Smart collaborative video caching for energy efficiency in cognitive Content Centric Networks

Zhang, Mingchuan; Hao, Bowei; Song, Fei; Yang, Meiyi; Zhu, Junlong; Wu, Qingtao

doi:10.1016/j.jnca.2020.102587

Cited by 9 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the routing nodes in NDN have the crucial feature of being cached in network, the efficiency of video communication can be effectively improved in NDN [8]. In addition, by decoupling information content and location, NDN can well support the mobility of video communications [9].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

Hao

Wang

Zhang

et al. 2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

Named Data Networking (NDN) can effectively deal with the rapid development of mobile video services. For NDN, selecting a suitable forwarding interface according to the current network status can improve the efficiency of mobile video communication and can also avoid attacks to improve communication security. For this reason, we propose a stochastic adaptive forwarding strategy based on deep reinforcement learning (SAF-DRL) for secure mobile video communications in NDN. For each available forwarding interface, we introduce the twin delayed deep deterministic policy gradient algorithm to obtain a more robust forwarding strategy. Moreover, we conduct various numerical experiments to validate the performance of SAF-DRL. Compared with BR, RFA, SAF, and AFSndn forwarding strategies, the results show that SAF-DRL can reduce the delivery time and the average number of lost packets to improve the performance of NDN.

show abstract

Section: Introductionmentioning

confidence: 99%

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

Hao

Wang

Zhang

et al. 2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…As an important branch of reinforcement learning, policy gradient methods are widely used in many fields, 1 School of Information Engineering, Henan University of Science and Technology, Luoyang, China 2 such as video game, 8 AlphaGo, 9 news recommendation, 10 and network resource. 11,12 Policy gradient methods have a better performance in continuous action space (or higher dimensional space), and they can implement a randomized strategy. Policy gradient methods directly parameterize the policy and learn based on the strategy, which defines a policy p as a function with parameter u; the policy p is a probability distribution, indicating the probability distribution of choosing different actions in a certain state.…”

Section: Introductionmentioning

confidence: 99%

A randomized block policy gradient algorithm with differential privacy in Content Centric Networks

Wang

Zhao

et al. 2021

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the [Formula: see text]-privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment.

show abstract

“…With the rapid development of the Internet of Things (IoT), this paradigm opens a new era of smart devices and has become a comprehensive application of a new generation for information technology [1], [2]. On traditional networks, researchers have studied different research directions of the IoT, such as routing [3], buffering [4], privacy [5], computing offloading [6], perception and fusion [7], [8], identification [9], and optimization [10], etc., to improve the overall performance of the IoT. However, in the traditional network, it has a huge challenge due to the fundamental problems of the network architecture, for example, decoupling location and information and the node cannot caching content in-network [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…future Internet [14]. As the most promising future network architecture, ICN decouples information content from location, users can request through content names, and ICN also offers clear benefit that the network performance is improved by increasing the effect of the caching in-network [4], [15]. Information-Centric Internet of Things (IC-IoT) can apply the ICN's the characteristics of content caching to the IoT, which can reduce the energy loss and improve the performance of the network.…”

Section: Introductionmentioning

confidence: 99%

A Pre-Caching Strategy Based on the Content Relevance of Smart Device’s Request in Information-Centric IoT

2020

Self Cite

View full text Add to dashboard Cite

In recent years, investigations on various forms of solution for Internet of Things (IoT) paradigm opens the new era of smart devices. The Information-Centric Internet of Things (IC-IoT) can apply the characteristics of content caching which is a typical feature of Information-Centric Networking. Therefore, designing an efficient caching strategy for IC-IoT is able to improve the efficiency of smart devices. In this paper, we propose a Pre-Caching Strategy based on the Relevance of smart device request Content, namely PCSRC. After the first request of content chunk at a smart device side, PCSRC pre-caches the rest of the content chunks. There are two types of cached content chunks, that is, actual requested content chunks and pre-caching content chunks. Actual requested content chunks are pushed forward hop-by-hop according to the trend of the local activity. The popular content will be gradually pushed to the edge of IC-IoT. Furthermore, a sojourn time of a cached content chunk is employed to save storage space. The simulation results show PCSRC can reach 42.7% on the cache hit ratio with the different storage sizes. For the average hop count, PCSRC can increase about 42.2% than that of ProbCache. And PCSRC can improve about 47.9% than that of CEE for the average hop count.INDEX TERMS Information-centric networking, Internet of Things, content chunk, local activity, pre-caching.

show abstract

Smart collaborative video caching for energy efficiency in cognitive Content Centric Networks

Cited by 9 publications

References 38 publications

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

Stochastic Adaptive Forwarding Strategy Based on Deep Reinforcement Learning for Secure Mobile Video Communications in NDN

A randomized block policy gradient algorithm with differential privacy in Content Centric Networks

A Pre-Caching Strategy Based on the Content Relevance of Smart Device’s Request in Information-Centric IoT

Contact Info

Product

Resources

About