The Role of Sensor Networks and IoT in Urban Planning
The concept of gathering data from sensor networks is not a novel one; such networks have been utilized for decades. It used to be the case that they required a lot of planning, and their integration needed to happen from the start – one would not be able to easily add them to a system post its deployment. Nowadays, sensors have become so cheap and so easy to deploy and maintain, wireless and battery-powered, that it is very easy to add them to any scenario that can benefit from gathering metrics in real-time.
New use-cases are enabled, especially in large-scale environments that are otherwise difficult to observe. In the Urban planning landscape, engineers now have access to a wealth of data that can be utilized to optimize how a city is maintained, to offer better comfort to people, and also help out with planning its expansion in a cost-efficient and sustainable way.
The concept of the Internet of Things (IoT) isn’t new, but its widespread applicability and importance have surged in recent years. At its core, IoT refers to the network of physical devices, from household appliances to industrial machinery, embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet. The result is a more integrated and autonomous digital ecosystem where devices can gather, share, and act upon data without human intervention.
Modern cities with their vast infrastructure and diverse populations are fertile grounds for IoT implementation. Streetlights, traffic signals, waste management systems, and even public transportation can be embedded with sensors to gather valuable data. This concept of integrating digital intelligence into the urban environment is often referred to as the Smart City paradigm. Here, the city’s infrastructure and services communicate in real-time, optimizing resources, reducing costs, and improving the inhabitants’ overall quality of life.
The Significance of Air Quality Monitoring in Smart Cities
Among the myriad applications of IoT in urban environments, air quality monitoring has emerged as a top priority. With urbanization comes increased vehicular traffic, industrial activities, and construction, all of which can significantly degrade air quality. Poor air quality not only affects the health of residents but also has broader implications for the environment and economy.
Utilizing IoT for air quality monitoring means cities can have real-time insights into pollutant levels. This immediacy in data acquisition is pivotal for quick decision-making, allowing authorities to issue timely advisories, regulate industrial activities, or reroute traffic.
But IoT’s potential in air quality management isn’t limited to mere monitoring. With the vast amount of data collected, urban planners can employ advanced analytics and machine learning (ML) algorithms to predict future pollution spikes based on historical data, weather forecasts, and anticipated urban activities. Such predictive insights allow cities to be proactive, taking measures to prevent or minimize anticipated air quality issues before they happen.
Infrared (IR) Gas Sensing: A Robust Approach for Air Quality Monitoring
Air quality sensors have become pivotal instruments in assessing and monitoring atmospheric pollutants. With the capacity to detect various gases and particulates, these devices offer insights that can inform policies, public advisories, and preventive measures.
When integrated into the IoT framework, these sensors enable real-time data acquisition and analysis, rendering them indispensable tools in the modern urban setting. Several methodologies drive the operation of these sensors, each suited to detecting specific pollutants. Infrared (IR) gas sensing stands out for its specificity and sensitivity.
The working principle of IR gas sensing is based on the unique molecular structure of each gas, which absorbs infrared light at specific wavelengths. When an infrared beam passes through a gas sample, the gas molecules absorb specific wavelengths, leaving gaps or absorption lines in the transmitted light spectrum. By identifying these gaps and quantifying the absorption, the gas’s concentration can be determined.
An IR gas sensor typically consists of an IR light source, a chamber or path where the gas sample flows, a series of optical filters to select the appropriate wavelength, and a photodetector to measure the intensity of the transmitted light. The difference between the emitted and received light intensities correlates with the gas concentration.
Calibration is essential to ensure accurate readings. Periodic checks with known gas concentrations help in adjusting sensor readings. While IR sensors are robust, it’s crucial to ensure the optical components remain clean and free from obstructions or contaminants.
The Advantages of NDIR Sensors in IoT-based Air Quality Monitoring
Among the various types of IR gas sensors, Non-Dispersive Infrared (NDIR) sensors have gained widespread popularity in the IoT space due to their performance and cost-effectiveness.
NDIR sensors are highly selective, capable of detecting specific gases without being affected by other gases present in the atmosphere. This is because each gas has a unique absorption fingerprint in the infrared spectrum, and the filters in NDIR sensors can distinguish with a high degree of certainty.
In addition, NDIR sensors provide very stable readings, as their accuracy does not degrade over time. Unlike some chemical sensors that can get poisoned or saturated, NDIR sensors have no such issues. Once calibrated, they maintain their accuracy for extended periods, reducing the frequency of recalibration.
NDIR sensors are also less affected by ambient temperature and humidity variations, ensuring consistent readings across varying environmental conditions. This longevity of the sensing element, coupled with the up to 10 years of battery life for modern IoT Wireless Sensors, translates into zero maintenance operation over a period of multiple years, leading to significant cost savings and time optimization.
Moreover, NDIR sensors offer a quick response time, which is essential for real-time monitoring and rapid detection of concentration changes. They are also highly sensitive, able to detect gas concentrations from very low parts per million to high levels, making them versatile for different monitoring needs.
While NDIR sensors provide numerous advantages, it’s essential to note that they are not universally ideal for detecting all gases. Their efficiency and accuracy depend on the specific gas of interest and its absorption characteristics in the infrared spectrum. Nevertheless, for gases like CO, they remain a preferred choice in many air quality monitoring applications.
Deploying an End-to-End IoT-based Air Quality Monitoring Solution
Let’s explore an example use-case scenario where a Wireless IoT Air Quality sensor can be most beneficial for urban air quality management.
In metropolitan city center areas, where traffic is heavy 24/7, constant CO2 emission levels can significantly degrade the air quality. Measures need to be taken, and traffic needs to be regulated not only based on road congestion but also on emission levels.
A network of sensors is deployed in strategic places throughout the city, starting at the busiest traffic junctions and continuing with smaller ones, adjusting as the system gathers more data. Additional sensors should be installed in underground parking spaces, as it is vital to keep track of the air quality in confined spaces.
These are outdoor devices, so weatherproofing is important. Sensors should be at the very least IP65 rated and have robust exposure and solid mounting options. The NCD Industrial Air Quality Sensor is a good example of these qualities.
Additionally, the IoT sensors should utilize a networking technology that promotes large-scale deployment via long range, cost-efficiency, and long battery life. DigiMesh and LoRaWAN are good examples, where the former has the advantage in network stability and ease of deployment, and the latter is more cost-efficient with better per-device range.
The data is relayed via Gateways to the cloud, where it is processed and analyzed. It is important to select devices that are compatible with a wide range of platforms like AWS IoT Core, Azure IoT Hub, Temboo’s Kosmos, etc., and have interoperability between vendors. NCD has solved this by making their API open, so you can own your data and send it to any endpoint where you are free to combine and analyze it in any way you want with the rest of the data in your IoT ecosystem.
Readings are taken over consistent intervals of time. As these devices are battery-operated, the time between transmission should be optimized for the best balance between battery life and data granularity. Measuring once every couple of hours or at the time of the heaviest traffic is a good strategy and could extend battery life up to 10 years for NCD DigiMesh Devices.
Periodic calibration of these sensors might be required; however, good-quality sensors come calibrated out of the box. This being said, opt for devices that support Over-the-Air (OTA) configuration and update to minimize the need for hands-on operation, supported by all NCD nodes.
Ingesting the data and visualizing it in a way that is conducive to making informed decisions is crucial to the effectiveness of a management and monitoring system. In this particular example, the best strategy would be to forward the data to the Cloud, where it will be analyzed and visualized on a platform.
A city is a large ecosystem with a wide area and many data points compared to a smaller network that could be limited to a building or an area factory, school, hotel, etc. There is a plethora of solutions to store and process the data, platforms like AWS IoT Core, Azure IoT Hub, etc. You could also choose to host your own solution and/or go open-source, which has the advantage of more control and better customization, but requires more skill and infrastructure management.
A good example of the aforementioned deployment would be using an IoT Edge Gateway that comes pre-provisioned with Node-RED. This makes it easy to parse and reformat the data and forward it over the web to an endpoint using HTTP or MQTT. An InfluxDB instance stores the parsed data, which can be analyzed and visualized with an in-house solution such as Grafana.
Another possibility would be to use an all-in-one platform that provides both storage, processing, and visualization of the data, such as TagoIO and DataCake, two very popular platforms that support a wide range of protocols, including Webhooks and MQTT, and have rule engines and a large selection of visualization widgets with a deep level of customization.
The Impact of Real-Time Air Quality Data on Urban Planning and Policy
Having real-time and historical data on CO2 levels empowers both communities as a whole and individuals to take action and focus their efforts in areas that are most in need of improvements.
Policy formation: Knowing which areas are most in need of improvement, city councils can make informed decisions and create policies that specifically target certain areas. For example, they can limit the use of vehicles in certain areas at certain times.
Infrastructure decisions: Knowing where pollution is heavier can be used to plan future infrastructure expansion. For example, parks can be created, and greener spaces can be preserved to balance CO2 levels and filter out pollutants to create an overall better environment. Levels can be constantly monitored, and plans adjusted as needed.
Public awareness: Having real-time data publicly available would create awareness of the state of the environment and give people the choice to improve things on a personal level. This could lead to less car usage, avoidance of more polluted areas, and overall awareness of the state of the environment to make decisions that would positively impact it.
As urban centers continue to grow and evolve, the integration of technology, especially IoT and air quality sensors, will be instrumental. It provides not only a snapshot of the current state of the environment but also offers insights to anticipate future challenges and address them proactively.
With advancements in sensor technology and a growing emphasis on sustainable living, urban centers worldwide will undoubtedly lean more on such data-driven strategies. The synergy of IoT and urban planning holds the promise of cleaner, healthier, and smarter cities for future generations.
Sensor Networks is at the forefront of this revolution, providing innovative solutions and expertise to help cities worldwide harness the power of sensor networks and IoT for sustainable urban development.