Secure and Efficient Data Model for Public Lighting in México with AMI/IoT: Implementing LZ4 Compression, IPFS, and Blockchain
René GARCÍA-REYES,
Javier ORTIZ-HERNANDEZ,
Rito MIJAREZ,
José Alberto HERNÁNDEZ-AGUILAR,
Yasmín HERNÁNDEZ https://doi.org/10.15514/ISPRAS-2024-36(6)-10
Abstract
The advent of digitalization and Internet of things (IoT) technologies brings new challenges to the management of electric metering systems. Integrating institutional energy billing systems with government Ambient Intelligence (AMI) systems is essential for effective management. Blockchain technology is proposed to maintain data integrity through automated energy readings. This study introduces an innovative model designed to enhance public lighting in Mexico by integrating AMI and IoT, and employing LZ4 and IPFS for data compression. This approach aims to optimize the handling of large data volumes, resulting in improved data efficiency, enhanced security, cost reductions, and better energy resource management.
Keywords
blockchain,
Internet of things (IoT),
advanced metering infrastructure (AMI),
inter planetary file system (IPFS),
LZ4,
public lighting.
Supporting agencies: This research was supported by the National Center for Research and Technological Development (CENIDET) through the CONACYT doctoral scholarship. RGR acknowledges the financial support of CONACYT, Mexico.
About the Authors
René GARCÍA-REYES
National Technological Institute of Mexico, National Center for Research and Technological Development
Mexico
Mexico
Final-year PhD student at the Computer Science Department of the National Center for Research and Technological Development (CENIDET) in Mexico. His research interests include decentralized technologies, cryptography, advanced metering infrastructure (AMI), data science, data mining, Internet of Energy (IoE), cloud computing, and virtualization.
Javier ORTIZ-HERNANDEZ
National Technological Institute of Mexico, National Center for Research and Technological Development
Mexico
Mexico
PhD in Automation and Computer Science, full professor at the Computer Science Department of the National Center for Research and Technological Development in Mexico. Research interests: digital transformation, requirements engineering, business process modeling.
Rito MIJAREZ
National Institute of Electricity and Clean Energy
Mexico
Mexico
PhD in Electrical and Electronics Engineering, a full-time researcher at the Instituto Nacional de Electricidad y Energías Limpias in Mexico. Research interests: digital signal processing, embedded systems, non-destructive testing, ultrasonics.
José Alberto HERNÁNDEZ-AGUILAR
Autonomous University of the State of Morelos
Mexico
Mexico
PhD of Engineering and Applied Sciences from the Center for Research in Engineering and Applied Sciences at the Autonomous University of the State of Morelos (UAEM). His research interests include data mining, machine learning, deep learning, optimization algorithms on Graphics Processing Units (GPUs), and applied artificial intelligence. Dr. Hernández Aguilar is a Level 1 member of the National System of Researchers (SNI) and has authored several research and outreach articles, as well as three books.
Yasmín HERNÁNDEZ
National Technological Institute of Mexico, National Center for Research and Technological Development
Mexico
Mexico
PhD in Computer Science from Tecnológico de Monterrey and a full-time professor and researcher at the Computer Science Department of the National Center for Research and Technological Development (CENIDET) in Mexico. Her areas of expertise include artificial intelligence, intelligent tutoring systems, educational data mining, affective computing, natural language processing, human-computer interaction, and machine learning.
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Review
For citations:
GARCÍA-REYES R., ORTIZ-HERNANDEZ J., MIJAREZ R., HERNÁNDEZ-AGUILAR J., HERNÁNDEZ Ya. Secure and Efficient Data Model for Public Lighting in México with AMI/IoT: Implementing LZ4 Compression, IPFS, and Blockchain. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2024;36(6):179-194. https://doi.org/10.15514/ISPRAS-2024-36(6)-10
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