Arduino and IoT-based LPG gas leak detection systems: a focused review of sensors, architectures, and reliability issues - Rinasa Agistya Anugrah; Faiz Bintang Adhidarma; Rico Satrio Hutama; Vitho Kevi Restu Maulana; Yusa Figar Winarno (Universitas Negeri Yogyakarta, 2026)
Liquefied petroleum gas (LPG) leakage remains a major safety concern in households, restaurants, and micro, small, and medium enterprises because leaked combustible gas can accumulate rapidly and trigger fire or explosion. This paper presents a focused review of Arduino- and Internet of Things (IoT)-based LPG gas leak detection systems, emphasizing sensing technologies, embedded architectures, alert mechanisms, performance indicators, and reliability limitations. In response to reviewer concerns, the literature base was expanded from a small set of local prototype papers to more than fifty international peer-reviewed studies from Scopus-indexed journals and proceedings, covering metal oxide semiconductor sensors, MEMS gas sensors, wireless sensor networks, IoT security, low-power communication, signal processing, and TinyML-based gas detection. The synthesis shows that MQ-series sensors remain dominant in low-cost prototypes because of their availability and simple analog interface; however, their practical reliability is constrained by cross-sensitivity, humidity and temperature dependence, heater power consumption, aging, baseline drift, and insufficient calibration. IoT integration improves remote awareness through Wi-Fi, GSM, LoRa, Zigbee, or cloud dashboards. However, it also introduces latency, network outage, service availability, data integrity, and cybersecurity issues. Recent studies indicate that multi-sensor fusion, calibrated testing, edge intelligence, and reliability-oriented design provide a more credible pathway than threshold-based prototypes alone. Future LPG safety systems should therefore combine robust sensing, standardized performance reporting, local fail-safe alarms, secure IoT communication, energy-aware operation, ergonomic installation, and long-term field validation before large-scale household or industrial deployment.