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Item type:Publication, Access status: without files , Prioritized multistream traffic in uplink IoT networks: spatially interacting vacation queuesMassive Internet of Things (IoT) is foreseen to introduce a plethora of applications for a fully connected world. Heterogeneous traffic is envisaged, where packets generated at each device should be differentiated and served according to their priority. This article develops a novel priority-aware spatiotemporal mathematical model to characterize massive IoT networks with uplink prioritized multistream traffic (PMT). Stochastic geometry is utilized to account for the macroscopic network-wide mutual interference between the coexisting devices. Discrete-time Markov chains (DTMCs) are employed to track the microscopic evolution of packets within each priority queue. To provide a systematic and tractable model, we decompose the prioritized queueing model at each device to a single-queue system with a server vacation. To this end, the IoT PMT network is modeled as spatially interacting vacation queues. Dedicated and shared channel priority-aware access strategies are presented. A priority-agnostic scheme is used as a benchmark to highlight the impact of prioritized uplink transmission on the performance of different priorities in terms of transmission probabilities and delays. Additional performance metrics as the average number of packets, the peak age of information, delay distribution, and Pareto frontiers for different parameters are presented, which give insights on the stable operation of uplink IoT networks with PMT.Publicationtype: Journal Article86 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Grant-Free Opportunistic Uplink Transmission in Wireless-powered IoT: A Spatio-temporal ModelIEEE Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for wireless-power Internet of Things (IoT) networks. This paper jointly characterizes energy harvesting and packet transmissions in grant-free opportunistic uplink IoT networks energized via harvesting downlink energy. To do that, a joint queuing theory and stochastic geometry model is utilized to develop a spatio-temporal analytical model. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC). Such two dimensions are utilized to jointly track the scavenged/depleted energy to/from the batteries along with the arrival/departure of packets to/from devices buffers over time. Consequently, the adopted queuing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average delay, and the average buffer size. The effect of base stations (BSs) densification is discussed and several design insights are provided. The results show that the parameters for uplink power control and opportunistic channel access should be jointly optimized to maximize average network packet throughput, and hence, minimize delay.Publicationtype: Journal Article56 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Spatiotemporal Dependable Task Execution Services in MEC-Enabled Wireless SystemsMulti-access Edge Computing (MEC) enables computation and energy-constrained devices to offload and execute their tasks on powerful servers. Due to the scarce nature of the spectral and computation resources, it is important to jointly consider i) contention-based communications for task offloading and ii) parallel computing and occupation of failure-prone MEC processing resources (virtual machines). The feasibility of task offloading and successful task execution with virtually no failures during the operation time needs to be investigated collectively from a combined point of view. To this end, this letter proposes a novel spatiotemporal framework that utilizes stochastic geometry and continuous time Markov chains to jointly characterize the communication and computation performance of dependable MEC-enabled wireless systems. Based on the designed framework, we evaluate the influence of various system parameters on different dependability metrics such as (i) computation resources availability, (ii) task execution retainability, and (iii) task execution capacity. Our findings showcase that there exists an optimal number of virtual machines for parallel computing at the MEC server to maximize the task execution capacity.Publicationtype: Journal Article80 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Spatiotemporal Framework for Information Freshness in IoT Uplink NetworksTimely 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, such as delay, fails to characterize the timeliness of the system update or to capture the freshness of information from application perspective. Age of information (AoI) is a time-evolving measure of information freshness that has received considerable attention during the past years. In the foreseen large-scale and dense IoT networks, joint temporal (i.e., queue aware) and spatial (i.e., mutual interference aware) characterization of the AoI is required. In this work we provide a spatiotemporal framework that captures the peak AoI for large scale IoT uplink network. To this end, the paper quantifies the peak AoI for large-scale cellular network with Bernoulli uplink traffic. Simulation results are conducted to validate the proposed model and show the effect of traffic load and decoding threshold. Insights are driven to characterize the network stability frontiers and the location-dependent performance within the network.Publicationtype: Conference Paper40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Spatiotemporal Model for Peak AoI in Uplink IoT Networks: Time Versus Event-Triggered Traffic(2020-08); ; Timely message delivery is a key enabler for Internet of Things (IoT) and cyber-physical systems to support a wide range of context-dependent applications. Conventional time-related metrics (e.g., delay and jitter) fail to characterize the timeliness of the system update. Age of Information (AoI) is a time-evolving metric that accounts for the packet interarrival 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 the large-scale IoT uplink network under time-triggered (TT) and event-triggered (ET) traffic. Tools from the 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 the 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.Publicationtype: Journal Article86