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case study

IoT-Based GPS Fleet Tracking App Development

Development of a GPS fleet tracking solution that uses the Internet of Things (IoT) data to provide visibility into what’s happening on the road in real time and allow interventions even if the truck is thousands of miles away. Optimizing the system’s code and improving its integration capabilities so it can display up to 1000 trucks on a map simultaneously and visualize their movements in real time.

client

3D Telematics

Malaysia
Up to 50 employees

A GPS fleet tracking company and logistics software provider that offers a web and mobile-based solution for managing and streamlining the workflow of multiple fleet operators on a single platform.

request background

Implement a GPS fleet tracking system based on a proof-of-concept

The client reached out to us with the task to develop an IoT-based vehicle tracking system that would collect vehicle data and visualize it on a map. It would use connected devices to provide the real-time operational updating of vehicle data — such as location, speed, fuel consumption, driving time, and video from cameras — through the CAN bus. The harvested data would be stored in a database and available for processing.

According to the client’s request, the solution would enable dispatchers to manage the history of vehicle data updates, generate reports, monitor events and notifications, control connected devices remotely, manage customs billing, and take advantage of other GPS tracking fleet management features with the help of a mobile device or a laptop.

challenge

A testing prototype of a GPS tracking app that had low flexibility, limited integration capabilities, and no documentation

The Malaysian provider of fleet tracking services approached us with a PoC that was completely undocumented. To begin with, all we had was the product vision, a basic web-based application with some testing data to demonstrate how the future GPS fleet tracking software solution should work, and certain documentation provided by producers of IoT devices we had been integrating the client’s solution with.

After we conducted a thorough audit of the client’s fleet management and tracking app, (just as we did in the web-based shipping platform project), we noticed that it had two significant limitations. First, the poor code quality hampered new feature implementation. Second, the existing integration mechanism wasn’t stable enough to allow hundreds of trucks to be connected to the system and displayed on the map simultaneously with real-time movement visualization as the client required.

The client couldn’t achieve their goals using the existing codebase. After all, the provided app was more of a concept rather than a fully-fledged GPS telematics fleet tracking software product that needed a couple of updates. This meant we needed to rewrite most parts of their app from scratch.

goals

Code optimization of the client’s app to make new feature implementation easier.
Upgrading the integration mechanism of the client’s solution to increase the number of IoT devices integrated by the system and visualized on a map.
New feature implementation, such as remote vehicle control and real-time freight movement visualization

solution

The transforming power of technology and expertise

Node.js, PHP, HTML, CSS, AWS
2 years
3 specialists

First of all, we improved the integration capabilities of the client’s GPS fleet management solution using a Node.js-based IoT signal parser. This allowed us to dramatically increase the number of connected devices. Second, we optimized the backend code, which gave us a stable core for further development and feature implementation.

To implement functionalities for the client’s fleet tracking system, the development team's capabilities were increased from two to three contributors. From the client’s end, the product owner was in touch with us throughout the entire process.

As a result, this product traveled a two-year-long path from a basic proof-of-concept (PoC) with many challenges to be resolved to become one of the best fleet GPS tracking systems in the market.

The following IoT fleet tracking and management features were implemented:

Constant data transmission. If a connected fleet tracking device is switched on, the system constantly receives a signal from it to collect the needed vehicle data, such as speed, time on the road, fuel level, GPS position, tire pressure, and video streaming from cameras.
Reporting. All device data is stored in the system for further analysis and can be exported in preferable formats, like .csv and .pdf.
Data visualization. The collected data can be visualized in web reports and on an interactive map. Optimized code allowed us to boost the system’s stability, enabling us to significantly increase the number of trucks displayed on a map simultaneously. Meanwhile, the WebSocket protocol allowed for smooth map updates for real-time freight movement visualization.
Remote command processing. The dispatcher can send a remote command to a device to perform some actions, such as switching the engine off/on, opening containers, and so on. This feature particularly comes in handy at customs. Since drivers typically don’t have access to the freight and can’t open containers on their own to present the goods to a customs officer for inspection, the dispatcher can open the container remotely upon the driver’s request.

outcome

From basic PoC to powerful telematics system

AWS-based architecture with powerful data storage and processing capabilities
Seamless integration with approximately 1000 IoT devices, including GPS trackers from different manufacturers
New features, such as video streaming from cameras or remote command processing
An increase in the number of trucks displayed on a map from 100 to 1000 objects
Smooth truck movements visualization in real-time — before we stepped in, the location of trucks on the map of the client’s GPS tracking app was updated only every 5-10 seconds
The considerable increase in speed of automated composing of the truck movements report: from 4 days for 4 trucks to 15-40 seconds for 1000 trucks

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