Uncoordinated Massive Wireless Networks: Spatiotemporal Models and Multiaccess Strategies

Chisci, Giovanni; ElSawy, Hesham; Conti, Andrea; Alouini, Mohamed‐Slim; Win, Moe Z.

doi:10.1109/tnet.2019.2892709

Cited by 48 publications

(46 citation statements)

References 44 publications

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“…The interaction among queues, together with the temporal correlation, incurs memory to the service process and can highly complicate the analysis. Fortunately, such dependency of neighborhood queueing status becomes relatively weak at the macroscopic scale, which motivates the following assumption [29]:…”

Section: (21)mentioning

confidence: 99%

“…By noticing that centralized protocols, e.g., the ones proposed in [14], [15], can incur large communication overhead and do not scale with the network size, we propose a distributed scheduling policy that exploits only local information to control the medium access probability at each node. Notably, while a few recent attempts to the design and analysis of a scheduling scheme under similar model have been made in [25], [28], [29], the current paper differs from and generalizes these works in two key aspects: 1) Design: Different from [25], [28], [29], where the channel access probability is universally designed as a single parameter, our approach gives a channel access probability which is a function of local topology and varies among different transmitters. 2) Analysis: While [25], [28], [29] carry out their analysis based on constant channel access probabilities, our analysis characterizes the dynamics of a scheduling policy that changes according to node locations.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Yang

Arafa

Quek

et al. 2021

IEEE Trans. on Mobile Comput.

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Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers' geographic locations, is not well understood. In this paper, we leverage a spatiotemporal model, which allows one to characterize the age of information (AoI) from a joint queueing-geometry perspective, for the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we also derive accurate and tractable expressions for the peak AoI. Numerical results reveal that: i) the packet arrival rate directly affects the service process due to queueing interactions, ii) the proposed scheme can adapt to traffic variations and largely reduce the peak AoI, and iii) the proposed scheme scales well as the network grows in size. This is done by adaptively adjusting the radio access probability at each transmitter to the change of the ambient environment.Index Terms-Poisson bipolar network, age of information, scheduling policy, spatiotemporal analysis, stochastic geometry.

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Section: (21)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Yang

Arafa

Quek

et al. 2021

IEEE Trans. on Mobile Comput.

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“…See Appendix A 1) Network Categorization: It is observed from Lemma 1 that the macroscopic network-wide aggregate characterization depends on the parameter Θ T . Before delving into the details of such characterization, we first discretize the meta distribution of P s (θ) through uniform network partitioning [31]. Categorizing each devices within the network into a distinctive QoS class is not feasible due to the continuous support of P s (θ) ∈ [0, 1].…”

Section: Across Different Time Slots Within the Same Cycle T By An Apmentioning

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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“…Remark 3: The analysis in this paper can be extended to the case of static network with high b and/or d by following the same methodology in [18], which is postponed to future extension.…”

Section: Remarkmentioning

confidence: 99%

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

Benkhelifa¹

2020

Preprint

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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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Uncoordinated Massive Wireless Networks: Spatiotemporal Models and Multiaccess Strategies

Cited by 48 publications

References 44 publications

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

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

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