A SMDP-based forwarding scheme in named data networking

Yao, Jinfa; Yin, Baocai; Tan, Xiaobin

doi:10.1016/j.neucom.2018.03.057

Cited by 12 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MDP algorithm is used to decrease RTT values and, thus, fulfill the delay requirements of delay-intolerant applications in the IoT-NDN environment. In the study conducted by Yao et al [18], sending requests in the NDN network is modeled as a semi-Markov decision problem (SMDP). An adaptive forwarding strategy is then developed based on the SMDP theory and the unpredictability of network requests.…”

Section: Related Workmentioning

confidence: 99%

“…In the proposed method, bandwidth is considered a positive parameter, and delay and hop count are considered negative parameters. Based on equations ( 17) and (18), equations (19) and (20) will be used to calculate the sum of the + and -indicators.…”

Section: Rating Each Of the Interfacesmentioning

confidence: 99%

“…The method's performance is evaluated in an ABILINE topology according to Figure 5 In performing simulations of content-based networking, a set of four cache/content types are selected and placed in the nodes of the network. The traffic pattern follows the Zipf [18] distribution with an interest rate of 200-4000 Interests/sec. The cache hit ratio ranges from 1 to 60 percent.…”

Section: Table 7 Simulation Scenariosmentioning

confidence: 99%

“…In NDN, the data delivery process starts by initiating a request for the desired content, which leads to the discovery of the required information. In NDN, the request is made hop by hop, rather than end to end [18]. As a result, routers have to choose suitable interfaces during the request process based on network conditions by making forwarding decisions [19].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

FSCN: a novel forwarding method based on Shannon entropy and COPRAS decision process in named data networking

et al. 2023

View full text Add to dashboard Cite

One of the next-generation Information-Centric Networking (ICN) architectures for the future Internet is Named Data Networking (NDN). The appropriate output interface (forwarding) has recently emerged as a key challenge affecting the performance of this network and has received a lot of attention from researchers due to its importance. Although efforts have been made through the methods introduced so far to address this challenge, the neglect of effective network load balancing parameters, non-dynamic weighting, and the use of only a few criteria have been suggested for selecting the output interface have made it vital to further improve the network delay, congestion, and ultimately the network's throughput. Regarding this matter, a new strategy is presented in this article based on the COPRAS Decision-Making Process with the dynamic weighting of Shannon's entropy called FSCN for transmitting request packets through the optimal output interface. The essential parameters identified in the suggested approach such as bandwidth, delay, and the number of hops can be dynamically weighted using the Shannon entropy technique and conditions. Then, the interfaces are scored using the COPRAS method, and the suitable output interface is ascertained. The advantages of the proposed method include considering influential criteria to characterize the path's performance and the dynamic weighting of criteria. The simulation outcomes in NDNsim demonstrated enhancements in critical parameters, including interest throughput, satisfaction ratio, packet drop, and delivery time when compared to comparable approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Rating Each Of the Interfacesmentioning

confidence: 99%

Section: Table 7 Simulation Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FSCN: a novel forwarding method based on Shannon entropy and COPRAS decision process in named data networking

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As the advantages of reinforcement learning gradually manifest, some researchers use reinforcement learning to find the optimal adaptive forwarding strategy. Yao et al [25] proposed an adaptive forwarding strategy called SMDPF.…”

Section: Related Workmentioning

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

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

A source-driven reinforcement learning-based Data reply strategy to reduce communication overhead in Named Data Networks (NDN)

2021

View full text Add to dashboard Cite

A SMDP-based forwarding scheme in named data networking

Cited by 12 publications

References 10 publications

FSCN: a novel forwarding method based on Shannon entropy and COPRAS decision process in named data networking

FSCN: a novel forwarding method based on Shannon entropy and COPRAS decision process in named data networking

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

A source-driven reinforcement learning-based Data reply strategy to reduce communication overhead in Named Data Networks (NDN)

Contact Info

Product

Resources

About