An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol

Abstract

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10^-4 over a distance of 590 m.

Keywords: Internet of Things (IoT); airborne pollution hazard; intelligent gas sensor system (IGSS); long range (LoRa); low-power wide-area network (LPWAN).

Figure 1

Block schematic diagram of the networked intelligent gas sensor system (N-IGSS).

Figure 2

(a–c) The proposed architecture of the networked intelligent gas sensor system (N-IGSS).

Figure 3

Connection diagram of (a) sensor node (transmitter) and (b) gateway (receiver module), as interfaced with the microcontroller and LoRa module.

Figure 4

Basic circuit diagram of the N-IGSS: (a) sensor node and (b) receiver gateway at the RDPS.

Figure 4

Basic circuit diagram of the N-IGSS: (a) sensor node and (b) receiver gateway at the RDPS.

Figure 5

Physical prototype of N-IGSS: (a) sensor node, (b) receiver gateway at the RDPS. 1: Gas sensor array; 2: temperature and humidity sensor; 3: LoRa module (Tx); 4: microcontroller (Tx); 5: antenna (Tx); 6: power supply (DC, 5 V); 7: LoRa module (Rx); 8: antenna (Rx); 9: microcontroller (Rx).

Figure 6

Block schematic diagram of process for obtaining 3D scatter plot.

Figure 7

3D scatter plot of SLDA-transformed dataset.

Figure 8

Block schematic diagram of proposed classifiers.

Figure 9

AdaBoost classification model.

Figure 10

XGBoost classification model.

Figure 11

Random Forest classification model.

Figure 12

MLP classification model (a,b).

Figure 13

Performance of AdaBoost, XGBoost, RF, and MLP classifiers.

Figure 14

Performance of MLP classifier.

Figure 15

Confusion matrix of the MLP classifier.