A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Ru, Huang; Chu, Xiaoli; Zhang, Jie; Hu, Yu; Yan, Huaicheng

doi:10.18494/sam.2019.2298

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…By designing a quadruple reward function and combining with the Q-Learning algorithm [23], it effectively improves the energy efficiency of the SDWSN and prolongs the lifetime of network nodes. Some routing planning algorithms are proposed based on Q-Learning, such as the QELAR model proposed by Hu et al [24], the SDWSN model proposed by Huang et al [25], and the DACR routing algorithm proposed by Razzaque et al [26]. These models are designed to improve the energy efficiency of nodes and the quality of service (QoS) of WSN.…”

Section: Related Workmentioning

confidence: 99%

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

et al. 2022

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Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.

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Section: Related Workmentioning

confidence: 99%

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

et al. 2022

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“…Mobile monitoring devices have many research challenges aimed at ultra-low power consumption demands [19][20][21][22]. To address these constraints, there is a significant need for smart software control algorithms using machine learning principles for automated and intelligent device management [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Prauzek¹,

Paterova²,

Konecny³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

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ASTPPO: A proximal policy optimization algorithm based on the attention mechanism and spatio–temporal correlation for routing optimization in software-defined networking

Chen

Xuefeng

Wang

et al. 2023

Peer-to-Peer Netw. Appl.

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A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Cited by 7 publications

References 41 publications

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

ASTPPO: A proximal policy optimization algorithm based on the attention mechanism and spatio–temporal correlation for routing optimization in software-defined networking

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