The concept of gathering data from sensors 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, 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 cheat and so easy to deploy and maintain (wireless, 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.
This article will delve deeper into some applications, devices and scenarios that are applicable to the smart city field.
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.
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 algorithms (ML) 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.
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. Here’s a detailed exploration of how it works:
Commercial buildings and offices stand to gain significantly from the implementation of flood detection technologies. They can benefit even more than residential building for the following reasons:
Every gas has a unique molecular structure that 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 infrared 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 IR light source emits light that passes through the sample chamber. As the gas in the chamber absorbs specific IR wavelengths, the photodetector captures 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.
All of the aforementioned make for a much easier time when managing commercial property, which translates into cost and resource savings. A survey conducted by the Commercial Real Estate Development Association in 2023 revealed that properties utilizing IoT flood sensors saved an average of 15% in annual maintenance costs compared to those without. Furthermore, properties equipped with IoT systems saw an 85% decrease in water-related incidents. This more than makes up for the initial investment in provisioning the building with wireless smart sensors.
There is a good reason for NDIR sensors to be so popular, as they offer a very good balance between performance and costs. There following characteristics are what makes this type of sensor so wide-spread.
NDIR sensors are very selective. They can specifically detect certain 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 are able to distinguish with a high degree of certainty.
In addition, these types of sensors provide very stable reading, their accuracy does not degrade over time. Some chemical sensors get poisoned and/or saturated, NDIR sensors have no such issues.
Once calibrated, NDIR sensors maintain their accuracy for extended periods, reducing the frequency of recalibration.
Advanced NDIR sensors can be designed to be less affected by ambient temperature and humidity variations, ensuring consistent readings across varying environmental conditions.
Overall, they are very stable over time, very accurate once calibrated and are not affected much by changing operation conditions.
NDIR sensors typically have a longer operational life compared to other types of gas sensors. They can operate for years without significant degradation in performance. This longevity of the sensing element coupled with up to 10 years of battery life for modern IoT Wireless Sensors (as per NCD specifications for all their sensors) translates into zero maintenance operation over a period of multiple years. This can add up to a lot of costs saving, time optimization and decreased service unavailability time.
Last, but not least this is also the result of the fact that they do not have consumable parts, which further limits the need for maintenance.
NDIR sensors offer a quick response time, which is essential for real-time monitoring and rapid detection of concentration changes. They are also very sensitive, able to detect gas concentrations from very low (parts per million) to high levels, making them versatile for different monitoring needs.
Many NDIR sensors are designed to consume minimal power, making them suitable for battery-operated or remote applications.
As NDIR sensors operate based on infrared light absorption, they do not involve any chemical reactions. This means they pose no risk of explosions or harmful byproduct formation, making them safe for various applications.
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.
Infrared gas sensing offers a robust and efficient method for detecting specific gases in the urban environment. As cities grapple with challenges like vehicular emissions and industrial pollutants, technologies like IR sensors, especially when integrated into the broader IoT ecosystem, become essential tools for timely and accurate air quality monitoring. Their long lifespan, efficient operation and the decreasing cost allow for the creation of large-scale IoT Wireless Sensor Networks, which paves the way for even more refined and responsive urban air quality management in the future.
Let us look into an example use-case scenario where a Wireless IoT Air Quality sensor can be most beneficial. We provide an overview of the complete end-to-end solution, from the sensor itself, through the network to the cloud back-end.
As society continuously develops so does traffic, people need transportation and this is causing cities to become more crowded. This has the negative effect of increased gas emissions from vehicles that pollute the air and cause significant degradation of the quality of life. This is especially severe in certain areas like metropolitan city center areas, where traffic is heavy 24/7, which cause a constant C02 emission level hike. 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. The initial deployments can start at the busiest traffic junctions and continue on 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 confided spaces like these.
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 promote large scale deployment via long range, cost-efficiency and long battery life. DigiMesh and LoRaWAN are a good example, where the former has the advantage in network stability, ease of deployment and is self-healing (no single point of failure) and the latter is more cost-efficient and has better per device range.
The data is relayed via Gateways to the cloud where it is processed and analyzed. It is important to select 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 end-point 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 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 in order to minimize the need for hands on operation (supported by all NCD nodes).
This is a must in any good IoT deployment. Ingesting the data and visualizing it in a way that is conducive to making informed decisions is crucial to how effective a management and monitoring systems is going to be. In this particular example the best strategy would be to forward the data to the Cloud where it will be analyzed and visualized in 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 of solutions to store and process the data, platforms as AWS IoT Core, Azure IoT Hub, etc. as we mentioned in the last paragraph. You could also choose to host your own solution and/or go open source. This has the advantage of more control and better customization (to a degree as it requires more skill), however you get the burden of managing additional hardware and making sure you have solid uptime.
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 for example 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. Good examples are 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. They come at a cost but reduce the deployment time a lot and also remove the infrastructure management overhead.
In the end, which topology you choose would depend on the particular application, its size and the budget (skill) one has. A good start would be the MING stack (MQTT + influxDB + Node-RED + Grafana). Take a look at the article from Balena, it is a good start.
Speaking from a practical perspective pollution is an issue, especially in larger cities, that has a direct and visible impact on the quality of life in these population centers. Having insights into CO concentration levels (a heatmap is a good representation) can be quite empowering as decision on future improvements can be made with confidence as they are backed by data.
Policy formation
Knowing which areas are most in need of improvement, city councils can make informed decision and create policies that specifically target certain areas. For example they can limit the use of vehicles in certain areas, in certain times etc.
Infrastructure decisions
Knowing where pollution is heavier can be used to plan future infrastructure expansion. For example, parks can be created, greener spaces can be preserved in order to balance CO levels and filter out pollutants to create an overall better environment. Levels can be constantly monitored and plans adjusted as need be.
Public awareness
People get wrapped up in their daily lives, becoming ignorant on issue sometimes. Having real time data publicly available would created awareness of the state of the environment and giving them 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 in order to make decision that would positively impact it (if they choose to).
In summary having real time and history data on CO level empowers both communities as a whole and individuals to take action and focus their efforts in areas that are in most need of improvements.
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.
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