Water Quality Monitoring System in Autonomous Underwater Vehicle Based on Internet of Things (IoT)

Bayusari, Ike; Adawiyyah, Nyiayu Aisyatul; Dwijayanti, Suci; Hikmarika, Hera; Husin, Zaenal; Suprapto, Bhakti Yudho

doi:10.23919/eecsi53397.2021.9624211

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Temperature is an important water quality parameter, which influences other water quality parameters and aquatic life. DO is another important parameter and is significantly affected by temperature (Zhi et al 2023) since it is the amount of free oxygen available in the water that supports aquatic life; low DO can cause aquatic life to suffocate (Bayusari et al 2021). pH is a measure of acidity or alkalinity with a safe environmental range of 6.5-8.5 (Cheng et al 2021).…”

Section: Water Quality Parametersmentioning

confidence: 99%

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Qadir,

Mumtaz,

Manzoor

et al. 2024

Water Supply

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Deteriorating water quality poses a substantial risk to human health, with billions at risk of waterborne diseases due to contamination. Insufficient water quality data augment risks as conventional monitoring methods lack comprehensive coverage. Technologies like the Internet of Things and machine learning offer real-time water quality monitoring and classification. IoT nodes often provide point data insufficient for monitoring the quality of entire water bodies. Remote sensing, though useful, has limitations such as measuring only optically active parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named `AquaDrone' has been developed. AquaDrone traverses water bodies, collecting data of four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. The data is transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like data tables, trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time classification. A comparative analysis of various oversampling techniques has been conducted in the context of water quality classification. The AquaDrone offers a feasible solution for monitoring quality of small to medium-sized water bodies, crucial for safeguarding public health.

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Section: Water Quality Parametersmentioning

confidence: 99%

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Qadir,

Mumtaz,

Manzoor

et al. 2024

Water Supply

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show abstract

“…Modern AUVs are usually equipped with numerous sensors, depending on the target mission and requirements. The most adopted are two-dimensional sonar imaging, photographic cameras or GPS, alongside field-specific transducers such as pH or oxygen meters [ 2 , 3 ]. Not only have sensor devices been upgraded, but in recent years, UUVs have also benefited from both the recent advancements in machine learning tools and enhanced hardware-processing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

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“…Quality of monitoring (QoM) [1][2][3][4] of information in the underwater domain [5,6] is a recent concept that has attracted the attention of researchers in the field of wireless sensors and connected objects. In recent years, there has been an upsurge of interest in underwater wireless sensor networks (UWSNs).…”

Section: Introductionmentioning

confidence: 99%

Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach

Aoueileyine

Bennouri

Berqia

et al. 2023

Sensors

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Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of diversity. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors.

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Water Quality Monitoring System in Autonomous Underwater Vehicle Based on Internet of Things (IoT)

Cited by 3 publications

References 1 publication

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach

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