Estimation of Quality of Service in Tactile Internet, Augmented Reality and Internet of Things

Alzaghir, Abbas; Paramonov, Alexander; Koucheryav, Andrey

doi:10.1007/978-3-030-97777-1_4

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the TI follows a Pareto distribution, it was necessary to aggregate a Pareto distribution at the traffic generator based on the studies developed by [17]. On the other hand, the AR/VR traffic was modeled as a Poisson distribution, and the IoT devices followed an exponential distribution based on the work done in [21]. Specifically, it was decided to set up synthetic application flows between the GW1 and ER1 to add congestion through the green.DIF and end-to-end traffic by adding synthetic application flows in the blue.DIF.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Fine-Grained Traffic Differentiation in Tactile Internet Scenarios Using RINA

Sarabia-Jácome,

Grasa,

Catalán

2023

2023 IEEE 9th World Forum on Internet of Things (WF-IoT)

View full text Add to dashboard Cite

The tactile Internet (TI) has emerged as an advanced communication technology that enables interaction between humans and machines. However, TI use cases demand low latency, high reliability, and high-capacity requirements to achieve high quality of experience (QoE) in their applications. IoT networks provide transmission services to heterogeneous applications, and the inclusion of high-demand Quality of Service (QoS) requirements by TI applications will increase the difficulty of guaranteeing end-to-end QoS. We proposed a fine-grained traffic differentiation in tactile Internet scenarios using the Recursive Inter-Networking Architecture (RINA) QoS cubes and the Quantitative Timeliness Agreement Multiplexor (QTA-Mux) flow prioritization scheme. We designed four RINA QoS cubes to meet the delay and packet loss requirements of TI, Augmented Reality/Virtual Reality (AR/VR), and IoT applications and to provide fine-grained traffic differentiation. Also, we designed the QTA-Mux policy to enable a trade-off between delay and packet loss and adequately scheduling the service data unit (SDU) for delivering. We implemented a RINA network to test the proposed schemes in IoT environments and conducted several experiments to evaluate the delay and packet loss. The results showed that RINA-based prioritization and traffic differentiation proposed guaranteed end-to-end QoS to support TI applications. Thus, the next generation of IoT networks can use RINA to guarantee the QoS requirements of TI applications.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%