ANEMOI is a solution for increasing the awareness of air quality in urban areas by equipping Electric Rental Scooters (ERS) with sensors. Each ERS will form a huge network of sensors throughout the city that works together to create a map of the air quality.
A large sensor network is a great tool for city planners to offer better living conditions to the large metropoles of the world. Unfortunately, such networks are often prohibitively expensive. The cost of installations, permits, labor, and securing connectivity and power at each point of interest often dwarf the price of the sensor itself and reduces the number of installed nodes. We believe that there is a more efficient solution to making cities smart - by installing such sensors on ERS.
The recent popularity of ERS is a great opportunity to revolutionize city-wide air and noise quality monitoring. Every such scooter is already equipped with the infrastructure required for remote sensing - they have a power source, a mobile uplink and GPS. The biggest benefit from this synergy, comes from the mobility of such scooters. This offers an unprecedented level of sensor coverage, at a drastically reduced cost.
ERSs have already expanded to nearly every major city in the US and Europe and is still a growing phenomena. By exploiting this movement, a cluster of ERSs can be utilized as a sensor network to collect data such as noise, barometric pressure, humidity, temperature, TVOCs and equivalent CO2 (or eCO2) levels as well as pollutants carbon monoxide and natural gases. The scooters will roam the city and collect data which can be used to identify areas with bad air quality or noise pollution. As seen in illustration #3, a large number of idle scooters present a unique opportunity to map air and noise pollution.
How it works
An overview diagram of the sensor system can be seen in illustration #2. A sensor module will be developed to be retro-fitted on already existing scooters. Utilizing the scooters and/or the clients infrastructure such as GPS and mobile uplink together with the onboard computer greatly reduces manufacturing cost of such device. The module can be made to communicate with the scooters via Bluetooth or serial-cable or as a completely standalone unit with a dedicated computer system with GPS localization, data processing and mobile uplink.
he following sensor will be evaluated for the system
- Sparkfun Environmental Combo CCS881/BME280
- Sensor Combo with barometric pressure, humidity, temperature, TVOCs and equivalent CO2 (or eCO2) levels.
- For hazardous gases like carbon monoxide, alcohol, acetone, formaldehyde and thinner.
- MQ-Series Gas Sensor
- A series of gas sensor, for alcohol, carbon monoxide and hydrogen gas.
(Onboard Data Collection/Processing)
Data from the sensors together with GPS position and a timestamp will be sampled and stored on an onboard memory. The scooters usually have 400 Wh batteries and are charged every 2-3 days. This means that a sensor package needing 0.1W on average will have an insignificant (<2%) impact on the performance of the scooter.
The scooters already have a 3G/4G mobile connection that could be utilized to transfer data from the onboard memory to a server. If this is not possible a separate modem can be implement on the sensor system.
(Backend Data Processing)
Data from the scooters will be processed on a backend server, possibly in a cloud service. The data collected will be used to map air and noise pollution in the city.
(Data Visualization to End Users)
A fun way to spread knowledge about local air quality and to spur people to cover areas that have not been visited by scooters would be to introduce a “painting”-game, where uncovered areas would have to be painted and users can be awarded green points to assure that measurements are being taken.
Potential Impact around the Globe
Increasing awareness of poor air quality global warming is the key concern for this project. By making users aware of the problems, we hope that more people will engage and influence policy makers. To make them focus resources in an efficient way to affect the air and noise pollution in the city, making the city environment a better place to be and potentially saving lives. The data can also be used to highlight areas where people with air or noise sensitivity can find a suitable housing.