IOTyre Architecture Diagram
We take tyres for granted, perhaps too much, but if we learn that tyres are at least part and parcel the cause of an accident in which we are involved, we suddenly become very interested. Both from the designer’s point of view and the one servicing them, it is an excellent idea to be aware. The mission is to reduce the problems faced by the drivers because of air loss in tyres. IoTyre is an Air Pressure Monitoring system which has a sensor that is installed in the vehicle near its tyre and monitors the tyre pressure. If a pressure loss occurs in one or more tyres, the system automatically shows this as a warning in the Iotyre Dashboard. Since an incorrect tyre pressure increases the braking distance, can impair cornering and can cause tyres to heat up, this automatic monitoring is intended to increase driving safety and prevent accidents. The driver also no longer needs to perform manual air pressure checks and is informed immediately about any tyre pressure problems.
What it does
Once the user installs the sensors near the tyre of his vehicle he has to monitor the air pressure through the IoTyre application. The sensors will check the pressure using distance formula and give the user the percentage of air pressure there in tyres. It will compare the baseline value of the pressure which is calculated initially when the air is filled into the tyre. Then the user will get the current air pressure in the tyre in the application and if the air pressure is less than cut off value i.e. it’s too low then the user gets the SMS notification through the application that the tyre are not in the good condition and need to fill with air. After every hour sensor will send data to Amazon IOT through MTQQ protocol automatically and update the values of the air pressure in the application so that the user gets the latest air pressure of his vehicle tyre.
How I built it
- Set up AWS IoT data source using sample sensor data.
- Running the Python script generates fictitious AWS IoT messages from multiple devices. The IoT rule sends the message to Firehose for further processing.
- Created three Firehose delivery streams: one to batch raw data from AWS IoT, and two to batch output device data and aggregated data from Analytics.
- Configured AWS IoT to receive and forward incoming data.
- Created an Analytics application to process data using Amazon Kinesis
- Amazon Kinesis generates two output streams.
- BASIC_STREAM contains the device ID, device parameter, its value, and the time stamp from the incoming stream.
- AGGREGATE_STREAM aggregates the average value of distance between sensor and ground over a one-minute period from the incoming data.
- End result stored in S3 bucket.
- Amazon Lamba function calculates air pressure from data received through sensor, and posts to Dashbord using Amazon API gateway.
- In case air pressure is below than cut off value, then this app would send notification to end user on registered mobile number.
- So at the end user will see air pressure data on dashboard and in case of low pressure will get SMS notifications
Challenges I ran into
- How to calculate the air pressure ?
- How to store the data on amazon cloud and retrieve it ?
- Providing accurate values to the user.
Accomplishments that I'm proud of
We built this application and proud of the fact that were able to quickly build a working prototype in a very short time and the proof-of-concept already shows great potential and value to our prospective users
What I learned
We learned :
- Amazon IOT
- Amazon Kinesis
- Amazon S3
- Amazon lambda
- Amazon DynamoDB
- Amazon API Gateway
- Amazon SNS
What's next for IoTyre- Iotimated Air Pressure Monitoring
I am planning to improve performance of IoTyre app and once its up to the mark, then I planned to launch its Beta version in market.