Towards Fresh and Low-Latency Content Delivery in Vehicular Networks: An Edge Caching Aspect

Zhang, Shan; Li, Junjie; Luo, Hongbin; Gao, Jie; Zhao, Lian; Shen, Xuemin

doi:10.1109/wcsp.2018.8555643

Cited by 63 publications

(32 citation statements)

References 15 publications

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“…Following the work of [12], there has been increasing attention towards optimizing the AoI in wireless networks, e.g., through queue system optimization [21], multi-hop network implementation [22], and scheduling policies [23]. In addition, the study in [24] also suggests that reducing the AoI can come at the expense of incurring higher service latency. As such, the Cache-Assisted Lazy Update and Delivery (CALUD) scheme is proposed to manage this tradeoff through selecting the appropriate content update frequency.…”

Section: Related Workmentioning

confidence: 99%

“…During the FL task, there can be more than one instance of model training request initiated by the model owner, e.g., to ensure that the global model is kept up-todate, through model training with updated data. We assume that each instance of request arrival follows the Poisson process [24]. An FL based model training is first initiated through the request of the model owner.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Following [24], we denote the time taken to complete K iterations of local computation and wireless transmission, i.e., one instance of model training, as t, i.e., period. Note that t applies across all workers given that in the synchronous FL scheme, model training duration is constrained by the slowest worker, i.e., a worker has to wait for others to complete the training before the model can be aggregated [19].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

Lim

Xiong

Kang

et al. 2022

IEEE Trans. Ind. Inf.

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For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a Federated Learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation from the deployed IIoT devices to completion of the FL based training. On one hand, if the data is collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner towards AoI and service latency. Performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme towards satisfying varying preferences of AoI and service latency.

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Section: Related Workmentioning

confidence: 99%

Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

Lim

Xiong

Kang

et al. 2022

IEEE Trans. Ind. Inf.

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“…The work in Reference [51], instead, defines a caching mechanism for RSUs only, based on service latency and content freshness. The authors clarify that the service latency shows how fast users get the contents after raising a request, while the freshness shows if the result is updated or not.…”

Section: Caching Policiesmentioning

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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“…Recently, several works have studied the optimization of AoI in vehicular networks [3]- [5]. In [3] and [4], the network dynamics are assumed to be known and in [5], they are estimated in a centralized manner without any consideration of future AoI. Yet, in a URLLC setting [6], [7], reliably learning and estimating the network dynamics with minimum communication overhead is desirable.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

et al. 2020

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In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to actively learn the network dynamics, estimate the vehicles' future AoI, and proactively allocate resources. Simulation results show a significant improvement in terms of AoI violation probability, compared to several baselines, with a reduction of at least 50%.Index Terms-Gaussian process regression (GPR), ultrareliable low-latency communication (URLLC), age of information (AoI), V2X.

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Towards Fresh and Low-Latency Content Delivery in Vehicular Networks: An Edge Caching Aspect

Cited by 63 publications

References 15 publications

When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

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

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

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