DESIGN OF A SMART GATEWAY BASED ON REST API FOR DATA TRANSMISSION SYSTEM BETWEEN EDGE DEVICES AND SERVER IN THE AQUACULTURE SECTOR

This research aims to design and implement a smart network gateway based on REST API to improve the efficiency and security of data transmission in the aquaculture sector. The background of this research is based on the need for aquaculture farmers to monitor environmental conditions in real-time...

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Bibliographic Details
Main Author: Naura Azzahra, Iftitah
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/82326
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:This research aims to design and implement a smart network gateway based on REST API to improve the efficiency and security of data transmission in the aquaculture sector. The background of this research is based on the need for aquaculture farmers to monitor environmental conditions in real-time and take preventive actions based on accurate and up-to-date data. The use of Internet of Things (IoT) technology in the aquaculture sector has provided various benefits, including the ability to monitor aquaculture environmental conditions such as water quality, temperature, and other important parameters. However, the main challenge faced is how to transmit this data efficiently and securely. Therefore, this research develops a system that combines data compression features using GZIP and ZLIB libraries and data encryption with the AES algorithm to ensure that the data sent is secure and efficient. The main objective of this research is to test the successful implementation of compression and encryption algorithms in reducing data size and transmission duration, as well as enhancing security during the data transmission process. Additionally, this research aims to evaluate the system's performance in real-world scenarios in the aquaculture sector. The research methods used include system testing in various data transmission scenarios between edge devices and servers, as well as comparative analysis of data size and transmission duration with and without compression and encryption. The testing is conducted using a dataset relevant to aquaculture environmental conditions, where the data is sent through the smart network gateway to the server for analysis and storage. The research results show that using the ZLIB library is more efficient in compressing data compared to GZIP, resulting in smaller data sizes and faster transmission durations. The testing indicates that the ZLIB + AES combination produces the most efficient compressed and encrypted data size compared to other combinations. Meanwhile, the AES + GZIP and AES + ZLIB combinations show increased data sizes and longer transmission durations due to the encryption process being done before compression, making the data more random and harder to compress effectively. This highlights the importance of the order of applying compression and encryption in data transmission systems. Furthermore, the designed system also shows the ability to integrate with existing IoT infrastructure, providing flexibility for aquaculture farmers to monitor their iv environmental conditions in real-time. The testing also ensures that the system can store data in CSV format, including key, value, and data size, facilitating further data analysis. The implementation of this system is expected to improve productivity and operational efficiency in aquaculture farming, as well as reduce the risk of losses due to undetected environmental conditions. The conclusion of this research is that the implementation of a smart network gateway based on REST API with compression and encryption features can improve operational efficiency and data security in the aquaculture sector. This system not only meets the needs of farmers for real-time monitoring but also provides adequate data protection. Suggestions for future research include exploring the use of other compression and encryption algorithms that may offer better performance and testing in more diverse scenarios to gain a deeper understanding of the system's performance. Further research can also be conducted to integrate machine learning technology into this system, which can provide smarter predictions and recommendations based on the collected data, thereby helping farmers make more timely and effective decisions. With the adoption of this technology, it is hoped that aquaculture farmers can more easily monitor environmental conditions and take necessary actions quickly and accurately, thereby increasing production results and the contribution of the aquaculture sector to the national economy. This technology can also be applied in other sectors that require real-time environmental monitoring and control, providing broad benefits for various industries.