1. Technical Seminar Presentation Phase 2
R.V.COLLEGE OF ENGINEERING,BENGALURU

2. R. V. COLLEGE OF ENGINEERING BENGALURU – 560059
“Solving Bengaluru’s Traffic Problem using IoT”
Presented by
Pooja Chukkanakal
1RV16SSE11
Under the Guidance of
Dr. G. N. Srinivasan
Professor,
Department of ISE, RVCE, Bengaluru-59.

3. Agenda
Methodology
Drawbacks
Conclusion

4. System Components
Figure 1: System components of the targeted system

5. Sensors and actuators :The system will collect data from a large set of sensors.
The traffic lights are the system’s actuators. A traffic light can be queried for its last reported state and the state can be changed with a control signal.
Device orchestration: The devices, sensors and actuators are orchestrated in such a manner that enable them to register, validate, and securely communicate with the system.
Scientific analysis :To determine the effectiveness of a strategy, analysis is performed on the data. The intent and output of analysis can either be preprocessing for the controller or scientific evaluations of the system.

6. Storage: The data collected by the devices, the control decisions, the current state of the systems processes and entities, and the analysed data is recorded and stored indefinitely for concurrent and future analysis.
Controller: The system operates a set of controllers that act on multiple inputs from both real-time, stored, and preprocessed sensor data produce a set of control signal that is then relayed to their constituent traffic lights.
Monitoring: The performance and availability of the system’s components and devices are monitored in runtime.

7. Methodology
Figure 2: AWS components and data flow for the testbed

8. IoT : AWS IoT is a platform that enables connected devices to securely communicate and relay information to and from the AWS platform using Message Queue Telemetry Transport (MQTT).
An entity in AWS IoT is referred to as a Thing. AWS IoT maintains the state of each Thing, through what is referred to as a Shadow state.
Lambda: AWS is offering a highly scalable serverless microcompute platform called Lambda.
Instead of running an entire application in a single Virtual Machine (VM), it can now be broken up into a set of redundant asynchronous sub-functions.

9. DynamoDB
AWS offer a number of Database (DB) services. AWS DynamoDB is a low-latency No-SQL schema-based DB. In this work AWS DynamoDB is used for storing collected data and maintaining shard states.
Kinesis
AWS Kinesis is a highly scalable aggregating streaming data buffer. Kinesis is scalable in the sense that it can achieve a high throughput by forwarding ingress data to a practically infinite pool of parallel end- points.

10. The simulated traffic environment is supplied by Simulation of Urban MObility (SUMO).
A SUMO simulation scenario is at minimum specified by a network of roads, traffic-monitoring induction loops, traffic lights, and vehicle arrival rates, speeds, and entry points.
SUMO provides an extensible interface, Traffic Control Interface (TraCI), that allows researches and developers to interact with the simulator, environment, and the visualisation tool over a socket in real-time.

11. The message is intercepted by an AWS IoT rule which internally produces a Lambda function that assesses the punctuality of that bus by comparing the ingress data with a time table in DynamoDB.
TraCI module is used to expose the induction loops, buses, cars, bus- stops, and traffic lights to the information flow as Things in AWS IoT.

12. Figure 3: AWS components and data flow with simulated/emulated endpoints

13. Drawbacks
The credentials required for sensors to communicate with AWS IoT have to be stored locally in each sensor. This means that an overhead is incurred each time a sensor is added to the system.
Punctuality is hard to maintain in the country where there is huge population.

14. Conclusion
The system created provided dynamic vehicle traffic control based on vehicle volumes/patterns and weather conditions. This can be implemented in India where there is less traffic and then can be expanded to areas with more traffic.

15. References
William Tärneberg, Vishal Chandrasekaran, Marty Humphrey.” Experiences Creating a Framework for Smart Traffic Control using AWS IOT”, 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing.
Mr. Basavaraju S R,” Automatic Smart Parking System using Internet of Things (IOT)”, International Journal of Scientific and Research Publications, Volume 5, Issue 12, December 2015
S. Djahel, R. Doolan, G.-M. Muntean, and J. Murphy. A communications-oriented perspective on traffic management systems for smart cities: challenges and innovative approaches. Communications Surveys & Tutorials, IEEE, 17(1):125–151, 2015.
V. Gradinescu, C. Gorgorin, R. Diaconescu, V. Cristea, and L. Iftode. Adaptive traffic lights using car-to- car communication. In Vehicular Technology Conference, VTC2007-Spring. IEEE 65th, pages 21– 25. IEEE, 2007.

16. Thank You