QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

Islam, Muhammad Zubair; Ali, Rashid; Haider, Amir; Kim, Hyung Seok

doi:10.1109/access.2022.3197900

Cited by 3 publications

(7 citation statements)

References 239 publications

(93 reference statements)

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“…A simulator, known as IoTactileSim aims to provide a tool to examine the strict QoS and QoE requirement for tactile applications. Recent work [ 36 ] on QoS provisioning explored the haptic coding, control systems, and intelligent prediction techniques with a focus to deal with mission-critical and delay-sensitive stringent QoS and QoE requirements like ultralow latency, ultrahigh reliability, high availability, and ultrasecurity. This paper aims to propose an ITE framework to utilize the recently proposed haptic traffic reduction or compression methods to adjust the packet rate and packet size based on the level of congestion in the network and improve QoS and QoE.…”

Section: Discussion and Future Work Directionsmentioning

confidence: 99%

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications

Islam

Shahzad

Ali

et al. 2022

Sensors

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With the advancement in next-generation communication technologies, the so-called Tactile Internet is getting more attention due to its smart applications, such as haptic-enabled teleoperation systems. The stringent requirements such as delay, jitter, and packet loss of these delay-sensitive and loss-intolerant applications make it more challenging to ensure the Quality of Service (QoS) and Quality of Experience (QoE). In this regard, different haptic codec and control schemes were proposed for QoS and QoE provisioning in the Tactile Internet. However, they maximize the QoE while degrading the system’s stability under varying delays and high packet rates. In this paper, we present a reinforcement learning-based Intelligent Tactile Edge (ITE) framework to ensure both transparency and stability of teleoperation systems with high packet rates and variable time delay communication networks. The proposed ITE first estimates the network challenges, including communication delay, jitter, and packet loss, and then utilizes a Q-learning algorithm to select the optimal haptic codec scheme to reduce network load. The proposed framework aims to explore the optimal relationship between QoS and QoE parameters and make the tradeoff between stability and transparency during teleoperations. The simulation result indicates that the proposed strategy chooses the optimal scheme under different network impairments corresponding to the congestion level in the communication network while improving the QoS and maximizing the QoE. The end-to-end performance of throughput (1.5 Mbps) and average RTT (70 ms) during haptic communication is achieved with a learning rate and discounted factor value of 0.5 and 0.8, respectively. The results indicate that the communication system can successfully achieve the QoS and QoE requirements by employing the proposed ITE framework.

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Section: Discussion and Future Work Directionsmentioning

confidence: 99%

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications

Islam

Shahzad

Ali

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The TI concept envisions advanced communication and interaction between humans and machines (H2M). Nowadays, IoT has enabled machine-to-machine (M2M) communications and allows the interaction between objects [1]. The TI extends this interaction by adding the sense of touch and tactile feedback to the communication capabilities of IoT devices [2].…”

Section: Introductionmentioning

confidence: 99%

“…Also, high bandwidth capacity is required to accommodate the transmission of large volumes of data. Some TI applications involve high-definition video streaming, virtual reality, and haptic feedback [1].…”

Section: Introductionmentioning

confidence: 99%

“…Quality of service (QoS) schemes are fundamental in the TI to guarantee low latency and high capacity. Efficient traffic engineering is highly required to optimize the network resources and ensures the necessary QoS [1]. The QoS mechanisms reduce the latency, packet loss, and jitter, ensuring that the tactile feedback reaches the user promptly and accurately.…”

Section: Introductionmentioning

confidence: 99%

“…The QoS mechanisms reduce the latency, packet loss, and jitter, ensuring that the tactile feedback reaches the user promptly and accurately. Also, prioritization schemes and differentiated services are crucial to allocate network resources according to the needs of latency-sensitive applications [1]. For example, a TI application that involves virtual reality can be assigned higher priority to ensure its performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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)

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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.

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Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks

Alfakeeh,

Javed

2023

Mathematics

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Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

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QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

Cited by 3 publications

References 239 publications

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications

Fine-Grained Traffic Differentiation in Tactile Internet Scenarios Using RINA

Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks

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