AWS IoT for Automated Fleet Management

Challenges

During the first conversation with the client, their team mentioned important issues in their fleet management process. 

Their primary concern was related to their current GPS system. Sometimes, it is delayed, and sometimes, it won’t show accurate location data. Because of this concern, operational inefficiencies and customer dissatisfaction peaked.  

The second problem was their unorganized route planning system, which increased the fuel consumption of their vehicles. During traffic hours and bad weather conditions, fuel consumption often peaks. 

Scheduled deliveries were frequently delayed due to a Lack of communication between drivers and dispatchers. The client wanted to bridge this communication gap by upgrading their current fleet management system. 

The final issue resulted from manual work. Their current system was not automated, leading to higher operational costs as it increased fuel usage and extended work hours. 

Solutions 

By Understanding client's requirement and addressing both their current problems and new challenges, we provided them new fleet management solution: 

The first solution focused on improving location accuracy. Their previous GPS system suffered from delays and inaccurate data. To resolve this, we implemented AWS IoT Core to collect real-time sensor data, processed by AWS Lambda for instant updates. A custom Node.js analysis module was developed to handle diverse data formats, ensuring precise vehicle tracking. 

The second solution tackled inefficient route planning, which had led to high fuel consumption. The client’s outdated system lacked real-time traffic updates. We integrated Amazon Location Service with live traffic data and introduced a dynamic rerouting mechanism to adapt to road closures, traffic, and weather conditions, optimizing fuel efficiency. 

The third issue was poor communication between drivers and dispatchers, causing delayed deliveries. To bridge this gap, we built a responsive communication module using React JS and Redux. This provided drivers with a real-time dashboard to report issues, enhancing coordination with dispatchers. 

 The fourth challenge involved excessive manual processes, increasing operational costs. To streamline operations, we implemented Amazon SNS with AWS Lambda to deliver customizable, user-friendly alerts. By integrating Twilio API for SMS notifications, we ensured dispatchers received only critical updates, reducing information overload. 

Finally, the client needed better insights into fleet performance. Their previous system lacked robust data integration. We introduced an intermediate layer to consolidate data in Amazon S3, enabling comprehensive analytics and actionable performance insights.