A Distributed Caching Scheme Using Non-Cooperative Game for Mobile Edge Networks

Gu, Huixian; Wang, Haijiang

doi:10.1109/access.2020.3009683

Cited by 15 publications

(4 citation statements)

References 49 publications

(48 reference statements)

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“…This facility allows unauthorized data (attacker data) to be stored in the cloud [21]. Each unauthorized data stored in the cloud will have a different token, which must be stored in the cloud [22]. The proposed clustering-based storage and search optimization approach (crosslayer analysis based on intrusion detection methods) was implemented on Network Simulator 3 (NS-3) [23].…”

Section: Proposed System Architecturementioning

confidence: 99%

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Venkatachalam¹,

Prabu²,

Kang³

et al. 2022

Computers, Materials &Amp; Continua

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Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput.

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Section: Proposed System Architecturementioning

confidence: 99%

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Venkatachalam¹,

Prabu²,

Kang³

et al. 2022

Computers, Materials &Amp; Continua

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“…Edge computing studies include those that focus on responsiveness [9], [18]- [21] and those that focus on reliability [22]. Responsiveness is enhanced by improving the caching algorithm of the objects in the data cache.…”

Section: Related Workmentioning

confidence: 99%

“…In edge computing, a design that emphasizes responsiveness to many sensor nodes has been proposed for the purpose of real-time response at a short distance to sensor nodes existing on the edge side close to the device [8]. As one of the responsiveness improvement technologies, a mechanism has been proposed in which a cache is deployed at the edge to effectively cache data up to the cloud on an edge server [9].…”

Section: Introductionmentioning

confidence: 99%

Fogcached-Ros: DRAM/NVMM Hybrid KVS Server with ROS Based Extension for ROS Application and SLAM Evaluation

Higashi

Ishiwata²,

Ohkawa

et al. 2021

IEICE Trans. Inf. & Syst.

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“…Some resourcedevouring approaches, such as higher frequency reuse, a larger scale of antennas or setting more bandwidth, can tackle this problem by increasing the capacity of cellular networks, but most of them fail to offer a durable solution in terms of scalability, costs and flexibility [2]. On the other hand, some studies [3], [4] point out that a tremendous data load comes from the repeated requests for a few same popular targets, especially the multimedia services at the edge, so does the scenario of AI service, in which the same AI samples or trained models usually may devote to numerous applications [5]. Therefore, aiming at dealing with these repetitive actions by storing part of fashionable content (e.g., deep neural network models, videos) at the network edge, it can obviously alleviate the backhaul traffic burden caused by the data explosion and greatly reduce the transmission delay or other issues [6].…”

Section: Introductionmentioning

confidence: 99%

AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

Zhu¹,

Li²,

Ding³

et al. 2022

Preprint

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Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people's requirements about latency, the network architecture like informationcentric network (ICN) intends to proactively keep limited popular content at the edge of network based on predicted results. Meanwhile, the interactions between the content (e.g., deep neural network models, Wikipedia-alike knowledge base) and users could be regarded as a dynamic bipartite graph. In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. Furthermore, in order to have deeper insights into the dynamics within the evolving graph, we propose an age of information (AoI) based attention mechanism to extract valuable historical information while avoiding the problem of message staleness. Combining this aforementioned prediction model, we also develop a cache selection algorithm to make caching decisions in accordance with the prediction results. Extensive results demonstrate that our model can obtain a higher prediction accuracy than other stateof-the-art schemes in two real-world datasets. The results of hit rate further verify the superiority of the caching policy based on our proposed model over other traditional ways.

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A Distributed Caching Scheme Using Non-Cooperative Game for Mobile Edge Networks

Cited by 15 publications

References 49 publications

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Fogcached-Ros: DRAM/NVMM Hybrid KVS Server with ROS Based Extension for ROS Application and SLAM Evaluation

AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

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