Collision-free medium access based on traffic forecasting

Petkov, Vladislav

doi:10.1109/wowmom.2012.6263687

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…Other work [31,45,48] has developed proactive access schemes for Machineto-Human (M2H) or Human-to-Machine (H2M) traffic, while earlier research [43,44] has focused on Human-to-Human (H2H) traffic. In [43] a schedule-based protocol an expert system is used to determine schedules that minimize delay and maximize channel utilization.…”

Section: Proactive Solutionsmentioning

confidence: 99%

“…In [43] a schedule-based protocol an expert system is used to determine schedules that minimize delay and maximize channel utilization. In [44], forecasts of data rates of individual applications are used to schedule channel scheduling, and network load has also been balanced based on the forecast of the total load of all machine-type devices [31]. To mitigate the latency bottleneck due to the contention in optical or wireless networks [48], the proactive allocation of bandwidth to transmissions of packet bursts based on Artificial Neural Network (ANN) forecasts is studied, while in [45] the prediction of network throughput for better Quality of Experience (QoE) is investigated.…”

Section: Proactive Solutionsmentioning

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: Proactive Solutionsmentioning

confidence: 99%

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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“…Second, prior to the development of JFS, only a few works [32]- [34] used predictions of the traffic from individual devices for MAC-layer scheduling. The main differences between these articles and JFS are as follows: (1) While these articles have addressed only Human-to-Human (H2H) applications, we focus entirely on M2M traffic for IoT in this paper.…”

Section: Relationship To the State Of The Artmentioning

confidence: 99%

Multi-Channel Joint Forecasting-Scheduling for the Internet of Things

et al. 2020

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We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.

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“…Another trend of research [25], [26], [27], [28], [29], [30] has suggested proactive network solutions, and other research has used the predictability of traffic generation patterns of IoT devices to address the Massive Access Problem under the "predictive network" framework. To this end, a recent paper [31] proposed the Joint Forecasting-Scheduling (JFS) system and the Priority based on Average Load (PAL) scheduling algorithm which allocates available resources to IoT devices based on forecasting the traffic generation pattern of each device for the case of single frequency channels.…”

Section: Introductionmentioning

confidence: 99%