Comparing Platforms and Analyzing Internet of Things Regulations for Commercialization on Real-Time Rainwater Quality Measurement Using Juridical Analysis
Real-time measurement of rainwater quality through Internet of Things (IoT) technology has become an important focus in environmental monitoring. Some popular IoT platforms such as Thinspeak, Antares and Ubidots are frequently used. One use of IoT is to measure rainwater quality in real-time, which currently exists and is in the patent process. However, in order for these tools to be commercialized, it is necessary to compare platforms and analyze IoT protocol regulations in various jurisdictions so that they comply with applicable legal standards. This research aims to compare IoT platforms and analyze protocols IoT in a case study of the real-time measurement of rainwater quality. Through comprehensive juridical and literature analysis, this research identifies relevant regulations in Indonesia. Several related regulations include Law No. 36 of 1999, Government Regulation No. 53 of 2000, Minister of Communication and Information Regulation No. 13 of 2018, and Minister of Communication and Information Regulation No. 21 of 2014. In addition, selecting the IoT platform used to measure rain quality using Thingspeak has fulfilled the Quality of Service category with a score of 4 for lost, delayed and postponed packets. This tool has the potential to be commercialized, and this research provides an in-depth view to stakeholders so that it can be used as an early warning system or decision support system by considering technical, data security, suitability for commercialization, communication and interoperability standards as an evaluation of the use of IoT platforms and related regulations.
Authors
- Arief Arianto (ORCID: https://orcid.org/0009-0002-1033-906X)
- Helni Mutiarsih Jumhur
- Ardiansyah Ramadhan
- Khairina Fadilah
Institutions
- National Nuclear Energy Agency of Indonesia (ID)
- Telkom University (ID)
- Université de Franche-Comté (FR)
Publication Details
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1109/icondbtm64135.2024.11122778
- Primary Topic
- Hydrological Forecasting Using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00