In recent years, there has been an increasing need to monitor environmental health especially in big cities with very high industrialization. The development of smart cities play an important role in improving the overall human and environmental health, which in turn increase the productivity and economic growth in these cities. In this project, a sensor network that monitors indoor air quality, water quality, and sound quality is designed. The project is designed for an urban residential and industrial area which is typical of most modern-day cities.

The study conducted by Environment Canada from 1990 to 2016 showed 5 key air pollutants namely sulphur oxide, nitrogen oxide, volatile organic compounds, carbon monoxide and fine particles [1]. In a separate study [2], these pollutants were showed to contribute to the overall indoor air quality in the Canadian households. Moreover, various studies [3-5] have shown the health hazards associated with radon emissions around the world. Radon is produced when uranium, thorium and radium break down soil, rock and water, and then released to the air. Also, the province of Nova Scotia in Canada has been shown to have high levels of radon emissions which is often concentrated indoors in the basement of different houses [6]. Radon and other water pollutants also contribute significantly to the water quality found in each household in Nova Scotia and around the world. Therefore, in this project, a scalable indoor sensor network solution that monitors and analyzes the data for air and water quality is presented.

The indoor air quality is determined by comparing the levels of the five key air pollutants earlier mentioned to the recommended reference levels by Health Canada. Also, the indoor water quality is determined by comparing the pH levels, dissolved oxygen and conductivity to the permitted reference levels in each house. Furthermore, since the sensor network is deployed in an urban area where there are manufacturing industries, the overall sound quality in the working areas is also monitored. For each deployment location, the water, air and sound quality sensors are integrated into a single module through a very low power microcontroller.

The author in [7] identified the standardization problem involved with the rapid growth of internet of things (IoT). Among other challenges, the author identified the problems with the interconnectivity of devices ranging from smart home to smart cities. Consumers are faced with many isolated devices while back-end developers are faced with the difficulty of mining data from too many platforms. Also, their connectivity to the internet makes them prone to attacks. Although this project is not prototyped for internet enabled sensors, however, the system is designed as a scalable solution which will integrate many smart home sensors into a single transceiver unit while also achieving a smart land solution. For this reason, the ESP 8266 microcontroller module was chosen for this project. This module has a high-speed processing chip as well as many other input/output interfaces, while maintaining a very low power consumption. The module is connected serially to a MRF24J40MB transceiver which supports a ZigBee mesh network. 

The module is deployed indoors - in the basement level of residential houses, at a spot which is able to maximize the RF link. For this reason, the link budget is calculated to minimize the path loss between nodes. Also, considering the sensitivity of the microcontroller and the transceiver, the power budget is calculated, and the battery unit is determined for an extended deployment over 365 days.

At the controller unit in the mesh network, an internet gateway is enabled for the data across the network to be uploaded to a cloud storage. For an industrial application, the Amazon web service (AWS) is proposed. The data insights are transformed into usable JSON formats and uploaded to an S3 bucket. For every S3 bucket event, the AWS Lambda function generated an event which updates a master table on the AWS Redshift. Finally, novel algorithms are run on a local machine and the analytics results are made available on a web application. However, the MATLAB ThingSpeak platform service is used for prototyping this project. The S3 bucket is replaced by input channels that are able to capture the time series data. The analytics are done on the ThingSpeak platform and the results are sent to output channels. This would serve as a low-cost prototyping solution.

Finally, many stakeholders will benefit from this project. For instance, the real estate market will have access to real-time radon levels while houses are being sold. Government parastatals will also have access to reliable data on air, water and sound quality. Households will also be able to make informed health decisions. The scalable solution gives manufacturers a competitive advantage while consumers are free from the problems of isolated smart home devices.

References
[1] https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/air-pollutant-emissions.html
[2] https://www.canada.ca/en/health-canada/services/air-quality/improve-indoor-air-quality.html
[3] https://www.americanradonllc.com/why-do-radon-levels-fluctuate/
[4] Blanco-Novoa, et al. 2018. A cost-effective IoT system for monitoring indoor radon gas concentration. Sensors, 18(7), p.2198.
[5] Kim, Jae-Hak, Sung-Ha Yun, and Gyu-Sik Kim. "A Study on a Wi-Fi System for Radon Monitoring."
[6] https://fletcher.novascotia.ca/DNRViewer/?viewer=Radon

Inspiration

We observed the level of radon emissions in Nova Scotia. Mere measurements are taken only when people want to purchase houses or signing mortgages. The data is quite unreliable because measurements are taken over such a short period of time. The rest is left to fate! Also, we have worked on project for smart home devices in the past. Through this project, we observed the challenges involved with integrating smart homes into smart cities and the problem of so many isolated smart home devices. To analyze our results, we had a really hard time mining data from some devices as well. We see an opportunity through the Keysight challenge to proffer a novel solution to these problems. Thus, our project monitors indoor radon emissions alongside other air, water and sound quality parameters for extended periods. This makes the data reliable. Furthermore, the scalability solution enables the integration of many other smart home devices into a single module.

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