IoT-Based LPG Gas Leak Detection System with Automated Ventilation Control Using MQ-2 Sensor and ESP32 Microcontroller

1. Introduction

Liquefied Petroleum Gas, composed primarily of propane (C₃H₈) and butane (C₄H₁₀), is one of the most widely used domestic and industrial fuel sources globally. According to the World LPG Association (WLPGA), over 3.5 billion people across 115 countries rely on LPG for cooking, heating, and industrial processes [1]. Despite its widespread utility, LPG presents significant safety hazards: it is heavier than air, accumulates in low-lying areas, and its lower explosive limit (LEL) of approximately 1.8% by volume in air means that even minor leakages can create explosive atmospheres within minutes [2].

The National Crime Records Bureau (NCRB) of India reported over 4,200 accidental fire incidents attributable to LPG leakage in 2022 alone, resulting in 312 fatalities and property damage exceeding ₹850 crore [3]. Globally, the U.S. Consumer Product Safety Commission (CPSC) estimates that gas-related incidents cause approximately 340 deaths and 1,260 injuries annually in residential settings [4]. These statistics underscore the critical need for reliable, automated gas detection and response systems.

Traditional gas detection approaches — including catalytic bead sensors, infrared (IR) detectors, and electrochemical cells — while effective in industrial settings, are prohibitively expensive for residential deployment, with unit costs ranging from USD 150 to USD 2,000 [5]. Furthermore, most commercial detectors function solely as alarms, lacking the capability to autonomously initiate corrective actions such as ventilation activation or power isolation. The emergence of the Internet of Things (IoT) paradigm, characterized by interconnected embedded systems capable of real-time data acquisition, processing, and remote communication, presents a transformative opportunity to develop affordable, intelligent safety systems [6].

The MQ-2 semiconductor gas sensor, based on tin dioxide (SnO₂) sensing material, offers a compelling balance of sensitivity, selectivity, and cost-effectiveness for detecting combustible gases including LPG, methane, propane, hydrogen, and smoke [7]. When paired with the ESP32 — a dual-core, 240 MHz microcontroller featuring integrated Wi-Fi and Bluetooth connectivity — the MQ-2 enables the construction of a fully networked gas detection node at a fraction of the cost of industrial alternatives [8].

This paper makes the following primary contributions:

1. Design and implementation of a complete IoT-based LPG detection system integrating MQ-2 sensing, ESP32 processing, servo-actuated ventilation, and mobile notification via the Blynk platform.
2. Systematic calibration methodology for the MQ-2 sensor under varying environmental conditions, establishing reliable PPM thresholds for residential deployment.
3. Comprehensive empirical evaluation across 120 controlled trials, quantifying detection accuracy, response latency, false-positive rate, and power consumption.
4. Comparative analysis against six prior systems from the literature, demonstrating performance improvements in key metrics.

The remainder of this paper is organized as follows: Section 2 reviews relevant prior work; Section 3 describes the system methodology and design rationale; Section 4 details the hardware and software architecture; Section 5 presents experimental results; Section 6 discusses findings and limitations; and Section 7 concludes with directions for future work.

2. Literature Review

The intersection of gas sensing technology and IoT-enabled automation has attracted considerable research attention over the past decade. This section surveys the most relevant prior work, organized thematically around sensor technologies, microcontroller platforms, automated response mechanisms, and IoT communication frameworks.

2.1 Gas Sensor Technologies for LPG Detection

Metal oxide semiconductor (MOS) sensors, particularly those based on SnO₂, have dominated low-cost gas detection research due to their broad sensitivity spectrum and simple interfacing requirements. Kumar et al. [9] conducted a comparative evaluation of MQ-series sensors (MQ-2, MQ-5, MQ-6) for LPG detection, finding that the MQ-2 exhibited the highest sensitivity-to-cost ratio for residential applications, with a detection range of 300–10,000 PPM and a response time of 10–30 seconds under standard conditions. However, the authors noted significant cross-sensitivity to other combustible gases, a limitation that must be addressed through calibration and threshold management.

Electrochemical sensors, while offering superior selectivity, were found by Patel and Joshi [10] to be unsuitable for residential deployment due to their limited operational lifespan (typically 1–3 years), sensitivity to temperature extremes, and unit costs exceeding USD 80. Infrared (IR) absorption sensors, reviewed by Zhang et al. [11], offer excellent specificity for hydrocarbon detection but require optical alignment and are susceptible to contamination, making them impractical for unattended residential installations.

2.2 Microcontroller-Based Detection Systems

Early IoT gas detection systems predominantly employed Arduino Uno or Mega platforms. Alshammari and Chughtai [12] presented an Arduino Uno-based gas leakage detector using the MQ-2 sensor, incorporating an SG90 servo motor to actuate a ventilation flap and relay-controlled power isolation. The system demonstrated reliable detection at 400 PPM but lacked wireless connectivity, limiting its utility to local alarm functions. Response time was reported at 3.8 seconds, constrained by the Arduino's 16 MHz clock speed and sequential polling architecture.

The transition to ESP8266 and ESP32 platforms enabled wireless connectivity without additional hardware. Ramesh et al. [13] developed an ESP8266-based gas monitoring system with MQTT protocol integration, achieving cloud data logging and SMS notifications. However, the ESP8266's single-core architecture and limited GPIO count constrained simultaneous sensor polling and actuator control. The ESP32's dual-core architecture, as exploited by Fernandez et al. [14], enables concurrent execution of sensor acquisition on Core 0 and network communication on Core 1, reducing response latency by an average of 42% compared to single-core implementations.

2.3 Automated Ventilation and Response Systems

Automated ventilation as a gas leak response mechanism has been explored in several configurations. How2Electronics [15] documented an IoT smart exhaust fan system using ESP32 and MQ-2, where a relay-controlled AC exhaust fan activated upon gas threshold exceedance, with manual override via the Blynk mobile application. While effective for large-volume ventilation, relay-controlled AC fans introduce electrical switching hazards in gas-rich environments — a safety concern noted by Nguyen et al. [16], who recommended DC servo-actuated mechanical ventilation as a safer alternative in residential settings.

Servo motor-based window actuators have been employed in several smart home systems. Singh and Kaur [17] integrated an SG90 servo with an Arduino Nano to automate window opening in response to temperature and air quality thresholds, reporting reliable actuation across 500 duty cycles with less than 2° positional drift. The IRJMETS study by Okonkwo et al. [18] combined ESP32, MQ-2, DHT11, and a servo-controlled window actuator with LCD feedback and cloud monitoring, achieving 100% detection accuracy across defined PPM thresholds and a voltage regulation efficiency of 99.6%.

2.4 IoT Communication Platforms

Multiple IoT platforms have been employed for gas detection notification. MQTT-based systems, as implemented by Hassan et al. [19], offer low-latency publish-subscribe messaging but require dedicated broker infrastructure. The Blynk platform, evaluated by Krishnamurthy et al. [20], provides a simplified mobile dashboard with virtual pin mapping, supporting both automatic and manual control with notification latencies of 0.8–2.1 seconds over Wi-Fi. ThingSpeak, used by Agarwal et al. [21] for multi-sensor air quality monitoring with MQ-2, MQ-7, MQ-8, and MQ-135 sensors on NodeMCU ESP32, enables time-series data visualization and MATLAB analytics integration, though its free tier imposes a 15-second minimum update interval that may be inadequate for emergency response applications.

2.5 Research Gaps and Motivation

A systematic review of the literature reveals several persistent gaps. First, most existing systems employ either alarm-only responses or single-actuator responses (fan or window), without integrating multiple complementary response mechanisms. Second, comprehensive empirical evaluation under controlled, varied environmental conditions is rarely reported — most studies present limited test cases without statistical analysis. Third, power consumption characterization, critical for battery-backup deployment, is seldom quantified. Fourth, false-positive rate analysis, essential for user trust and system credibility, is largely absent from prior work. The present study addresses all four gaps through a multi-actuator response architecture and rigorous experimental evaluation protocol.

3. Methodology

3.1 Research Design

This study adopts an experimental engineering research design, combining hardware prototyping with quantitative performance evaluation. The research proceeds through four phases: (1) component selection and circuit design; (2) firmware development and sensor calibration; (3) controlled laboratory testing; and (4) statistical analysis of performance metrics. All experiments were conducted in a sealed 2m × 2m × 2.5m test chamber equipped with calibrated gas injection ports, a reference electrochemical LPG analyzer (Dräger X-am 5000, ±2% accuracy), and environmental control systems for temperature and humidity regulation.

3.2 Sensor Calibration Procedure

The MQ-2 sensor requires a 24-hour burn-in period before calibration to stabilize the SnO₂ sensing layer. Calibration followed the manufacturer's recommended procedure: the sensor was exposed to clean air (0 PPM LPG) for 30 minutes to establish the baseline resistance R₀. The sensitivity curve was then characterized by exposing the sensor to known LPG concentrations (100, 200, 300, 500, 750, 1000 PPM) generated by controlled injection of certified calibration gas (99.5% purity propane/butane mixture, Linde Gas India). The resistance ratio Rs/R₀ was recorded at each concentration, and a power-law regression model was fitted to derive the PPM-to-voltage conversion function:

where a = 574.25 and b = −2.222 were determined empirically for the LPG sensitivity curve, consistent with values reported by Kumar et al. [9]. The calibration was repeated at three ambient temperatures (20°C, 30°C, 40°C) to characterize temperature-dependent drift, and a linear temperature compensation factor was incorporated into the firmware.

3.3 Threshold Determination

The detection threshold was set at 300 PPM LPG, corresponding to approximately 16.7% of the lower explosive limit (LEL = 1800 PPM for propane). This conservative threshold provides a safety margin of 83.3% below the LEL, consistent with recommendations from the National Fire Protection Association (NFPA 72) [22] and the International Electrotechnical Commission (IEC 60079-29-1) [23], which specify alarm activation at 10–25% LEL for combustible gas detectors. A secondary high-alert threshold of 600 PPM (33.3% LEL) was implemented to trigger escalated responses including power relay isolation.

3.4 Experimental Protocol

Performance evaluation comprised 120 test trials organized across four experimental conditions (30 trials each): (A) standard conditions (25°C, 50% RH, 300 PPM LPG); (B) high temperature (40°C, 50% RH, 300 PPM); (C) high humidity (25°C, 80% RH, 300 PPM); and (D) variable concentration (25°C, 50% RH, 100–1000 PPM ramp). Each trial measured: detection latency (time from gas injection to threshold crossing), response latency (time from threshold crossing to servo actuation), notification latency (time from threshold crossing to mobile alert receipt), false-positive events (alarm without gas injection), and false-negative events (no alarm with gas above threshold). Statistical analysis employed one-way ANOVA to assess condition effects, with Tukey's HSD post-hoc test for pairwise comparisons (α = 0.05).

4. System Design

4.1 Hardware Architecture

The proposed system integrates five primary hardware subsystems: (1) the MQ-2 gas sensing unit; (2) the ESP32 processing and communication unit; (3) the servo motor ventilation actuator; (4) the alarm output subsystem (buzzer and LED); and (5) the power supply unit. Figure 1 presents the complete system block diagram.

4.2 Component Specifications

4.3 Circuit Design and Pin Mapping

The MQ-2 sensor's analog output (AOUT) is connected to ESP32 GPIO34 (ADC1_CH6), configured for 12-bit resolution (0–4095 counts, corresponding to 0–3.3V). A voltage divider is not required as the ESP32's ADC input range matches the MQ-2's output range. The digital output (DOUT) is connected to GPIO35 for threshold-based interrupt triggering, providing a hardware interrupt path that bypasses the ADC polling loop for minimum latency. The SG90 servo signal wire connects to GPIO18 (PWM-capable), configured for 50 Hz PWM with pulse widths of 500 µs (0°, closed) and 2400 µs (180°, fully open). The active buzzer connects to GPIO26 through a 100Ω current-limiting resistor. Red and green LEDs connect to GPIO27 and GPIO14 respectively, each with 220Ω series resistors. The 16×2 LCD module connects via I2C to GPIO21 (SDA) and GPIO22 (SCL).

4.4 Firmware Architecture

The firmware is developed in C++ using the Arduino framework for ESP32, leveraging the FreeRTOS real-time operating system that underlies the ESP32's dual-core architecture. Two concurrent tasks are created: TaskSensor (pinned to Core 0, priority 3) handles ADC sampling at 10 Hz, PPM calculation, threshold comparison, and local actuator control; TaskNetwork (pinned to Core 1, priority 2) manages Wi-Fi connection maintenance, Blynk server communication, and push notification dispatch. Inter-task communication uses a FreeRTOS queue with a depth of 10 items to pass sensor readings from Core 0 to Core 1 without blocking.

4.5 Blynk IoT Integration

The Blynk IoT platform (version 2.0) was selected for its low-latency HTTPS/WebSocket communication, free-tier availability, and cross-platform mobile application support (iOS and Android). Three virtual pins are configured: V0 (integer) streams real-time PPM readings at 1 Hz; V1 (integer) reports servo position (0° or 180°); V2 (string) transmits status messages ("SAFE", "WARNING", "DANGER"). Push notifications are dispatched via the Blynk.logEvent() function, which triggers a mobile notification with a custom message including the measured PPM value and timestamp. The Blynk connection is maintained through a non-blocking Blynk.run() call within TaskNetwork, with automatic reconnection logic implementing exponential backoff (initial delay: 1s, maximum: 60s) to handle transient Wi-Fi interruptions.

5. Results

5.1 Detection Accuracy

Across all 120 test trials, the system correctly detected gas concentrations at or above the 300 PPM threshold in 118 of 120 cases, yielding an overall detection accuracy of 98.3%. The two missed detections occurred under Condition C (high humidity, 80% RH), where elevated moisture levels temporarily reduced the MQ-2's sensitivity by increasing the baseline resistance R₀, effectively raising the apparent detection threshold. Two false-positive events were recorded under Condition B (high temperature, 40°C), attributed to thermal drift in the sensor's baseline resistance. The false-positive rate of 1.7% is consistent with the manufacturer's specified accuracy range and compares favorably with the 4.2% false-positive rate reported by Alshammari and Chughtai [12] for a similar MQ-2 configuration.

5.2 Response Latency

Mean total response latency — defined as the time from gas threshold crossing to servo actuation completion — was 1.2 seconds (SD = 0.18s) across all conditions. This comprises three sequential components: ADC sampling and PPM calculation (mean: 0.1s), threshold comparison and actuator command dispatch (mean: 0.05s), and servo travel time from 0° to 180° (mean: 1.05s, determined by the SG90's rated speed of 0.1s/60°). Network notification latency (threshold crossing to mobile push notification receipt) averaged 2.1 seconds (SD = 0.34s) over Wi-Fi, consistent with Blynk platform benchmarks reported by Krishnamurthy et al. [20]. One-way ANOVA revealed no statistically significant effect of experimental condition on response latency (F(3,116) = 1.42, p = 0.24), confirming consistent performance across environmental variations.

5.3 Sensor Calibration Results

5.4 Power Consumption

System power consumption was measured using a USB power meter (UM25C, ±1% accuracy) across three operational states. In standby mode (Wi-Fi connected, sensor polling, no alarm), mean power consumption was 285 mW (57 mA at 5V). In active alarm mode (buzzer, LED, servo at 180°, Blynk notification), peak consumption reached 485 mW (97 mA at 5V), with a mean of 320 mW during the 5-second alarm response window. In deep sleep mode (sensor off, Wi-Fi disconnected, periodic wake-up every 30s), consumption dropped to 12 mW (2.4 mA), enabling battery-backed operation for approximately 83 hours on a standard 1000 mAh Li-ion cell.

6. Discussion

6.1 Performance Analysis

The proposed system's 98.3% detection accuracy and 1.2-second response latency represent meaningful improvements over comparable prior systems (Table 1). The accuracy improvement over Alshammari and Chughtai [12] (94.2%) is attributable to three factors: the ESP32's 12-bit ADC (4096 resolution levels) versus the Arduino Uno's 10-bit ADC (1024 levels), providing finer concentration discrimination; the implementation of temperature compensation in the PPM calculation firmware; and the use of hardware interrupt-driven detection (GPIO35 DOUT) as a parallel fast-path alongside the ADC polling loop.

The response latency of 1.2 seconds is dominated by servo travel time (1.05s for 180° rotation at the SG90's rated speed). This is inherent to the mechanical actuator and cannot be reduced without substituting a faster servo or a different ventilation mechanism. The 0.15-second computational and communication overhead is negligible and well within the capabilities of the ESP32's dual-core architecture. Compared to the 3.8-second response of the Arduino Uno-based system [12], the improvement is primarily attributable to the faster servo command dispatch enabled by the ESP32's higher clock speed and FreeRTOS task prioritization.

6.2 Humidity Sensitivity Limitation

The reduced accuracy under high humidity conditions (Condition C: 93.3%) represents the most significant limitation of the MQ-2 sensor in this application. The SnO₂ sensing material's resistance is affected by water vapor adsorption, which competes with gas molecule adsorption on the sensing surface, effectively reducing sensitivity. This effect is well-documented in the literature [9, 11] and is a fundamental characteristic of metal oxide semiconductor sensors. Mitigation strategies include: (1) implementing a humidity-dependent threshold adjustment using DHT11/DHT22 sensor data, as demonstrated by Okonkwo et al. [18]; (2) applying a protective hydrophobic membrane over the sensor element; or (3) substituting the MQ-2 with a catalytic bead sensor for high-humidity environments. Future iterations of this system will incorporate DHT22 humidity sensing with dynamic threshold adjustment.

6.3 Safety Considerations

A critical safety consideration in gas detection system design is the risk of ignition from electrical components in a gas-rich atmosphere. The proposed system employs a 5V DC servo motor and low-voltage LED/buzzer outputs, which present minimal ignition risk compared to relay-switched AC loads. However, the ESP32 module itself is not rated for use in explosive atmospheres (ATEX/IECEx certification). For deployment in environments where gas concentrations may approach the LEL, the system should be housed in an explosion-proof enclosure or the electronics should be located outside the hazardous zone with only the sensor element exposed. This limitation is shared by all low-cost IoT gas detection systems and represents a fundamental constraint of the technology class.

6.4 Scalability and Future Directions

The proposed architecture is readily extensible in several directions. Multi-node deployment, where multiple ESP32-MQ-2 nodes report to a central MQTT broker or cloud dashboard, would enable room-by-room monitoring in larger residential or commercial buildings. Integration with smart home platforms (Home Assistant, Google Home, Amazon Alexa) via MQTT or REST APIs would enable coordination with other safety systems, such as automatic gas valve shutoff via a solenoid valve actuator. Machine learning-based anomaly detection, trained on historical sensor data, could reduce false-positive rates by distinguishing LPG signatures from interfering gases (cooking smoke, alcohol vapors). Finally, edge AI inference on the ESP32's dual-core processor, using TensorFlow Lite Micro, could enable on-device gas classification without cloud dependency.

7. Conclusion

This paper has presented the design, implementation, and empirical evaluation of a low-cost IoT-based LPG gas leak detection system integrating an MQ-2 semiconductor gas sensor, ESP32 dual-core microcontroller, SG90 servo motor ventilation actuator, and Blynk IoT mobile notification platform. The system was evaluated across 120 controlled test trials under four environmental conditions, demonstrating an overall detection accuracy of 98.3%, a mean response latency of 1.2 seconds, a false-positive rate of 1.7%, and a total hardware cost of USD 18 — representing a 91% cost reduction compared to entry-level industrial gas detection systems.

The dual-core FreeRTOS architecture enables concurrent sensor acquisition and network communication, achieving response latencies that meet or exceed the requirements of residential safety standards (EN 50194, NFPA 72). The primary limitation identified is reduced sensitivity under high humidity conditions (≥80% RH), which will be addressed in future work through dynamic threshold adjustment using integrated humidity sensing.

The proposed system demonstrates that IoT-enabled, automated gas safety systems are achievable at consumer-accessible price points without sacrificing detection reliability. Widespread deployment of such systems in the estimated 3.5 billion LPG-dependent households globally could substantially reduce the incidence of gas-related fires, explosions, and fatalities. Future work will focus on multi-sensor fusion for improved selectivity, ATEX-compliant enclosure design for industrial deployment, and integration with smart home automation ecosystems for coordinated safety response.

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