In the path to the smart city era, we believe that sound and air pollution will greatly affect people’s mental and physical health. These lead to a decrease in productivity and people's happiness. Hence, we need a way to measure precisely to facilitate the authorities in fixing the problem.
By using the wireless sensor network strategy, we can locate the exact position of the sound source that exceeded the safe limit. This is possible by utilizing sound localization to triangulate with multiple sensors that have the sound source within their measurable range. For instance, when a specific sound reaches the first sensor and the second sensor after some amount of delay, it introduces a phase delay. By calculating the time difference, we can know the distance relationship between different sensors, and calculate the exact position as we know the decibel level and we can compare with the sound damping with distance profile. Then, the position can be overlaid on Google Maps since we know the longitude and latitude of the sensors we placed. This will allow the authorities to view in big picture and take necessary actions.
Moreover, this sound mapping feature could identify the sound to know what type of sound it is. This is paramount as it could help in alerting the authorities when a gunshot sound, fire alarm or car crash is identified. More lives could be saved with this system implemented.
In conjunction with the microphone, the system will also contain a MQ-135 gas sensor. The resistance of the sensor will decrease when the air pollution is high. The gas sensor also provides the digitized value to the gas concentration in parts per million (PPM). We can use that to determine the air quality of the area.
We aimed to place the sensors on top of every few road lamps along the road. This is favorable as it eases the transmission of the radio frequency signals (the sensors mostly in the line of sight of each other) and the cost could be lowered. The sensors send data through mesh connections with other sensors so if one of the sensors failed the system would not be affected critically.
The battery for both sensors is a rechargeable Lithium battery which has the size of 18650. By utilizing the deep sleep feature of the microcontroller, which only needs to send a signal every hour, the current consumption is reduced significantly, and the battery capacity can last for a year.
In terms of manufacturing, only two processes are needed. First, produce the printed circuit board and assembled with the two sensors, microcontroller, battery holder and others. Then, cover it by silicone resin except for the antenna and the battery for weatherproofing. With the location being on top of the road lamp, the sensor can last for years without being destroyed by external factors and hence less maintenance is required.
Overall, this method sets us apart from other products in the market as we focus on the low cost and easy implementation. We included the sound mapping and sound identification techniques to have a different approach in terms of sound quality.
