UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

Rahmati, Mehdi; Pompili, Dario

doi:10.1109/mass.2019.00052

“…In recent years sensor networks have been deployed to tackle some of the most fundamental problems facing human beings such as disaster warnings, climate change, and renewable energy. These emerging scientific applications include underwater or ocean sensor networks [9,22,28,35,43,60], wind and solar harvesting [34,40], seismic sensor networks [42,55], and monitoring of volcano eruption and glacial melting [18,47]. One common characteristic of these applications is that they are all deployed in challenging environments such as in remote or inhospitable regions, or under extreme weather, to continuously collect large volumes of data for a long period of time.…”

Section: Introductionmentioning

confidence: 99%

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Tang¹

2022

Preprint

0

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Many emerging sensor network applications operate in challenging environments wherein the base station is not available. Data generated from such intermittently connected sensor networks (ICSNs) therefore must be stored inside the network for some unpredictable period of time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow (DAO2). Utilizing spatial data correlation that commonly exists among sensory data, DAO2 employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem (TSP2) and quota traveling salesmen placement problem (Q-TSP2). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, TSP2 and Q-TSP2 must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in terms of energy consumption. The building blocks of our framework are two novel graph structures called aggregation network and minimum q-edge forest that accurately capture the information needed for energy-efficient data aggregation in ICSNs.

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“…In recent years sensor networks have been deployed to tackle some of the most fundamental problems facing human beings such as disaster warnings, climate change, and renewable energy. These emerging scientific applications include underwater or ocean sensor networks [9,22,28,35,43,60], wind and solar harvesting [34,40], seismic sensor networks [42,55], and monitoring of volcano eruption and glacial melting [18,47]. One common characteristic of these applications is that they are all deployed in challenging environments such as in remote or inhospitable regions, or under extreme weather, to continuously collect large volumes of data for a long period of time.…”

Section: Introductionmentioning

confidence: 99%

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Tang¹

2022

Preprint

0

View full text Add to dashboard Cite

Many emerging sensor network applications operate in challenging environments wherein the base station is not available. Data generated from such intermittently connected sensor networks (ICSNs) therefore must be stored inside the network for some unpredictable period of time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow (DAO2). Utilizing spatial data correlation that commonly exists among sensory data, DAO2 employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem (TSP2) and quota traveling salesmen placement problem (Q-TSP2). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, TSP2 and Q-TSP2 must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in terms of energy consumption. The building blocks of our framework are two novel graph structures called aggregation network and minimum q-edge forest that accurately capture the information needed for energy-efficient data aggregation in ICSNs.

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“…. $15.00 https://doi.org/10.1145/3366486.3366523 many of these applications, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with the camera and multiple onboard sensors, are used in the exploration of undersea resources and for gathering scientific data in autonomous or semi-autonomous monitoring missions [18].…”

Section: Introductionmentioning

confidence: 99%

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Chen

¹

,

Rahmati

²

,

Sadhu

³

et al. 2019

Proceedings of the International Conference on Underwater Networks &Amp; Systems

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Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.

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UW-SVC: Scalable Video Coding Transmission for In-Network Underwater Imagery Analysis

Cited by 8 publications

References 31 publications

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

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