Resilience of thick-line WSN based surveillance systems under irregular crossings

Arfaoui, Imen; Boudriga, Noureddine; Trimche, Khalifa

doi:10.1109/apcc.2016.7581517

Cited by 1 publication

(1 citation statement)

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“…Moreover, how the system can be adapted to networks with different data transmission is an interesting problem that can be studied in the future. Our future work with this proposal will be the total integration with the routing module and its application to other types of data form environmental monitoring in wireless sensor networks, 21 especially, the ones where video transmission is required 22 and Internet of things. 23 This may imply the definition of new actions, states, and data structures.…”

Section: Discussionmentioning

confidence: 99%

Adapting reinforcement learning for multimedia transmission on SDN

Rego

Sendra

García

et al. 2019

Trans Emerging Tel Tech

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Multimedia transmissions require a high quantity of resources to ensure their quality. In the last years, some technologies that provide a better resource management have appeared. Software‐defined networks (SDNs) are presented as a solution to improve this management. Furthermore, by combining an SDN with artificial intelligence (AI) techniques, networks are able to provide a higher performance using the same resources. In this paper, a redefinition of reinforcement learning is proposed. This model is focused on multimedia transmission in an SDN environment. Moreover, the architecture needed and the algorithm of the reinforcement learning are described. Using the Openflow protocol, several sample actions are defined in the system. Results show that using the system users perceive an increase in the image quality three times better. Moreover, the loss rate is reduced more than half the value of losses recorded when the algorithm is not applied. Regarding bandwidth, the maximum throughput increases from 987.16 kbps to 24.73 Mbps while the average bandwidth improves from 412.42 kbps to 7.83 Mbps.

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Section: Discussionmentioning

confidence: 99%