Hidden Markov Model based Trust Management Model for Underwater Wireless Sensor Networks

SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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“…Arifeen et al 115 developed an HMM‐based trust management approach. It can efficiently address and model the dynamic behavior of sensor nodes.…”

Section: Uwsns—attacksmentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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“…3 [78,321,322] Papers discussing trust management in UIoT networks. 19 [273,275,276,314,[323][324][325][326][327][328][329][330][331][332][333][334][335][336][337] Figure 15. Results based on the systematic analysis of security attacks and management.…”

Section: Q4: What Are the Findings Based On The Existing Research Work?mentioning

confidence: 99%

A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things

Kim

et al. 2021

Sensors

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Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning systems. In a constrained UIoT environment, communication media such as acoustic, infrared (IR), visible light, radiofrequency (RF) and magnet induction (MI) are generally used to transmit information via digitally linked underwater devices. However, each medium has its technical limitations: for example, the acoustic medium has challenges such as narrow-channel bandwidth, low data rate, high cost, etc., and optical medium has challenges such as high absorption, scattering, long-distance data transmission, etc. Moreover, the malicious node can steal the underwater data by employing blackhole attacks, routing attacks, Sybil attacks, etc. Furthermore, due to heavyweight, the existing privacy and security mechanism of the terrestrial internet of things (IoT) cannot be applied directly to UIoT environment. Hence, this paper aims to provide a systematic review of recent trends, applications, communication technologies, challenges, security threats and privacy issues of UIoT system. Additionally, this paper highlights the methods of preventing the technical challenges and security attacks of the UIoT environment. Finally, this systematic review contributes much to the profit of researchers to analyze and improve the performance of services in UIoT applications.

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“…However, the proposed work did not provide memory overhead and was not suitable for big misbehavior. For underwater wireless sensor networks, Ref [ 24 ] proposed a Hidden Markov Model (HMM) based trust management model for measuring the trustworthiness of the sensor nodes based on their dynamic behaviors. The trust is evaluated by observing the interaction quality between one node and its neighbours.…”

Section: Related Workmentioning

confidence: 99%

A Semantic-Based Belief Network Construction Approach in IoT

Dong

Wan

Yue

2020

Sensors

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Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

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Hidden Markov Model based Trust Management Model for Underwater Wireless Sensor Networks

Cited by 11 publications

References 12 publications

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things

A Semantic-Based Belief Network Construction Approach in IoT

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