Diffusion Analysis Improves Scalability of IoT Networks to Mitigate the Massive Access Problem

Gelenbe, Erol; Nakıp, Mert; Marek, Dariusz; Czachórski, Tadeusz

doi:10.1109/mascots53633.2021.9614289

Cited by 13 publications

(19 citation statements)

References 47 publications

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“…Most recently the Randomization of Generation Times (RGT) preprocessing algorithm [36] is shown to significantly improve the performance of scheduling heuristics with very low computational cost for large numbers of IoT devices. Also, the Quasi-Deterministic Transmission Policy (QDTP) traffic shaping approach [19,25] has been shown, using queueing theory, diffusion approximations [16] and trace driven simulations, to mitigate the MAP by drastically reducing the waiting time at gateways. This research has shown that RGT and QDTP can mitigate MAP with very low computational requirements for up to 6400 IoT devices.…”

Section: Proactive Solutionsmentioning

confidence: 99%

“…We will summarize together the results of two recent articles [19,36], which offer solutions to reduce MAP. To this end, we first present the analysis in [19] of the probability that the deadline of an IoT packet is met, providing a basis for access policies in IoT networks. Then we review the RGT [36] and QDTP [19] algorithms and their performance.…”

Section: Mitigating Map Using Queueing Theory and Diffusion Approxima...mentioning

confidence: 99%

“…To this end, we first present the analysis in [19] of the probability that the deadline of an IoT packet is met, providing a basis for access policies in IoT networks. Then we review the RGT [36] and QDTP [19] algorithms and their performance.…”

Section: Mitigating Map Using Queueing Theory and Diffusion Approxima...mentioning

confidence: 99%

“…In this paper, we first briefly review methodologies and results with regard to reactive or proactive (predictive) solutions that can mitigate MAP. Then, we summarize two recent research avenues: Randomization of Transmission Times [36] and a novel traffic shaping policy -the Quasi-Deterministic-Transmission-Policy (QDTP) [18,19,25]. We illustrate the results these approaches offer via analytical techniques and trace driven simulations using a publicly available dataset of up to 6400 IoT devices [1] with different deadline constraints.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating the Massive Access Problem in the Internet of Things

Gelenbe

Nakıp

Marek

et al. 2022

Communications in Computer and Information Science

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The traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for real-time data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.

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Section: Proactive Solutionsmentioning

confidence: 99%

Section: Mitigating Map Using Queueing Theory and Diffusion Approxima...mentioning

confidence: 99%

Section: Mitigating Map Using Queueing Theory and Diffusion Approxima...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mitigating the Massive Access Problem in the Internet of Things

Gelenbe

Nakıp

Marek

et al. 2022

Communications in Computer and Information Science

Self Cite

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“…-KONFIDO [17,18,96,97] on the security of communications and data transfers for interconnected European national or regional health services, -GHOST [8,11] regarding the security of IoT systems for the home, and the design of secure IoT home gateways, -SerIoT on the Cybersecurity of IoT systems [7,31] with a range of applications in supply chains, smart cities, smart manufacturing, and other areas. -IoTAC, which aims at securing IoT networks by strengthening the protection of gateways using novel techniques such as Botnet detection, system wide vulnerability assessment [93,94], disruptive checkpoints, and assuring the optimization of the massive access to IoT gateways [67,75].…”

Section: Introductionmentioning

confidence: 99%

AI and Quality of Service Driven Attack Detection, Mitigation and Energy Optimization: A Review of Some EU Project Results

Caglayan

2022

Communications in Computer and Information Science

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This article summarizes briefly the contributions presented in this EuroCyberSecurity Workshop 2021 which is organized as part of the series of International Symposia on Computer and Information Sciences (ISCIS), with the support of the European Commission funded IoTAC Project, that was held on November and in NIce, France, and sponsored by the Institute of Teoretical and Applied Informatics of the Polish Academy of Sciences. It also summarizes some of the research contributions of several EU Projects including NEMESYS, GHOST, KONFIDO, SDK4ED and IoTAC, primarily with a cybersecurity and Machine Learning orientation. Thus subjects covered include the cybersecurity of Mobile Networks and of the Internet of Things (IoT), the design of IoT Gateways and their performance, the security of networked health systems that provide health services to individuals across the EU Member states, as well as the issues of energy consumption by ICT which are becoming increasingly important, including in the cybersecurity perspective, as we focus increasingly on climate change and the needed transition towards highly reduced emissions. Many of the techniques and results discussed in this article are based either on Machine Learning (ML) methods, or on methods for the performance modeling and optimization of networked and distributed computer systems.

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