Random Access and Detection Performance of Internet of Things for Smart Ocean

Bai, Lin; Han, Rui; Liu, Jianwei; Choi, Jinho; Zhang, Wei

doi:10.1109/jiot.2020.2990164

Cited by 13 publications

(2 citation statements)

References 26 publications

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“…Driver assistance systems (DAS) are a big step forward in car safety, especially when it comes to lowering the risks of human mistake. As one of the many parts of DAS, visionbased systems are very important for understanding the surroundings, finding possible dangers, and helping people make decisions [2]. Usually, cameras, LiDAR, and other sensors are used in these systems to get real-time information about the area around the car.…”

Section: Introductionmentioning

confidence: 99%

Development of a Probabilistic Framework for Enhanced Vision Safety in Driver Assistance Systems

Rajesh D. Thakare

2024

cana

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Potholes on roads endanger public safety and infrastructure upkeep. This paper discusses the design and implementation of a real-time pothole detection system that employs a GPS module to locate potholes, a Raspberry Pi, an ESP32 microcontroller, a camera for image processing, and a 16x2 LCD to display pothole detection and GPS locations in real-time. To support successful maintenance, the technology tries to identify potholes in real time, precisely measure their dimensions, and document their locations. The proposed system enhances road safety while also lowering maintenance costs by taking a proactive approach to pothole reduction. The proposed system effectively achieves its aims by integrating a variety of hardware components. The GPS module is used to provide exact geolocation, allowing for detailed mapping of pothole sites and focused maintenance activities. A Raspberry Pi serves as the central processing unit, coordinating data collecting, analysis, and communication operations. An ESP32 microcontroller improves system performance by managing low-level hardware interfaces and real-time processes, allowing for a quick response to pothole detection. A camera built inside the device takes photographs of road surfaces, which are then examined using sophisticated image processing algorithms to properly identify and characterize potholes.

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Section: Introductionmentioning

confidence: 99%

Development of a Probabilistic Framework for Enhanced Vision Safety in Driver Assistance Systems

Rajesh D. Thakare

2024

cana

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“…Inspired by the great success of the Internet of Things (IoT) in the interconnected world, expanding the IoT to underwater scenarios to build intelligent ocean systems is a potential trend [1,2]. The Internet of Underwater Things (IoUT) is defined as a broad network that interconnects underwater environments with digital devices and plays an important role in marine science [3]. Underwater wireless sensor networks (UWSNs) have powerful capabilities and are widely utilized in underwater resource exploration, marine geographic meteorological data collection, earthquake and tsunami disaster prevention, water pollution monitoring, and national defense security [4].…”

Section: Introductionmentioning

confidence: 99%

High Value of Information Guided Data Enhancement for Heterogeneous Underwater Wireless Sensor Networks

Li,

Bai,

Chen

et al. 2023

JMSE

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Ensuring the freshness of high Value of Information (VoI) data has a significant practice meaning for marine observations and emergencies. The traditional forward method with an auv-aid is used to ensure the freshness of high VoI data. However, the methods suffer from two issues: an insufficient high VoI data throughput and random forwarding for cluster heads (CHs). The AUV (Autonomous Underwater Vehicle) with limited energy cannot meet the demand for the random generation of high VoI data. Low VoI data packets compete with high VoI data packets for channels, resulting in an insufficient high VoI data throughput and a low freshness. To address the above issues, we propose the Data Access Channel Scheme based on High Value of Information (DACS-HVOI), which is suitable for prioritizing the transmission packets with a high VoI. First, according to the level of VoI, the packets are divided into K classes, and the packets that are collected and forwarded by the AUV are defined as the highest K+1 class. Second, based on prior knowledge in the network, a Markov chain algorithm-based method is employed to predict which nodes should preferentially use the channel, to avoid conflict between a low and high VoI. Third, based on the stochastic fluid theory, a multilevel queueing system for CHs are constructed to avoid random forwarding. Last, compared with state-of-art protocols, experimental simulation shows that the proposed scheme has a low latency and high network throughput, while improving the throughput of high-VoI packets and ensuring the priority transmission of high-VoI packets.

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