A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Su, Xing; Ren, Yiting; Cai, Zhi; Liang, Yi; Guo, Limin

doi:10.1109/tnsm.2022.3218017

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…The authors of [75] also propose a new routing approach for WSNs that aims to improve energy efficiency during information transmission. This approach allows nodes to dynamically select optimal neighboring nodes for energy-efficient transmission.…”

Section: Related Research Studiesmentioning

confidence: 99%

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Musaddiq,

Olsson,

Ahlgren

2023

Sensors

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Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.

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Section: Related Research Studiesmentioning

confidence: 99%

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Musaddiq,

Olsson,

Ahlgren

2023

Sensors

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“…The 3rd Generation Partnership Project (3GPP) organization has categorized WSN’s application domains into seven major classes, including security monitoring, tracking and positioning, intelligent payments, electronic healthcare, remote monitoring, smart metering, and electronic consumption. For instance, WSN can revolutionize utility meter reading processes for water and gas [ 7 ]. Replacing labor-intensive manual readings with intelligent metering, WSN enables automated data collection through network connectivity, facilitating scheduled data transmission and enabling automated detection and control.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Yuan,

Peng,

Yan

et al. 2024

Sensors

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The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

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“…In modern communication systems, accurate and reliable transmission of data is of paramount importance. The transmission of data, however, can be hindered by various factors, including noise, interference, and channel fading. , Therefore, error detection and correction techniques are necessary to ensure the accuracy and integrity of the transmitted data. One commonly used technique is a parity check, which adds a single parity bit to detect errors in data transmission.…”

Section: Introductionmentioning

confidence: 99%

Leveraging DNA-Based Nanostructures for Advanced Error Detection and Correction in Data Communication

et al. 2023

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This study demonstrates the implementation of the Hamming code using DNA-based nanostructures for error detection and correction in communication systems. The designed DNA nanostructures conduct logical operations to compute check codes and identify and correct erroneous data based on fluorescence signals. The execution of intricate DNA logic operations requires individuals with specialized training. By interpretation of the fluorescence signals generated by the DNA nanostructures, binary language can be extracted, effectively protecting data security. The findings highlight the potential of DNA as a versatile platform for reliable data transmission.

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A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Cited by 10 publications

References 44 publications

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks

Leveraging DNA-Based Nanostructures for Advanced Error Detection and Correction in Data Communication

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