{"id":125,"date":"2021-07-10T08:34:00","date_gmt":"2021-07-10T15:34:00","guid":{"rendered":"http:\/\/www.taosdata.com.cn:88\/blog\/?p=125"},"modified":"2025-11-03T08:07:02","modified_gmt":"2025-11-03T16:07:02","slug":"using-tdengine-to-build-the-backend-of-a-connected-vehicle-platform","status":"publish","type":"post","link":"https:\/\/tdengine.com\/using-tdengine-to-build-the-backend-of-a-connected-vehicle-platform\/","title":{"rendered":"Building a Platform for Connected Vehicle Data"},"content":{"rendered":"\n

In the era of the Internet of Things (IoT), vehicles are not only just individual self-contained transportation objects, but also Internet-connected endpoints which can be capable of two-way communication. The real-time data generated by connected vehicles enhance user experiences in car rental and fleet management business, drive innovative business model like usage-based insurance<\/a>, and build the foundation of autonomous driving and vehicle-to-everything (V2X) paradigm<\/a>. Normally, sensors in vehicles collect real-time vehicle information such as vehicle speed, GPS location, etc., and send the data to cloud data processing platforms. The cloud platform provides fast persistence of the vehicle data and provides both real-time and historical data analysis for front end applications. <\/p>\n\n\n\n

For 100,000 connected vehicles with a data acquisition rate at around every 1-30 seconds, about 0.29 to 8.64 billion records will be generated every day. At this scale of data generation, the performance of the data persistence layer is often the bottleneck of the performance of the platform and a monster in the platform budget. A typical HBase solution usually needs at least 6 high-performance servers equipped with 8-core CPUs and 32GB RAM, while using a time series database<\/a> (TSDB) like TDengine only needs one server with a 2-core CPU and 8GB RAM.<\/p>\n\n\n\n

Architecture<\/h2>\n\n\n\n

The traditional HBase solution is extremely heavy for a connected vehicle data processing platform.<\/p>\n\n\n\n

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Architecture of an HBase solution<\/figcaption>\n<\/figure>\n\n\n\n
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Architecture of a TDengine solution<\/figcaption>\n<\/figure>\n\n\n\n

In both solutions, real-time vehicle data is collected by sensors, sent through network gateways, authorized and parsed, and then sent to the core processing modules. HBase itself only provides the data persistence functionalities, and the solution often needs a Kafka module for message queuing and subscription, a Redis cluster for caching the latest real-time data, and possibly a Spark module for the data aggregation and computing work. Fortunately for TDengine, the architecture is a lot lighter. All the functionalities mentioned above have already been integrated into TDengine. TDengine is a purposely built for time series data processing engine integrated with the fastest time series data storage\/query engine, message queuing, caching, data subscription and stream computing functionalities. In the following part of this article, we will show readers how to build the backend of a demo connected vehicle platform using TDengine.<\/p>\n\n\n\n

Data Model<\/h2>\n\n\n\n

Connected vehicle data is composed of a variety of sensor and usage data. Many companies use data models specified by the ISO 22901<\/a> industry-standard diagnostic format along with some customized modifications. Usually, the collected data types contain information about the vehicle location, drivetrain metrics, and some other third-party tracking indicators. Here we use a simple data model assuming our sensor only collects the most important data listed below:<\/p>\n\n\n\n