A Gas Leakage Detection Device Based on the Technology of TinyML †

Abstract

Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

“…The impact of TinyML extends to many areas, including wearable technology [12][13][14][15][16], smart cities [17][18][19][20][21][22], smart homes [23][24][25], smart agriculture [26][27][28][29][30][31], climatic change, environment protection, green AI sustainable applications [32][33][34][35][36][37][38], and automobiles [39,40]. Overcoming TinyML's challenges, especially in hardware, is key and can be advanced through creating an open-source community.…”

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

“…Optimizing irrigation for precision agriculture is used to improve crop yield and quality [21,22]. Smart sensing systems are used in the field of early warning and monitoring systems [23,24] to address the challenges posed by natural disasters and climate change [25]. Intelligent energy-management systems reduce energy waste and carbon emissions [26].…”