Dynamic link utilization empowered by reinforcement learning for adaptive storage allocation in MANET

Anand, R. P. Prem; Senthilkumar, V.; Kumar, Gokul; Rajendran, A.; Rajaram, A.

doi:10.1007/s00500-023-09281-8

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Link-prediction routing is based on the estimation of a path between the sender and the destination to forward information [16]. The most recurrent techniques applied to predict stable links include periodic patterns, decision trees, complex network predictions, deep learning, and reinforcement learning techniques [17].…”

Section: Related Workmentioning

confidence: 99%

ML-Enhanced Live-Video Streaming in Offline MANETs: An Applied Approach

Jesús-Azabal,

Soares,

Galán-Jiménez

2024

Preprint

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Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

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Section: Related Workmentioning

confidence: 99%

ML-Enhanced Live-Video Streaming in Offline MANETs: An Applied Approach

Jesús-Azabal,

Soares,

Galán-Jiménez

2024

Preprint

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show abstract

“…Link-prediction routing is based on the estimation of a path between the sender and the destination to forward information [20]. The most recurrent techniques applied to predict stable links include periodic patterns, decision trees, complex network predictions, deep learning, and reinforcement learning techniques [21].…”

Section: Related Workmentioning

confidence: 99%

ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach

Jesús-Azabal,

Soares,

Galán-Jiménez

2024

Electronics

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Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions is challenging due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts in which an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal is implemented and assessed in a laboratory context, in which the model performance and QoS metrics are analyzed. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% accuracy. Also, the obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

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Energy-aware multi-attribute trust modal for secure MANET-IoT environment

Aravindan,

Rajaram

2024

Multimed Tools Appl

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Dynamic link utilization empowered by reinforcement learning for adaptive storage allocation in MANET

Cited by 5 publications

References 15 publications

ML-Enhanced Live-Video Streaming in Offline MANETs: An Applied Approach

ML-Enhanced Live-Video Streaming in Offline MANETs: An Applied Approach

ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach

Energy-aware multi-attribute trust modal for secure MANET-IoT environment

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