Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks

Abdel-Aziz, Mohamed K.; Samarakoon, Sumudu; Liu, Chen-Feng; Bennis, Mehdi; Saad, Walid

doi:10.1109/tcomm.2019.2961083

Cited by 106 publications

(41 citation statements)

References 38 publications

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“…More advanced scheduling mechanisms are proposed to minimize AoI and, sometimes, jointly satisfy other performance metrics, e.g., limiting the energy consumption or the network congestion [31]. More recently, the notion of AoI has been considered for analyzing ultra-reliable low-latency communications (URLLC) in 5G vehicular networks [32]. The proposal considers both periodic and stochastic arrivals of transient packets and allows vehicles to minimize their transmit power while ensuring URLLC communications based on a probabilistic measure of AoI violation.…”

Section: Minimizing Age Of Information Of Safety Informationmentioning

confidence: 99%

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

Amadeo

2021

Telecom

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Vehicular Named Data Networking (VNDN) is a revolutionary information-centric architecture specifically conceived for vehicular networks and characterized by name-based forwarding and in-network caching. So far, a variety of caching schemes have been proposed for VNDN that work in presence of static Data packets, like traditional Internet contents. However, with the advent of Internet of Things (IoT) and Internet of Vehicles (IoV) applications, large sets of vehicular contents are expected to be transient, i.e., they are characterized by a limited lifetime and become invalid after the latter expires. This is the case of information related to road traffic or parking lot availability, which can change after a few minutes—or even after a few seconds—it has been generated at the source. The transiency of contents may highly influence the network performance, including the gain of in-network caching. Therefore, in this paper, we consider the dissemination of transient contents in vehicular networks and its effects on VNDN caching. By providing a detailed review of related work, we identify the main challenges and objectives when caching transient contents, e.g., to avoid cache inconsistency, to minimize the Age of Information (AoI) and the retrieval latency, and the main strategies to fulfill them. We scan the existing caching and replacement policies specifically designed for transient contents in VNDN and, finally, we outline interesting research perspectives.

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Section: Minimizing Age Of Information Of Safety Informationmentioning

confidence: 99%

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

Amadeo

2021

Telecom

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“…where A, x, y). Next, we show that the optimal historydependent updating control policy π ∈ Π H in Definition 2 for the risk-aware MDP in (9) can be constructed by obtaining the optimal augmented stationary updating control policyπ ∈Π S in Definition 3 for the augmented MDP in (11).…”

Section: A Reduction Of a Risk-aware Mdp To An Augmented Mdpmentioning

confidence: 99%

“…Theorem 1: For any given (A, x, y) ∈ A × X × Y , the optimal function V * (•) in (11) satisfies that:…”

Section: B Optimality Equationsmentioning

confidence: 99%

“…Thus, it is critical to focus on the AoI not only in the average sense, but also in a risk-related sense. Recently, the works in [10] and [11] considered the tail of the AoI distribution (with extremely large AoI) for vehicular networks and wireless industrial networks, respectively. Meanwhile, the work in [12] analyzed the violation probability of the peak AoI for a point-to-point communication system with short packets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk-Aware Optimization of Age of Information in the Internet of Things

Zhou

Saad

Bennis

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted riskaware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the riskaware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal historydependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in realtime IoT monitoring systems in a risk-aware manner.

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“…Extensive works have been devoted to realizing URLLC in vehicular networks [2]- [5]. While sub-6 GHz bands are widely considered in these works, millimeter wave (mmWave) bands have also been included as potential bands for vehicular networks in 3GPP standards [6].…”

Section: Introductionmentioning

confidence: 99%

1 A Deep Reinforcement Learning Framework to Combat Dynamic Blockage in mmWave V2X Networks

Chen

Zhou

et al. 2020

2020 2nd 6G Wireless Summit (6G SUMMIT)

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Millimeter Wave (mmWave) systems are considered as one of the key technologies in future wireless systems due to the abundant spectrum resources in mmWave band. With the aim of achieving the capacity requirements in vehicular networks, large antenna arrays can be deployed at both the road side units (RSUs) side and the vehicles side. However, dynamic blockage caused by mobile obstacles in mmWave bands may hinder the system reliability. In this work, we study the temporal effects of dynamic blockage in vehicular networks and propose a deep reinforcement learning framework to overcome dynamic blockage. By dynamically adjusting blockage detection parameters and making intelligent handover decisions according to the observed states, system reliability can be significantly improved. Simulation results based on ray-tracing channel data show that the proposed scheme reduces the violation probability by 28.9% over conventional schemes.

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Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks

Cited by 106 publications

References 38 publications

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

A Literature Review on Caching Transient Contents in Vehicular Named Data Networking

Risk-Aware Optimization of Age of Information in the Internet of Things

1 A Deep Reinforcement Learning Framework to Combat Dynamic Blockage in mmWave V2X Networks

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