Low-Altitude-Platform-Based Airborne IoT Network (LAP-AIN) for Water Quality Monitoring in Harsh Tropical Environment

Alobaidy, Haider A. H.; Nordin, Rosdiadee; Mandeep, J. S.; Abdullah, Nor Fadzilah; Haniz, Azril; Ishizu, Koji; Matsumura, Toshiro; Kojima, Fumihide; Ramli, Nordin

doi:10.1109/jiot.2022.3171294

Cited by 16 publications

(5 citation statements)

References 57 publications

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“…The propagation of mobile signals is greatly affected by the surrounding objects. This is due to the nature of radio waves that will be scattered, reflected, refracted, or absorbed when interacting with any object in its path [50]. As a result, it affects the signal power level at the UE location.…”

Section: ) Signal Propagation Status (𝑶𝑶𝑶𝑶𝑪𝑪)mentioning

confidence: 99%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

Self Cite

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Nowadays, the dependency on high-performance digital mobile connectivity is not limited to human usage but also the intelligent objects increasingly deployed to serve the needs of Internet of Things (IoT) applications. However, the current network planning technique limitation has constrained the real potential of mobile digital connectivity development. This situation has hindered sustainable Internet-oriented economic and technological development. The 3 rd generation partnership project (3GPP), through its specification release 18 (Rel.18), has included and leveraged the potential capabilities of machine learning (ML) technologies in advanced mobile network planning. The main objective is to enhance mobile network planning performance and reduce complexity. To materialize this aim, we propose a novel ML-based Online coverage Estimator (MLOE) tool developed based on Random Forest (RF) ML algorithm. It uses seven unique features to predict the mobile network performance through reference signal received power (RSRP). Accordingly, the results showed that MLOE outperformed traditional empirical techniques and previous works. The final trained RF algorithm has achieved an outstanding root mean square error (RMSE) of 2.65 dB and a coefficient of determination (𝑅𝑅 2 ) of 0.93. With the dynamic and fast-growing mobile technology, MLOE has been deployed on an online platform using MATLAB ® Web App Server, which offers a modular and scalable architecture.

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Section: ) Signal Propagation Status (𝑶𝑶𝑶𝑶𝑪𝑪)mentioning

confidence: 99%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

Self Cite

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“…The front-end is only responsible for the display interface. All data-related requirements are implemented by calling the service APIs provided by the business layer [13]. User Login Module: Users first register with the system to confirm the identity and password of the user who logged into the account, and then grant roles with different access rights.…”

Section: System Architecture Designmentioning

confidence: 99%

Optimization Method of River Water Pollution Prevention and Early Warning System Supporting Spectrum Classification and 3D Remote Sensing Technology

2023

WPPCP

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In recent years, people around the world are living in better and better conditions, and people are demanding more and more from their environment, and pollution of the water environment has become the most serious environmental hazard. In many areas, due to the strong development of agriculture and industry, their pollutants are discharged directly into nearby river basins, leading to eutrophication of water bodies. Failure to carry out river water pollution (WP) prevention and control not only poses a major threat to the health of the people in society, but also has a serious impact on the harmonious and stable development of society. Therefore, this paper uses 3D remote sensing technology (RST) to collect images of river basins, generate remote sensing maps of river basins, extract features of remote sensing images using spectrum classification, and establish river water quality monitoring stations to obtain water quality data of each basin section. The application of spectrum classification and 3D RST provides technical support for the construction of river WP prevention and early warning system (EWS), helps to monitor the water quality in river basins and early warning WP accidents, is conducive to promoting the development of WP prevention, and improves the current crisis of river WP prevention in China.

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“…This additional feature will facilitate the creation of an optimized truck route for garbage collection. A. Alfered [25] to be effective in accurately measuring the level of solid waste garbage in bins. Looking ahead, there is a possibility of incorporating deep learning techniques to determine the geolocations coordinates of the bins.…”

Section: Related Workmentioning

confidence: 99%

A Smart Waste Management System Framework Using IoT and LoRa for Green City Project

Laha,

Pattanayak,

Pattnaik

et al. 2023

IJRITCC

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Waste management is a pressing concern for society, requiring substantial labor resources and impacting various social aspects. Green cities strive for achieving a net zero-carbon footprint, including efficient waste management. The waste management system deals with three problems that are interrelated: a) the timely checking of the status of bins to prevent overflow; b) checking the precise location of bins; and c) finding the optimal route to the filled bins. The existing systems fail to satisfy all three problem areas with a single solution. To track the overflow of the bin, the proposed model uses ultrasonic sensors, which are complemented with LoRa to transmit the exact location of the bins in a real-time environment. The existing models are not that efficient at calculating the exact bin-filled status along with the precise location of the bins. The Floyd-Warshall algorithm in the proposed model optimizes waste collection using the Floyd-Warshall algorithm to determine the shortest path. Leveraging low-cost IoT technologies, specifically LoRa modules for data transfer, our solution offers benefits such as simplicity, affordability, and ease of replacement. By employing the Floyd-Warshall algorithm with a time complexity of O (n^3), our method efficiently determines the most optimal waste pickup route, saving time and resources. This study presents a smart waste management solution utilising Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN to measure waste levels accurately. The proposed strategy aims to create clean and pollution-free cities by addressing the problem of waste distribution caused by poor collection techniques.

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Low-Altitude-Platform-Based Airborne IoT Network (LAP-AIN) for Water Quality Monitoring in Harsh Tropical Environment

Cited by 16 publications

References 57 publications

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Optimization Method of River Water Pollution Prevention and Early Warning System Supporting Spectrum Classification and 3D Remote Sensing Technology

A Smart Waste Management System Framework Using IoT and LoRa for Green City Project

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