During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given rise to the development of wireless AQM portable systems. This paper presents the development of a LoRa (short for long-range) based sensor network for AQM and gas leakage events detection. The combination of both a commercial gas sensor and a resistance measurement channel for graphene chemoresistive sensors allows both the calculation of an Air Quality Index based on the concentration of reducing species such as volatile organic compounds (VOCs) and CO, and it also makes possible the detection of NO2, which is an important air pollutant. The graphene sensor tested with the LoRa nodes developed allows the detection of NO2 pollution in just 5 min as well as enables monitoring sudden changes in the background level of this pollutant in the atmosphere. The capability of the system of detecting both reducing and oxidizing pollutant agents, alongside its low-cost, low-power, and real-time monitoring features, makes this a solution suitable to be used in wireless AQM and early warning systems.

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LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events

Internet of Things (IoT) technology has become ubiquitous in a multitude of applications and its use is growing. However, the expansion of IoT faces a major difficulty: scalability, that is, very dense deployment of communicating devices is currently limited. In long-range networks, such as LoRa, the downlink is critical because it limits the number of acknowledgements that can be sent, and consequently reliability. It also limits the possibility to update the devices, which could be critical when they are deployed for decades. To overcome those problems, we propose a solution, inspired by Non Orthogonal Multiple Access (NOMA) techniques, to increase by at least one order of magnitude the number of devices that can be addressed. While the approach differentiates the devices by the power allocated to them, it differs from the vast majority of previous works on power domain NOMA because it does not require interference cancellation. Instead, it benefits from the spectrum spreading of the modulation scheme (chirp spread spectrum), where, at the end of the decoding phase, the information carried by a symbol is found in the position of a peak in the Fourier domain. In the vast majority of cases, the information from different users results in different peak positions, not creating any interference. In that sense, we get closer to avoidance schemes such as time or frequency hopping, but without using a code. In this article, we propose a new solution for NOMA in the power domain that does not suffer from the limitations induced by interference cancellation residues. The proposed system, including preamble detection and channel estimation, is presented and evaluated by simulations. We demonstrate that our proposed scheme increases the number of devices by one order of magnitude compared to the current system which allows addressing only one user at a time and maintains full compatibility with the LoRa physical layer standard.

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Multiuser Detection for Downlink Communication in LoRa- Like Networks

Thermal envelope analysis for new code compliance of Saudi Arabian dwellings

Cloud-Enabled IoT-based embedded system and software for intelligent indoor lighting, ventilation, early stage fire detection and prevention

Poor airflow in buildings can cause deterioration in the health of the occupants. The health concerns may be minor occurrences such as irritation of the eyes, nose and skin, headaches and fatigue to long term effects that include respiratory disease and heart inflammation. Indoor air quality monitoring system has been touted as one of the potential applications for the Internet of Things (IoT). This paper described the design and development of an IoT-based remote monitoring device and testbed that measures indoor air quality (IAQ), specifically eCO 2 level, Total Volatile Organic Compound (TVOC), temperature and humidity. Air quality sensors (BME680 and CCS811), were used with an ESP32 microcontroller, which acts as the brain and sends data to the InfluxDB cloud database. The data from the IoT device are visualized and monitored through the Freeboard.io dashboard hosted on the Amazon Web Services (AWS) server. A testbed was developed at Planet IoT, The Energy Sphere, UNITEN for IAQ monitoring. A performance study was conducted to test the functionalities of the IoT devices, communications, database effectiveness, and energy requirements in terms of battery life. An insight of the IAQ in the building was obtained with the IoT device able to function both in detecting harmful gases (eCO 2 and TVOC), temperature and humidity level condition $24\times 7$ with 76 hours of battery life for a 10 min reading interval or 273 hours of battery life for a 1-hour reading interval.

Cloud-Based IoT Air Quality Monitoring System

The effect of air quality on wellbeing and on life comfort is entrenched. Indoor air quality parameters are critical to make a gainful and sound indoor condition anyway by and large the air quality parameters are altogether different from those characterized as solid qualities. Taking into account that we spend about 90% of our lives inside, it is critical to screen the indoor air quality progressively to distinguish issues in the nature of air and plan intercessions in the building, or in the ventilation frameworks so as to enhance air quality. In this paper we are identifying the indoor air quality with the help of internet of things by using the sensors BME680 from bosch, SGP30 and CCS811 and low power wide area network (LPWAN) for the data transfer. Here we are separating the gases CO2 equivalent, Ethanol, TVoC and so on. For transmitting this IOT information, we require high vitality productive sensor hubs that can impart crosswise over long separation. This propels the improvement of some Low-Power Wide Area Networks (LPWAN) advances, for example, LoRa, to satisfy these requirements. The reports are exhibiting the destructiveness of air that we are taking in step by step. Here we are trying to give a social awareness by using this paper.

T. Sasipraba

Papers

Indoor air quality monitors using IOT sensors and LPWAN