@article{Monteros2024,
  author = {Amalia Rodriguez Espinoza de los Monteros and Maximo Giovani Tandazo Espinoza and Byron Ivan Punina Cordova and Ronald Eduardo Tandazo Vanegas},
  title = {IoT and Business Intelligence Based Model Design for Liquefied Petroleum Gas (LPG) Distribution Monitoring},
  journal = {Advances in Science, Technology and Engineering Systems Journal},
  year = {2024},
  volume = {9},
  number = {4},
  pages = {79–92},
  doi = {10.25046/aj090409},
  url = {https://www.astesj.com/v09/i04/p09/},
  language = {en},
  publisher = {ASTES Publishers},
  abstract = {<p style="text-align: justify;">Gas leakage caused by various causes poses significant risks to public safety. To address this problem, an intelligent model is proposed for the accurate monitoring of Liquefied Petroleum Gas (LPG) distribution based on the integration of Internet of Things (IoT) and Business Intelli- gence (BI) technologies. Through the use of sensors and actuators, it seeks to mitigate risks and prevent accidents by enabling automated control of devices and infrastructures. The PRISMA methodology was used to perform a systematic review and obtain general characteristics of the components. Then, the proposed model was evaluated according to Y. 4908 which addresses IoT network interoperability, usability and security, the evaluation with 30 IT professionals who examined the BI model. The results obtained by the professionals were encouraging and favorable. The proposal, which enables remote LPG monitoring, establishes service through a website, mobile app or SMS when it detects fluctuations in humidity, temperature and gas indi- cators, shuts off the flow of LPG and notifies immediately. The research led to the development of a model that combines an IoT component with a four-tier BI, demonstrating its effectiveness and acceptance in the professional arena. At the overall medium level, 49% strongly agree, 38% agree, 12% neither agree and 1% disagree. It is concluded that the model has an overall average level of approval of 87%.</p>},
  keywords = {Internet of Things, IoT, LPG, Sensing approach, Business Intelligence, Liquefied Petroleum Gas}
}