 • camera and a set of IoT devices connected to a Single Onboard Computer. The Digital Twin Box creates a digital twin of the physical road asset by constantly sending real-time data to the edge/cloud, including the 360 • live stream, GPS location, and measurements of the temperature, humidity. This data will be used for real-time monitoring and other purposes by displaying the live stream via head-mounted devices or using a 360 • web-based player. Additionally, we perform an object detection process to extract all possible objects from the captured stream. For some specific objects (person and vehicle), an identification module and a tracking module are employed to identify the corresponding objects and keep track of all video frames where these objects appeared. The outcome of the latter step would be of outermost importance to many other services and domains such as the national security. To show the viability of the proposed solution, we have implemented and conducted real-world experiments where we focus more on the detection and recognition processes. The achieved results show the effectiveness of the proposed solution in creating a digital twin of the roads, a step forward to enable self-driving vehicles as a crucial component of the smart mobility, using the Digital Twin Box.
The many advantages of Dynamic Adaptive streaming over HTTP (DASH) made it one of the most prevalent video streaming technologies in recent years. Unfortunately, many studies have unveiled the QoE issue of users when multiple DASH clients compete for the bandwidth of a bottleneck link. This issue consists of several aspects, namely the frequent encoding changes, the unfair bandwidth allocation, the inefficient bandwidth utilization, and the relatively long convergence time. These aspects are indeed conflicting each other and resolving them entails tradeoffs. In this paper, we propose a new mathematical model that leverages a score matrix to ensure a fair sharing of the server's bottleneck link between competing clients and satisfies the requests of as many clients as possible and that is for efficient bandwidth utilization. The proposed solution is compared against notable solutions through computer-based simulations, and the results show that the proposed solution achieves high scores in terms of both efficiency and fairness.
Network Function Virtualization (NFV) ecosystem enables the automation of deployment and scaling of softwarized network services (SNSs), thus reducing their operational expenditures. This enables operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. However, to realize the automation of such management practices, it is needed to determine the amount of required resources to allocate the SNS so that its performance requirements are met. This problem is commonly referred to as resources dimensioning problem. In this paper, we address the derivation of a closed-form expression for the optimal resources dimensioning of an SNS in terms of cost or energy efficiency. The performance requirement considered for the SNS is a limit on its mean response time. The performance model considered for the SNS is practical and accurate. The usefulness of the derived closedform expression is successfully validated by means of simulation. The scenario considered for the validation is a video optimization chain located at the SGi-LAN of a mobile network.
The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a CARTERY framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
The recent technological advances in many fields have significantly contributed to the development of the Advanced Driver Assistance System (ADAS), which in turn will greatly contribute to the flourishing of self-driving vehicles that can operate autonomously in all road scenarios. Until then, keeping the human input in the loop remains vital to either make decisions in unseen situations or approve vehicles' proposed decisions. In this paper, we leverage VR technology to provide remote assistance for self-driving in critical situations. Specifically, we study the delivery of a 360°live stream at high resolution (4K) to a remote operation center for supporting self-driving vehicles' decisions when, for example, merging onto the highway. The 360°video stream will be consumed by a human operator wearing a head-mounted display for increased flexibility, faster control, and an immersive experience. In addition, the 360°s tream is augmented with relevant context data, such as the vehicle's speed and distance to other road objects, in order to increase the human operator's awareness of the vehicle and its surroundings. Depending on the human operator's proximity to the source, the video stream can either be viewed through the cloud or the edge, which further reduces the glass-to-glass latency. Experimental results demonstrate the effectiveness of employing VR technology to remotely command and control self-driving vehicles in critical situations. The results show that a 360°stream at 4K resolution can be delivered in sub-second glass-to-glass latency, which allows the operator to make timely decisions.
HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery technology over best-effort networks nowadays, thanks to the myriad advantages it brings. However, many studies have shown that HAS suffers from many Quality of Experience (QoE)-related issues in the presence of competing players. This is mainly caused by the selfishness of the players resulting from the decentralized intelligence given to the player. Another limitation is the bottleneck link that could happen at any time during the streaming session and anywhere in the network. These issues may result in wobbling bandwidth perception by the players and could lead to missing the deadline for chunk downloads, which result in the most annoying issue consisting of rebuffering events. In this paper, we leverage the Software-Defined Networking paradigm to take advantage of the global view of the network and its powerful intelligence that allows reacting to the network changing conditions. Ultimately, we aim at preventing the re-buffering events, resulting from deadline misses, and ensuring high QoE for the accepted clients in the system. To this end, we use Deterministic Network Calculus (DNC) to guarantee a maximum delay for the download of the video chunks while maximizing the perceived video quality. Simulation results show that the proposed solution ensures high efficiency for the accepted clients without any rebuffering events which result in high user QoE. Consequently, it might be highly useful for scenarios where video chunks should be strictly downloaded on-time or ensuring low delay with high user QoE such as serving video premium subscribers or remote control/driving of an autonomous vehicle in future 5G mobile networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.