Spatiotemporal Model for Uplink IoT Traffic: Scheduling and Random Access Paradox

Gharbieh, Mohammad; ElSawy, Hesham; Yang, Hong-Chuan; Bader, Ahmed; Alouini, Mohamed‐Slim

doi:10.1109/twc.2018.2876522

Cited by 46 publications

(37 citation statements)

References 44 publications

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“…Evaluate F E (θ, ξ) based on (9) Compute d i , ∀i = {1, 2, · · · , N } from the Discretized F E (θ, ξ) based on (11) and (12) for n = {1, 2, · · · , N } do if α < d n then ⊲ Stability condition Compute x 0,n based on (29) else Set x 0,n = 0 end if end for Compute Θ E based on (18) Increment k end while Output: F E (θ, δ) end Input…”

Section: B Et Trafficmentioning

confidence: 99%

A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic

Emara

ElSawy

Bauch

2020

IEEE Internet Things J.

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Timely message delivery is a key enabler for Internet of Things (IoT) and cyber-physical systems to support wide range of context-dependent applications. Conventional time-related metrics (e.g. delay and jitter) fails to characterize the timeliness of the system update. Age of information (AoI) is a time-evolving metric that accounts for the packet inter-arrival and waiting times to assess the freshness of information. In the foreseen large-scale IoT networks, mutual interference imposes a delicate relation between traffic generation patterns and transmission delays. To this end, we provide a spatiotemporal framework that captures the peak AoI (PAoI) for large scale IoT uplink network under time-triggered (TT) and event triggered (ET) traffic. Tools from stochastic geometry and queueing theory are utilized to account for the macroscopic and microscopic network scales. Simulations are conducted to validate the proposed mathematical framework and assess the effect of traffic load on PAoI. The results unveil a counter-intuitive superiority of the ET traffic over the TT in terms of PAoI, which is due to the involved temporal interference correlations. Insights regarding the network stability frontiers and the location-dependent performance are presented. Key design recommendations regarding the traffic load and decoding thresholds are highlighted.

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Section: B Et Trafficmentioning

confidence: 99%

A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic

Emara

ElSawy

Bauch

2020

IEEE Internet Things J.

Self Cite

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“…Recently, several studies have integrated stochastic geometry and queueing theory to characterize the network performance while accounting for the spatiotemporal traffic generation. For instance, the work in [12], [13] characterizes the delay and the maximum spatiotemporal traffic that grant free and scheduled uplink cellular networks can accommodate. Random access for massive IoT uplink networks with unsaturated traffic is characterized in [14].…”

Section: Introductionmentioning

confidence: 99%

Recycling Cellular Energy for Self-Sustainable IoT Networks: A Spatiotemporal Study

Benkhelifa

ElSawy

McCann

et al. 2020

IEEE Trans. Wireless Commun.

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This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

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“…Scheduled uplink transmission contains multiple phases such as handshaking processes, scheduling request, scheduling response, resource allocation and exclusive access transmissions [5]. The complicated multi-phase transmission results in unnecessary latency and may break the constraint of delay-QoS of URLLC applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, uplink grant-free random access transmission schemes were proposed by 3rd Generation Partnership Project (3GPP). This simple single-phase transmission strategy allows skipping the lengthy scheduling request and resource allocation process [5], and so reducing the latency significantly.…”

Section: Introductionmentioning

confidence: 99%

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

Chi

Zhao

et al. 2020

IEEE Access

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As a simple single-phase transmission strategy, grant-free access is believed to be an effective way to guarantee the stringent quality of service (QoS) requirements for ultra-reliable low-latency communications (URLLCs). However, unless a theory-based fine evaluation on dynamic delay, we cannot hope to overcome the natural defects of random access and so effectively utilize the time-frequency resources. In this paper, we propose a novel multi-channel ALOHA-type (M-ALOHA) grant-free access algorithm for heterogeneous machine type communication (MTC) networks with URLLC-type terminals and delay-tolerant massive MTC (mMTC)-type terminals coexistence. Firstly, we construct a statistical service model characterizing the transmission rate of each terminal with joint consideration of the features of M-ALOHA access scheme, short packet transmissions and frequency-selective fading channel. Then, taking the great advantages of service-martingales theory in random queuing analysis, we present an ingenious delay analysis and obtain the martingales-based formulation of delay-bound violation probability, where the sporadic feature of MTC traffic arrival is carefully addressed. Finally, the M-ALOHA algorithm is formulated as a system throughput maximization problem subject to martingales-based statistical delay-QoS and the total bandwidth of system. The problem is solved by the proposed bi-objective multi-variable-grey wolf optimizer (BOMV-GWO) algorithm. As a result, we obtain the access probability for each terminal and the optimal parameters for the system design, including the number of sub-channels, the bandwidth for each sub-channel and the packets transmission rate. Simulation results demonstrate that the performance of our M-ALOHA algorithm is favorable. INDEX TERMS Grant-free access, multi-channel, service-martingales theory, ultra-reliable and low-latency communications (URLLCs).

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Spatiotemporal Model for Uplink IoT Traffic: Scheduling and Random Access Paradox

Cited by 46 publications

References 44 publications

A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic

A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic

Recycling Cellular Energy for Self-Sustainable IoT Networks: A Spatiotemporal Study

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

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