A Glider-Assist Routing Protocol for Underwater Acoustic Networks With Trajectory Prediction Methods

Su, Yishan; Zhang, Lin; Li, Yun; Xing, Yufeng

doi:10.1109/access.2020.3015856

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…The purpose of [2] is to investigate existing network technologies and their applicability in underwater acoustic channels. [12] considered that gliders are regarded as special sensor nodes in the current UIoT and proposed a glider-assist routing scheme to improve the connectivity of the hybrid network. Vinayak Khajuria et al introduced 3D-underwater wireless sensor networks [13].…”

Section: A Internet Of Things (Iot) and Uiotmentioning

confidence: 99%

Information-Centric Wireless Sensor Networking Scheme With Water-Depth-Awareness Content Caching for Underwater IoT

Li³

et al. 2022

IEEE Internet Things J.

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The existing underwater Internet of Things (UIoT) is based on IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this paper, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Secondly, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Thirdly, as the water depth is deeper, it becomes more difficult to replace sensor battery. To save energy consumption of deep-water sensors, water-depth-aware caching mechanism is proposed based on water depth, popularity and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency.

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Section: A Internet Of Things (Iot) and Uiotmentioning

confidence: 99%

Information-Centric Wireless Sensor Networking Scheme With Water-Depth-Awareness Content Caching for Underwater IoT

Li³

et al. 2022

IEEE Internet Things J.

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“…Efficient methodologies are needed for prediction and measurement of the ocean environment and water column parameters. However, algorithms such as VODA [1], RVPR [2], glider-assist routing [3], DVOR [4], DQELR [5], RCAR [6], DSR-SDN [7], EBOR [8] are having efficient routing in different ocean environmental conditions, whereas for water column variations such as geometric spread, sedimentation drift, and Doppler effect has only few UASN algorithms such as COCAN, LOCAN [9] are proposed. In UASN, acoustic signals perform better than extreme Low frequencies (ELF) due to less attenuation in underwater.…”

Section: Introductionmentioning

confidence: 99%

Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN

Amirthavalli¹,

Ramya²,

Shanker³

2022

Computer Systems Science and Engineering

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In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected in each node by various water column environmental factors such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity. High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predicts propagation delay of each node in UASN using temperature, salinity, depth, dissolved gases dataset. Dissolved gases, rotational and divergent winds, and stress plays a major problem in UASN, which increases propagation delay and energy consumption. Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm (CVOA) routing is performed on the selected nodes. The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes, based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds. From simulation results, CVOA Algorithm performs better than traditional DF and LION algorithms.

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“…Prediction or measurement of the ocean environment and water column parameters need efficient methods. However, algorithms such as VODA [1], RVPR [2], glider-assist routing [3], DVOR [4], DQELR [5], RCAR [6], DSR-SDN [7], EBOR [8] developed for efficient routing in different ocean environmental conditions, whereas for water column variations such as geometric spread, sedimentation drift, Doppler effect few UASN algorithms proposed such as COCAN, LOCAN [9]. In UASN, acoustic signal performs better than extreme Low frequencies (ELF) due to less attenuation in underwater.…”

Section: Introductionmentioning

confidence: 99%

Modified Mackenzie Sound Equation and CVOA Algorithm Based UASN Routing in Dissolved Gases with Divergent Wind Environment to Reduce Propagation Delay

Amirthavalli¹,

Shanker²

2021

Preprint

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In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected through various water column environment effects in each node such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of packets and high dissolved gases in the water environment increase sound velocity. High dissolved gases and sound velocity environment in water column provide high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predict propagation delay of each node in UASN from dataset which consists of Mackenzie Sound equation calculated sound velocity based on temperature, salinity, depth, dissolved gases. Dissolved gases, rotational and divergent winds, and stress play major problems in UASN, increases propagation delay and energy consumption. Predicted values from GPR and GROM lead to node selection and among selected nodes Corona Virus Optimization Algorithm (CVOA) routing is performed. The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds. From simulation results, CVOA Algorithm performs better than traditional DF and LION algorithms.

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A Glider-Assist Routing Protocol for Underwater Acoustic Networks With Trajectory Prediction Methods

Cited by 10 publications

References 31 publications

Information-Centric Wireless Sensor Networking Scheme With Water-Depth-Awareness Content Caching for Underwater IoT

Information-Centric Wireless Sensor Networking Scheme With Water-Depth-Awareness Content Caching for Underwater IoT

Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN

Modified Mackenzie Sound Equation and CVOA Algorithm Based UASN Routing in Dissolved Gases with Divergent Wind Environment to Reduce Propagation Delay

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