Reinforcement learning–enabled efficient data gathering in underground wireless sensor networks

Deng, Zhidong; Zhou, Zhangbing; Wang, Shangguang; Liu, Bo; Gaaloul, Walid

doi:10.1007/s00779-020-01443-x

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…The underground sensor nodes constantly need to sense due to precipitation and weather extremes, making remote contact far more difficult than in traditional over-the-air sensor networks. Zhao et al [14] suggested using sensors to detect strategies to achieve accurate and resource-efficient data collection in complex WUSNs to reduce the path loss through sensory information transfer, energy constraints, and device traffic shaping. They also examined the impact of underground conditions on wireless communications, route possibility, power production, and data aggregation functions in terms of prompting questions about security and availability.…”

Section: Related Workmentioning

confidence: 99%

“…The gathered information is regularly forwarded to SN via fp under the constraints of Poss way , Eng way , and Ebl way to obtain reliability and energy efficiency, with the absolute maximum minimum cost method. wusn x.energy > engcst (wusn x ) £ fp × V path (14) fpPoss way (wusn x , wusn x+1 ) ≥ fp × W way (15) wusn x , wusn x+1 £ fp × W way (16) fpC way (wusn x , wusn x+1 ) = 1 (17) avg (fpPoss way ) ≥ thsd ap (18)…”

Section: Based Transmission Path Selection In Wusnmentioning

confidence: 99%

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A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Kanthavel

Dhaya

Khalaf

et al. 2022

Sensors

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Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

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Section: Related Workmentioning

confidence: 99%

Section: Based Transmission Path Selection In Wusnmentioning

confidence: 99%

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Kanthavel

Dhaya

Khalaf

et al. 2022

Sensors

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“…Meanwhile, many industrial solutions are offered, among them WiTricity has been considered one of the most popular solutions [67]. Zhao et al [68] proposed a reinforcement learning-enabled efficient data gathering model for WUSNs by applying an optimal transmission policy by considering the factors like path loss of sensory data transmission, energy limitations, and network load balancing. In addition, the combination of WPT and NFC leading to the SWIPT principle sounds even more advantageous to the IoT [69].…”

Section: Prominent MI Signal Transmissionmentioning

confidence: 99%

Magnetic Induction Technology-Based Wireless Sensor Network for Underground Infrastructure, Monitoring Soil Conditions, and Environmental Observation Applications: Challenges and Future Aspects

Singh

et al. 2022

Journal of Sensors

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Wireless sensor networks (WSNs) especially with sensor nodes communicating with each other in medium other than air have been naive area of research since the last few years. In comparison to underwater communication, wireless underground sensor networks (WUSNs) are now being used in a large number of applications ranging from environmental observation, estimating chances of earthquake, communicating in underground tunnels or mines, and infrastructure monitoring to soil monitoring for agricultural purposes. In spite of all such promising applications, due to harsh and dynamically changing soil characteristics including soil type, water content in soil, and soil temperature, underground communication with conventional electromagnetic (EM) wave-based technology could not prove to be feasible for long-distance communication. Alternatively, due to magnetic permeability of soil being similar to air, magnetic induction- (MI-) based approach was adopted using magnetic coils as antenna for sensor nodes. Subsequently, MI waveguide and 3D coil mechanisms were considered to improve the system efficiency. Attributing to different characteristics of underlying transmission channels, communication protocols as well as architecture of MI-based WUSNS (MI-WUSNs) have been developed with different approaches. In this review paper, in addition to the latest advancements made for MI-WUSNs, closely associated areas of MI-WUSNs have also been explored. Additionally, research areas which are still open to be worked upon have been detailed out.

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“…In the scheme, the Micro Mobile Data Centers are designed and later selected to connect the huge number of intelligent sensing devices. By using reinforcement learning, Zhao et al [6] derived an adaptive transmission policy for underground sensors to efficiently use their energy and avoid transmitting sensory data in unreliable paths under a dynamic environment.…”

Section: Accepted Articlesmentioning

confidence: 99%

Active crowd sensing

Wang

Espada

2021

Pers Ubiquit Comput

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Reinforcement learning–enabled efficient data gathering in underground wireless sensor networks

Cited by 11 publications

References 36 publications

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Magnetic Induction Technology-Based Wireless Sensor Network for Underground Infrastructure, Monitoring Soil Conditions, and Environmental Observation Applications: Challenges and Future Aspects

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