A Scheme for Delay-Sensitive Spatiotemporal Routing in SDN-Enabled Underwater Acoustic Sensor Networks

Lin, Chuan; Han, Guangjie; Guizani, Mohsen; Bi, Yuanguo; Du, Jian Hua

doi:10.1109/tvt.2019.2931312

Cited by 38 publications

(10 citation statements)

References 44 publications

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“…Hence computational intelligence approaches provides solutions. This may happen because of changing characteristics of underwater environment or the acoustic system [18] – [23] . Some of the characteristics to make a tough task easier for the collection of data deep into the water have been discussed as follows: There are very few chances of using static topology in UWSN because most of the topologies are dynamic in nature as there are regular deviations in the network topology of the UWSN.…”

Section: Data Collection In Uwsnmentioning

confidence: 99%

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

et al. 2020

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Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today's time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed. INDEX TERMS Acoustic sensor network, coronavirus (COVID-19), computational intelligence, routing, underwater sensor network DIVYA ANAND received the Ph.D. degree in computer science and engineering and Masters of Technology in information security from the Lovely Professional University. She has expertise in Teaching, Entrepreneurship and Research and Development. She is currently an Assistant Professor with the Lovely Professional University. She has published over 20 conferences and journal articles. Her research interests include networks security, bioinformatics, machine learning, gene identification, big data analytics and computational models.

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Section: Data Collection In Uwsnmentioning

confidence: 99%

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

et al. 2020

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“…The experimental results revealed that it preserved a higher data transmission rate and a lower delay. C. Lin et al [46] complemented a software-defined routing scheme for UAN and realized an accurate estimation of the underwater acoustic channel. J. Wang et al [47] set up a large-scale UAN based on SDN.…”

Section: ) Advancement In the High-performance Communication Of Softmentioning

confidence: 99%

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

et al. 2020

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Underwater acoustic network (UAN) is a lightweight system used in inland waterway systems. Software-defined networking (SDN) is a promising approach to improve the supervision abilities and integration services. This paper introduces SM-UAN, an SDN-based UAN with multi-controllers. Firstly, a hierarchical framework of SM-UAN is offered. Secondly, a multi-flow table structure and a multi-flow processing method is built. Followed by, a distributed deployment model of controllers and a load balancing mechanism are established. Finally, we construct an experimental scenario on Mininet with WOSS-NS3. The simulation demonstrates that the compression ratio is promoted to 24.1%, the compression ratio 29.4%, the packet forwarding rate 14.7% and the processing delay 31.2%, when comparing SM-UAN with the item-by-item matching (IM) system. For the load balancing mechanism, it shows that the maximum load for a master controller is balanced to 0.55, while the minimum load for a slave controller 0.46, which proves that a fair load distribution achieves. In addition, SM-UAN is compared with the traditional single-sink-based UAN (TS-UAN), the traditional multi-sink-based UAN (TM-UAN), and the SDN-based UAN with a single controller (SS-UAN). The results reveal that the survival time of SM-UAN is extended to 32.7%, 24.9%, and 21.6%, respectively. Also, the bit error rate (BER) of that is less than 10-4. In conclusion, advantages of SM-UAN have been highlighted, which provides theoretical support for inland waterway systems.

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“…U DERWATER acoustic sensor networks (UASNs) are expected to play an increasingly important role in many fields, such as marine environmental monitoring, offshore exploration, auxiliary navigation, tsunami warning and marine military operations [1][2][3][4]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks

Han

Jiang

et al. 2022

IEEE Trans. on Mobile Comput.

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Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

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A Scheme for Delay-Sensitive Spatiotemporal Routing in SDN-Enabled Underwater Acoustic Sensor Networks

Cited by 38 publications

References 44 publications

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

SM-UAN: A Software-Defined Underwater Acoustic Network of Multi-Controllers for Inland Waterway Systems

A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks

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