A Cooperative Routing Protocol Based on Q-Learning for Underwater Optical-Acoustic Hybrid Wireless Sensor Networks

Shen, Zhongwei; Yin, Hongxi; Jing, Lianyou; Liang, Yanjun

doi:10.1109/jsen.2021.3128594

Cited by 43 publications

(10 citation statements)

References 23 publications

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“…In recent years, multi-modal communication has become a research topic to improve network performance and optimize data transmission in various marine application scenarios. Commonly adopted multi-modal technologies include acoustic multi-modal communication and acoustic–optical hybrid communication [ 13 , 22 , 23 , 24 ]. Among them, the acoustic multi-modal communication is constructed by a set of UAC modems working on different frequency bands [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this protocol, the reliability and delay of data transmission are optimized by UAC modems in multiple frequency bands. To explore the advantages of UAC and UOC during data transmission, Shen et al proposed an acoustic–optical multi-modal routing scheme based on packet size and link adaptation, which reduces packet loss and end-to-end delay [ 23 ]. However, the challenge of unbalanced energy consumption still exists in the multi-hop underwater networks.…”

Section: Related Workmentioning

confidence: 99%

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AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Luo

et al. 2023

Sensors

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Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Luo

et al. 2023

Sensors

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“…Offloading performance analysis as a function of data, number of servers and available bandwidth [132] Data offloading framework Analysis of total payment for service request and remaining data after expiration of transmission time Q-learning [133] Routing protocol QoS analysis with the proposed, VBF, QELAR, MU-RAO schemes [134] Resource management schemes Learning the behavior of the primary user and provide good channel to the secondary user with satisfactory QoS [135] Spectrum access Throughput, power efficiency and collision probability analysis [136] Random access approach Analysis of system throughput, effect of clustering and cluster size and frame size adaptation [137] Packet transmission scheduling algorithm Analysis of the number of packet transmission with and without SIC [138] Cluster formation in CR-ad hoc networks Network lifetime extension, reliable service provision and interference mitigation [14] Power control scheme for wireless energy harvesting…”

Section: Algorithmsmentioning

confidence: 99%

“…The Q-function is defined in terms of data aggregation efficiency, energy consumption of the sensors and their residual energy. In [133], the Qlearning based routing protocol performs the data forwarding operation by considering the received rewards, which depends on the evaluation of possible forwarding operation effects as well as routing performances before choosing the receiving network nodes. A resource allocation methodology is proposed in [134], where Q-learning-based channel assignment supports energy harvesting procedure and learning the random behavior of the primary user in the cognitive wireless network.…”

Section: Algorithmsmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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“…Therefore, when an AUV collects data at one node point, the UWSN node can be optimized using this method [22]. Additionally, the placement of nodal points can be optimized with a multi-layer architecture within one operational area [23].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Underwater Wireless Power and Data Transfer System

Wibisono,

Alsharif,

Song

et al. 2024

IEEE Access

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This research extensively explores contemporary techniques in Underwater Wireless Power and Data Transfer (WPDT), as found in recent literature over the past 5 years. These techniques involve Magnetic Induction (MI), acoustic communication, and optical communication. The study aims to evaluate transmission efficiency, propose solutions to overcome range limitations, facilitate integration, and precisely map multilayer networks. The research methodology includes proposed solutions to address the limitations of each transmission technique, multilayer network mapping, and model validation. Evaluation is conducted on the relative efficiency of each method, including power loss at each layer, the connection matrix between layers, and transmission speed. Simulations reveal power loss at nodes in each layer and the connection matrix between layers. The power loss at each node shows random values, providing a realistic aspect close to real-world scenarios, offering in-depth insights into the characteristics and performance of multilayer networks in an underwater context. The research series, involving literature elaboration, technological approaches, illustrations, and simulations, demonstrates the effectiveness of the proposed model. Simulation results indicate the potential of this model in real-world scenarios, with tolerable power loss values, suggesting that multilayer networks could be a solution to classic challenges in underwater power and data transmission. The hope of this preliminary study is to provide new insights and make a significant contribution to understanding and designing reliable underwater power and data transfer systems in the future.

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A Cooperative Routing Protocol Based on Q-Learning for Underwater Optical-Acoustic Hybrid Wireless Sensor Networks

Cited by 43 publications

References 23 publications

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

A Survey on Underwater Wireless Power and Data Transfer System

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