Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

Tapwal, Riya; Gupta, Nitin; Xin, Qin

doi:10.36227/techrxiv.12091410.v1

Cited by 2 publications

(2 citation statements)

References 52 publications

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“…Similarly, An efficient caching framework for IoT applications in order to ensure quick content retrieval and reduced latency was introduced in [30]. A review of different machine learning approaches for caching IoT data at fog nodes was carried out in [31].…”

Section: A Related Workmentioning

confidence: 99%

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

Öztürk

Abubakar²,

Rais

et al. 2022

IEEE Internet Things J.

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A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-theart approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

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Section: A Related Workmentioning

confidence: 99%

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

Öztürk

Abubakar²,

Rais

et al. 2022

IEEE Internet Things J.

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“…Therefore, the application of ML techniques in intelligent and optimal edge caching decisions is essential to meet the requirements of multimedia and URLLC content delivery [56], [57]. ML techniques employed towards optimal edge caching decisions are driving technologies and use cases with low latency requirements, e.g., autonomous vehicles, online gaming, virtual and augmented reality, dynamic content delivery, video streaming, IoT, and smart cities [58], [59], [23], [60], [48].…”

Section: A Motivationmentioning

confidence: 99%

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

Cited by 2 publications

References 52 publications

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

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