UTB Library OPAC Search for 'su:"Internet of Things"'

Visual Internet of Things on Peatland Water Level Management /

Bandar Seri Begawan : Universiti Teknologi Brunei , 2019 . xiii, 98 pages : , Report submitted for the Degree of BSc in Computer Network and Security Universiti Teknologi Brunei | ABSTRACT Peatland cover over 400 million hectares of the Earth surface. It stores massive amount of carbon pool. They play major roles in accommodating the Earth global warming. But the logging activities from development agriculture site, and housing, has disturbed the ecosystem of peatlands habitat. Thus, urgent action from worldwide is required to protect and restore the peatlands. This includes raising the peat surface water level to avoid drying to mitigate fire. Approaches such as canal blocking was done as one of the strategies to raise it. However, too high water level might contribute to another problem such as flooding. Hence, continuous monitoring is required at least to ensure the water level is maintained within the optimum level. This project explains the proposed system for water level detection on peatland areas using visual Internet of things (IoT). The proposed system includes the use of Raspberry Pi and camera as device to capture image with integration of Arduino to acquire sensors reading. The image then goes through several image processing algorithms for water level detection and finally transmitted over cloud network. The outcome of this project produced camera-based water level detection prototype. The scope for this project is to highlights on Brunei peatland areas located at Badas, Kuala Belait. Keywords: Raspberry Pi, Camera, Internet of Things (IoT), Image Processing, Water level detection, Peatland 30 cm .

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Smart Vending Machine (SVC) /

By Ahmad Muzhaff Bin Sahbudin. Brunei Darussalam : Universiti Teknologi Brunei , 2019 . vi, 75 pages : , Thesis Submitted for the degree in computer Network & Security Universiti Teknologi Brunei. | Abstract This report describes the outline, usage and work of how Arduino components infused inside vending machines to implement a cash (cashless, portable, energy-efficient, and secure installment) framework in smart vending cells by innovating/utilizing current technologies such as RFID, solar power, cashless payment, and the traditional vending bill acceptor. Cube stores are a form of micro-business for youth to set up their own business in the initial stages before it grows larger. However, more and more cube stores are now experiencing a saturated market in which competition is high among cube stores. This project was inspired by seeing the growth and acceptance of cube stores in Brunei. By combining the ideas of the cube concept and vending machine, "Smart Vending Cells" is introduced. The proposed viewpoint comprises building a new vending machine infused with Arduino's components, such as the Arduino Mega microcontroller, passive RFID for the inventory system, keypad for selection and keying in codes, LCD for showing buyer details of products (price), and real-life security technologies for notification and security purposes. Other than that, online transaction purchases and power solar technologies will also be introduced. This project will be divided into two parts, namely the sample/model part and the real/live product. For this project, it will only concentrate heavily on the sample/model part, where a small-scale vending cell model will be built to implement/setup all the technologies mentioned to run or operate smoothly first. When all the mechanics and functionalities of the smart vending cells model have undergone vigorous testing and operate as expected, it could then be transferred to a bigger vending structure for real-life implementation/business purposes (for public use). Software used in this project will mainly be Arduino for the program, SQL lite for database inventory, and Dreamweaver for web creation. For the hardware part, most components that will be used have to be compatible with the Arduino microcontroller, solar power with AC to DC converter, plywood, steel cabinet, and a lot of mechanical components. 30 cm..

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Improving The Efficiency of Oyster Mushroom Cultivation Using IoT and Deep Learning /

By Dayangku Azreen Binti Pengiran Hj Tajudin. Bandar Seri Begawan: Universiti Teknologi Brunei, 2023 . x, 113 pages : , Dissertation Submitted in Fulfilment of the Requirements for the Degree of Master of Science | ABSTRACT This research aims to enhance the quality and yield of oyster mushroom cultivation in Brunei by leveraging innovative technologies. Brunei's equatorial climate poses challenges for oyster mushroom cultivation, with constant heat and humidity hindering optimal growth conditions. As a result, Brunei relies on mushroom imports to meet domestic demand. However, recognizing the potential of agriculture, particularly mushroom cultivation, the government has incentivized local youths to engage in agribusiness through grants and support. This study focuses on two primary objectives: Utilizing IoT for Environmental Monitoring and Deep Learning for Disease Detection. For the first objective, we deploy loT sensors to monitor temperature, humidity, and other relevant parameters within cultivation environments, collect comprehensive datasets, analyse data to identify correlations between environmental conditions and mushroom yield, implement adjustments to cultivation practices, and quantify improvements in yield and quality, stating the experiments have successfully shown a higher yield of 2.17 grams per bag for rack A with loT than rack B without IoT for a small number of bags. For the second objective, we train CNN models using annotated datasets of diseased and healthy mushroom samples, integrate the models into an automated disease detection system, evaluate the system's performance, implement preventive measures based on early disease detection, and quantify the reduction in crop damage and increase in overall cultivation success rates, stating the experiments have successfully shown a higher success rate for using Convolutional Neural Network in disease detection in mushroom cultivation. The experiments reveal a consistent decrease of 0.5% in disease detection percentage each day towards healthier bags, alongside fluctuations of 5.9% in detection percentage between consecutive days for specific bags. Despite these variations, our CNN-based system consistently outperforms manual labelling methods, validating its efficacy in improving disease management strategies in mushroom cultivation. By achieving these objectives, this research endeavours to address the challenges mushroom cultivators face in Brunei and pave the way for sustainable and technology-driven agricultural practices in the region. 30 cm.

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Blockchain - Based Key State Management of Hash-Based Digital Signature in IoT Networks in the Post-Quantum Era /

By Lew, Vincent Kok Seng . Bandar Seri Begawan : Universiti Teknologi Brunei, 2024 . 84 pages : , Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master in Science 30 cm.

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