Packet Scheduling Scheme to Guarantee QoS in Internet of Things

Sharma, Reema; Kumar, Navin; Gowda, Namratha B.; Srinivas, T.

doi:10.1007/s11277-017-5218-8

Cited by 18 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the widespread adoption of the Internet, the issue of QoS has always been debated [21,22]. Traditional networks employ IntServ and DiffServ as mechanisms to ensure QoS [23,24].…”

Section: Qos Provisioningmentioning

confidence: 99%

Fine-grained QoS provisioning with micropayments in wireless networks

Masaki

Nguyen

Sekiya

2023

NOLTA

View full text Add to dashboard Cite

In the era of 5G and beyond, advanced network technologies will be adopted along with providing high, diverse Quality of Service (QoS) levels of network services. In such a context, it is necessary to have a more flexible and responsive network provisioning system than the existing ones in 4G. This paper addresses the issue of fine-grained QoS provisioning with payment, which benefits the users and providers. As a solution, we propose Software Defined Networking (SDN)-based QoS provisioning with IOTA micropayments (SQI), which allocates the guaranteed QoS service on a per-network flow or per-user basis after confirmed payment. Different from the previous works, SQI can provide both bandwidths and delay guaranteed services, which leverage the SDN technology. Additionally, SQI can support concurrently multiple network flows, e.g., from different clients. Besides, SQI adopts the (public) IOTA Tangle 1.5, which is more lightweight and faster than the previous IOTA versions. Moreover, SQI includes a private IOTA Tangle that can be tunable to speed up the transaction process. We have implemented SQI using Mininet-WiFi, the POX SDN controller, and IOTA 1.5 and evaluated SQI in various scenarios. The evaluation results show that SQI can provide expected delay and bandwidth (with micropayments) to one and multiple flows. We have also compared the processing request duration of the public IOTA and private ones to reveal the appealing feature of private IOTA.

show abstract

“…Since the widespread adoption of the Internet, the issue of QoS has always been debated [21,22]. Traditional networks employ IntServ and DiffServ as mechanisms to ensure QoS [23,24].…”

Section: Qos Provisioningmentioning

confidence: 99%

Fine-grained QoS provisioning with micropayments in wireless networks

Masaki

Nguyen

Sekiya

2023

NOLTA

View full text Add to dashboard Cite

show abstract

“…6, we consider the first scenario where the service time is fixed and we plot the curves of the number of packets that exceed their maximum delays ( Fig.6(a)), the mean number of packets that do not respect their priorities ( Fig.6(b)), and the mean number of packets in the system (Fig.6(c)) in function of the arrival rate lambda. These results are obtained using our QP-SH algorithm, the existing Round-Robin(RR) [31] and Weighted RR (WRR) [32] based solutions and the existing First in First out (FIFO) and FIFO preemptive (FIFO-prem) based solutions [9]. The mean number of packets that do not respect their priorities can be identified by comparing the QoS-level pair classification method (which is based on the priority provided by the two-level classifier; first using the maximum allowed delay and then, using QoS priorities) with that based on the QoS priority provided by the ToS field in the IP header.…”

Section: Performance Evaluationmentioning

confidence: 99%

Dynamic QoE/QoS-Aware Queuing for Heterogeneous Traffic in Smart Home

2019

View full text Add to dashboard Cite

Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different quality-of-service (QoS) requirements. The state-of-the-art QoS-aware scheduling methods consider only the conventional priority metrics based on the IP type of service (ToS) field to make a decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and quality of experience (QoE), since higher priority traffic may not require lower delay than lower priority traffic (for example, traffic generated from medical sensors has a higher priority than traffic from streaming devices, but the latter one requires lower maximum delay). To solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in the smart home network (QP-SH) to make dynamic QoS-aware scheduling decisions meeting delay requirements of all traffic while preserving their degrees of criticality. A new metric that combines the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay is defined. Our experiments show that the proposed solution increases 15% of packets that meet their priorities and 40% of packets that meet their maximum delays as well as 25% of the total number of packets in the system. INDEX TERMS Quality of service, quality of experience, smart home, traffic scheduling optimization.

show abstract

“…The authors investigate the reliability and power consumption of a node and the delay for successful packet transmission. Simulations based studies are conducted in [16,17,18]. In [16], hybrid scheduling is proposed to accommodate QoS constraints of heterogeneous traffics of IoT.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, the factors of the performance of an IoT network are analyzed. Three traffic classes with priorities are considered in [18]. An efficient packet scheduler is proposed and the average packet drop ratio and average jitter for all traffic classes are analyzed.…”

Section: Related Workmentioning

confidence: 99%

A Markovian Model for Internet of Things Application

Salameh¹,

Awad²,

AbuAlrub³

2019

IJCNC

View full text Add to dashboard Cite

Internet of Things (IoT) allows communication among human-to-things, things-to-human, and things-tothings that are incorporated into an information networks allowing automatic information interchange and the processing of data at real time. In this paper, we conduct a performance analysis of a real application defined through four traffic classes with the priorities present in smart cities using Continuous Time Markov Chains(CTMC). Based on a finite capacity queuing system, we propose a new cost-effective analytical model with a push-out management scheme in favor of the highest priority (emergency) traffic. Based on the analytical model, several performance measures for different traffic classes have been studiedextensively including blocking probability; push out probability, delay, channel utilization as well as overall system performance.

show abstract

Packet Scheduling Scheme to Guarantee QoS in Internet of Things

Cited by 18 publications

References 14 publications

Fine-grained QoS provisioning with micropayments in wireless networks

Fine-grained QoS provisioning with micropayments in wireless networks

Dynamic QoE/QoS-Aware Queuing for Heterogeneous Traffic in Smart Home

A Markovian Model for Internet of Things Application

Contact Info

Product

Resources

About