FA8 Monitoring and Estimation
Time : 09:00-10:30
Room : Room 8 (Ocean Bay)
Chair : Dr.Joongsup Yun (Cranfield University, UK)
09:00-09:15        FA8-1
Development of a Smart Aquaponic System based on IoT

A. M. Bassiuny(Heliopolis University, Egypt), Rania Darwish, M. M. M. Mahmoud(Helwan University, Egypt)

Aquaponics is a sustainable farming practice that combines aquaculture and hydroponics in a recirculating system.This process is self-sustaining and requires little human intervention. The monitoring and control of aquaponic systems is a complex task. In this research, the Internet of Things (IoT) is utilized to monitor and control the proposed system. In addition, a digital twin (DT) is deployed to digitize the physical system. The results explore that IoT and DT can improve aquaponic systems by collecting data about environmental conditions that is used to regulate plant growth, monitor fish health, optimize crop production, and optimize nutrient recycling.
09:15-09:30        FA8-2
Web-based Structural Health Monitoring System Using Piezoelectric Joint Sensors and IoT Devices

Keiju Seki, Kazuhisa Nakasho(Yamaguchi University, Japan), Carlos Cuadra, Nobuhiro Shimoi(Akita Prefectural University, Japan)

Recently, Japan has witnessed an upsurge in accidents attributed to aging infrastructural structures. The sensor device, developed in this study, provides continuous structural monitoring. Upon detection of an abnormal voltage by the piezoelectric joint sensor, the device transmits this voltage to an information aggregation system and simultaneously alerts the user of the precarious structural condition. The information aggregation system receives the voltage data dispatched from the sensor devices and offers a web application to track the time and location of abnormal voltage detection.
09:30-09:45        FA8-3
Link Budget Analysis for HSR Communication in Low Frequency Spectrum

Selvi Lukman, Yul Yunazwin Nazaruddin(Institut Teknologi Bandung, Indonesia)

This study proposes adesigned power link budget to be utilized in 900 Mhz as the frequency spectrum of GSM-R (The Global System for Communication for Railway). The nonstationary and diverse mechanism of HSR make the prediction of path loss as the most prominent factor in HSR channel modeling very difficult to conduct. An optimal link budget must comprise a total calculation of all parameters in which the factor of path loss must be included during the signal propagating between the base station and HSR. In this study, the prediction of path loss values is achieved from the utilization of optimization technique which differ from other existing path loss prediction.
09:45-10:00        FA8-4
Perimeter Intrusion Prediction Method Using Trajectory Frequency and Naive Bayes Classifier

Joongsup Yun, Hyo-Sang Shin, Antonios Tsourdos(Cranfield University, United Kingdom)

This paper proposes a novel perimeter intrusion prediction algorithm that can be applied to generic perimeter security systems. The proposed algorithm uses multiple probability mass functions that are computed using trajectory frequency information for different behaviour models: non-intrusive intention and intrusive intention. A Naive Bayes classifier is used to compute the intention probability for integrating multiple probabilities from different probability mass functions. The performance of the proposed algorithm is validated by numerical simulations and the classification characteristics are also discussed.
10:00-10:15        FA8-5
Implementation of WSN and Fire Hazard Alarm in a Smarthome System

Syd Tabat Castillo, Ramius Andrei Delos Santos Bagunu, Charmaine Cabrera Paglinawan(Mapua University, Philippines)

The objective of the study is to develop an IoT-based smart home system that will allow remote appliance control, monitor fire hazards, and alarm when fire is present in the house. The study fully utilized the IoT capabilities of the smart home system by sending all the data onto a cloud-based database and connecting a mobile application to it. The mobile application displays sensor data and active appliances for real-time monitoring and control. Once a fire or fire hazard is detected inside the house, the system’s hardware and software app triggers an alarm. The system was optimized in a square 10 x 10 house.

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