DeepCachNet: A Proactive Caching Framework Based on Deep Learning in Cellular Networks

Rathore, Shailendra; Ryu, Jung Hyun; Sharma, Pradip Kumar; Park, Jong Hyuk

doi:10.1109/mnet.2019.1800058

Cited by 41 publications

(18 citation statements)

References 11 publications

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“…For instance, SNSs employ various application programming interfaces (APIs), such as Twitter API and Facebook API, to collect and manage their data for near-real-time data analysis purposes [25]. Our research [26] developed a mobile application to accumulate mobile data (i.e., values of mobile's sensors) for caching management. Data preprocessing: It is performed to eliminate noisy, unnecessary, or inappropriate data from the raw data.…”

Section: A Cloud Planementioning

confidence: 99%

Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT

2021

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Recently, many IoT applications, such as smart transportation, healthcare, and virtual and augmented reality experiences, have emerged with fifth-generation (5G) technology to enhance the Quality of Service (QoS) and user experience. The revolution of 5G-enabled IoT supports distinct attributes, including lower latency, higher system capacity, high data rate, and energy saving. However, such revolution also delivers considerable increment in data generation that further leads to a major requirement of intelligent and effective data analytic operation across the network. Furthermore, data growth gives rise to data security and privacy concerns, such as breach and loss of sensitive data. The conventional data analytic and security methods do not meet the requirement of 5G-enabled IoT including its unique characteristic of low latency and high throughput. In this paper, we propose a Deep Learning (DL) and blockchain-empowered security framework for intelligent 5G-enabled IoT that leverages DL competency for intelligent data analysis operation and blockchain for data security. The framework's hierarchical architecture wherein DL and blockchain operations emerge across the four layers of cloud, fog, edge, and user is presented. The framework is simulated and analyzed, employing various standard measures of latency, accuracy, and security to demonstrate its validity in practical applications.

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Section: A Cloud Planementioning

confidence: 99%

Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT

2021

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“…In [27], big data analytics were applied to predict content popularity maximise the cache hit ratio, that is, the ratio of content requests hit in cache nodes to the entire content requests. In [28], a collaborative filter was used to predict the content popularity to reduce the pressure of backhaul links. However, unlike our proposed CPP-CC policy, these predictive cache policies have the following problems: (1) reference [25,27] do not consider that user mobility can change content popularity since content popularity is location-dependent.…”

Section: Related Workmentioning

confidence: 99%

A Novel Cooperative Cache Policy for Wireless Networks

Kwong

Liu

et al. 2021

Wireless Communications and Mobile Computing

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Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

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“…A related work applying auto encoder in 5G network proactive caching can be found in Reference [74]. In Reference [75], two auto encoders are utilized for extracting the features of users and content, respectively. Then, the extracted information is explored to estimate popularity at the core network.…”

Section: Auto Encodermentioning

confidence: 99%

A Survey of Deep Learning for Data Caching in Edge Network

Wang

Friderikos

2020

Informatics

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The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.

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DeepCachNet: A Proactive Caching Framework Based on Deep Learning in Cellular Networks

Cited by 41 publications

References 11 publications

Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT

Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT

A Novel Cooperative Cache Policy for Wireless Networks

A Survey of Deep Learning for Data Caching in Edge Network

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