###### Abstract
It would be even more challenging for readers to adapt the methodology to their individual requirements. While the above studies have demonstrated use cases for indoor environment studies using consumer-grade sensors with minimal modifications, there are several IAQ studies that implemented IoT technology with a high degree of customization, which often integrated various parts such as a single-board computer and sensors to form a sensing system. These customization approaches intended to achieve similar results to those that are based on consumer-grade sensors and included research into battery and energy management. For instance, [PERSON] et al. [30] proposed a multi-sensor multi-communication design for applications with accessible power outlets. Due to the accessibility to power outlets, they could incorporate four sensors and four communication modules in their designed system and achieved a relatively high sampling rate of 6 sample/min. However, their solution was considered over-designed for indoor IAQ monitoring purposes, as well as logistically challenging to deploy sensors at specific areas of interest for IAQ studies that has limited accessible power outlets. To address the constraint of power outlet issues, [PERSON] and [PERSON] [26] recently attempted to create a compact battery-powered wireless sensor for monitoring only temperature and humidity. In their work, an Arduino-based architecture was powered by a 300 mAh Li-ion battery. This allowed a continuous monitoring period of 12 h at 1 sample/min. While the design by [PERSON] and [PERSON] was compact and cost effective, their 12-h monitoring periods were far too short for IAQ monitoring applications, which normally last for days to weeks in order to capture meaningful periodic patterns. The parameters (i.e., temperature and humidity only) collected were also inadequate for evaluating the air flow and ventilation rate of an indoor environment. [PERSON] et al. [24] adopted a similar approach as the authors of [26], but utilised multiple sensors to capture multiple IAQ parameters. Sensors' battery lifetime was once again the focus. With a sampling rate of 0.1 sample/min, their devices could only last for 3+ days. By reducing the sampling rate to 1 sample/hour, their devices could last for roughly 1.5 weeks. However, such a low sampling rate was not sufficient for capturing transient data fluctuations that are critical for air flow/ventilation studies. [PERSON] et al. [25] proposed an intelligent IAQ monitoring and control system by integrating ventilation control in their design. However, their framework was based on a commercial software, LabView, which could prevent it from being widely adopted. Furthermore, technical details on sensors and communication standards were not provided to guide other users. In an earlier study, [PERSON] et al. [23] adopted the Message Queuing Telemetry Transport (MQTT) protocol, which made their system scalable. In their proposed system, discrete components and sensors were integrated to collect data at a sampling rate of 0.2 sample/min. Their customised sensing devices required rigorous calibration processes, which could once again be challenging for end-users with little to no knowledge of sensor calibrations. In contrast, systems based on off-the-shelf sensors would be more user-friendly as the sensors have been pre-calibrated by manufacturers. Nevertheless, no solution for data visualisation was provided in [23]. Among the aforementioned IAQ studies, the majority of the works focused on either (a) the IAQ pollutants, their sources, and correlations to health issues or (b) the technologies required to develop the IAQ monitoring system. In the latter cases, while they contributed to the study of IAQ monitoring, the knowledge and findings could be too technical to be adopted by the public. It should be noted that such custom-built devices were tailor-made for specific applications, which makes them difficult to adapt or deploy in scenarios that deviate from their intended settings.