Optimal caching policy for wireless content delivery in D2D networks

Ali, Ahmed S.; Mahmoud, Korany R.; Naguib, Khaled M.

doi:10.1016/j.jnca.2019.102467

Cited by 20 publications

(17 citation statements)

References 20 publications

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“…In this study, three different simulation scenarios were set up and were compared with the betweenness-based algorithm (referred to as the VC model) [35]. e FIR-based model proposed in this study and the VC model were used for identifying the Input: node set G Output: structural hole constraint coefficient, L ni , of all nodes in G (1) for i ∈ G: //G is the node set of the opportunistic network (2) Calculate k (i) (3) for i ∈ G: (4) Calculate Q (i) (5) for i ∈ G: (6) for j ∈ r(i): //r(i) is the set of all neighboring nodes of node i (7) Calculate P ij (8) for q ∈ (r(i) ∩ r(j)): (9) Calculate P iq and P qj (10) Calculate RC i (11) for i ∈ G: (12) Calculate L i (13) for i ∈ G: (14) Calculate L ni (15) return Ln ALGORITHM 1: Algorithm for calculating the importance of structural holes.…”

Section: Methodsmentioning

confidence: 99%

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Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Wang

Zhang

et al. 2021

Security and Communication Networks

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An opportunistic network is a special type of wireless mobile ad hoc network that does not require any infrastructure, does not have stable links between nodes, and relies on node encounters to complete data forwarding. The unbalanced energy consumption of ferry nodes in an opportunistic network leads to a sharp decline in network performance. Therefore, identifying the ferry node group plays an important role in improving the performance of the opportunistic network and extending its life. Existing research studies have been unable to accurately identify ferry node clusters in opportunistic networks. In order to solve this problem, the concepts of k-core and structural holes have been combined, and a new evaluation indicator, namely, ferry importance rank, has been proposed in this study for analyzing the dynamic importance of nodes in a network. Based on this, a ferry cluster identification model has been designed for accurately identifying the ferry node clusters. The results of the simulations conducted for verifying the performance of the proposed model show that the accuracy of the model to identify the ferry node clusters is 100%.

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Section: Methodsmentioning

confidence: 99%

“…Ferry nodes moving between different regions connect to different areas. At present, research studies on ferry nodes mainly include routing algorithms based on ferry nodes [3][4][5][6][7][8][9][10][11][12], ferry node motion path planning [13][14][15][16][17][18][19], and ferry node network signal coverage [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Wang

Zhang

et al. 2021

Security and Communication Networks

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“…Likewise, in [33], the authors VOLUME , presented a developing learning-based content caching strategy in edge networks which flexibly analyzes time-varying content popularity and when the cache is filled, it determines which contents should be refreshed. Besides, in [34], authors present an incentive caching policy in which networks incorporate preferences of the users and group mobility together with Device-to-Device (D2D) communication for solving the caching problem in mobile devices. Also, authors in [18] use the echo state network and the long short-term memory to predict the content popularity, along with the mobility of the users in the D2D communication technology.…”

Section: Related Workmentioning

confidence: 99%

Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning

2022

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The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars for improving quality-of-experience (QoE) and content retrieval cost in this work. We develop a low-complexity content popularity prediction mechanism in a hierarchical federated setting. In particular, we use a self-attention technique with an LSTM-based prediction mechanism to extract local content popularity patterns in self-driving cars. However, the local contents will not be sufficient to satisfy the passenger's requirements. Hence, using the popular contents of other self-driving cars will solve the requirement constraint but poses some privacy issues. We use the privacy-preserving decentralized model training framework of Federated Learning (FL) to tackle this issue. Specifically, we deploy the hierarchical Federated Averaging (FedAvg) algorithm on local models obtained from self-driving cars to develop a regional and global content popularity prediction model at RSU and MBS, respectively. Extensive simulations on real-world datasets show the proposed approach improves cache space utilization by maximizing the local cache hit ratio, and further, minimizes the content retrieval cost for self-driving cars as compared with alternative methods.INDEX TERMS proactive content caching, multi-access edge computing, federated learning, self-attention mechanism, recurrent neural network.

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“…From the wide variety of metrics available for computing significance of caching strategies [ 61 ], this work evaluates the performance of proposed strategy based on the following:…”

Section: Evaluation Scenariomentioning

confidence: 99%

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Gupta

Rani

Ahmed³

et al. 2021

Sensors

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The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

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Optimal caching policy for wireless content delivery in D2D networks

Cited by 20 publications

References 20 publications

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning

Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

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