INTRODUCTION
A public space such as a library provides a multitude of options ranging from study-alone sessions, group discussions, workshops and lectures etc. However, even with such a variety of options, a user might not end up being as productive as he/she might have hoped for. To achieve the optimum condition for productivity depends on two factors:
Infrastructural Parameters - Light Intensity, Temperature & Humidity
Other Users - Affecting activity & noise level
On top of this, personal preferences play a huge role. A quiet and comfortable environment may be perfect for an individual but too sleepy for other whereas an active location within the building may be conducive for a group discussion but may be too noisy for another group which wants to sit and work in peace.
What If?
A user can almost never have an effect on temperature/humidity or light level as those are part of the provided infrastructure. And what seemed to be a great location to do the study group in the morning may not stay the same because of other users sharing the area.
What if there is a way to track the parameters real-time? What if there is a way to collect the historical data and present average profile of any spot within a building?
This information can help the users to pick a spot which matches their preferences.
SYSTEM ARCHITECTURE
The system architecture is the schematic diagram for the system displaying various parts of the product. The hardware part consists of sensors mounted on a Raspberry Pi. Pi reads live data from all the sensors which is uploaded to a web-server through a web-socket. A user may access the data through a website or through an app on a mobile device.
HARDWARE DESIGN
SOFTWARE DESIGN - INFORMATION ARCHITECTURE
A user has the ability to search for a location, save favorite locations or let the app choose his/her current location. The app also comes with pre-loaded profiles which are based on different configurations of the parameters. For example, the profile Peaceful will focus on lower decibel levels in an area where as 'Natural Light' will indicate the more naturally lit areas. The user has ability to adjust these profiles by assigning unique values to the parameters.
The final screen of the app shows the floor plan of the selected part of the building. The floor plan is divided into squares, each representing a Raspberry Pi and the sensor packaging. The heat map is generated by percentage mapping of the parameter ratings in the profile to the real-time sensor data.
UI MOCK-UPS
REAL-TIME DATA & PROFILE SELECTION
Users can choose to view real-time data based on the unique combinations of the parameters. The App will have pre-loaded profiles which are editable. As well slot for Customized profiles to make the selection totally unique.
MONTHLY AVERAGE DATA
User can also choose to view monthly average of the data using historical data collected by the sensors. This feature is inspired by google maps which gives 'Popular Times' pertaining to activity level, related to a public place like a restaurant or a museum. The users can use this mode to pick a location that has a historical record of staying close to their preferences.
