The use of connected sensors and devices to collect and analyze data in real-time in smart cars has the potential to greatly improve road safety and efficiency. Also Analyzing real-Time Data in Microsoft Azure created a massive growth trend in the smart car industry.

By leveraging the power of Internet of Things (IoT) technology, smart cars can communicate with each other and with traffic infrastructure to proactively avoid accidents and optimize traffic flow.

The analysis of real-time data enables faster decision-making, leading to reduced congestion and enhanced overall driving experience.

The key components of the Internet of Things are connected sensors and devices enabling smart cars to communicate with each other, the cloud, and the environment.

The various benefits observed by these technologies for smart car owners, manufacturers, and service providers are:-

• Predictive maintenance: The performance and condition of various parts of the car are monitored by connected sensors and devices, such as the engine, tires, brakes, and battery. It can send real-time data to the cloud, where AI algorithms can analyze the data and detect any potential issues or failures. It also helps in preventing breakdowns, reduce repair costs, and improve safety. For example, Ford uses AI to enable in-vehicle predictive maintenance and remote software updates.
• Car connectivity: The connectivity and entertainment experience of the car can be enhanced by connected sensors and devices for the drivers and passengers. It can also integrate the car audio system with personal smart devices, such as smartphones, tablets, and smartwatches. It also provides access to various online services and apps such as navigation, music, podcasts, and audiobooks. For example, Apple’s CarPlay lets drivers make calls, send messages, and use Siri through the car console.
• Self-driving cars:  Connected sensors and devices are assisting self-driving cars by connecting them to a network of smart devices and sensors. The devices share real-time data about traffic, weather, road conditions, and nearby vehicles. The information also helps self-driving cars make safer and smarter decisions, such as adjusting speed and routes. For example, Tesla uses cameras, radar, and ultrasonic sensors enabling its Autopilot system.

The real-life examples of how connected sensors and devices are used in collecting and analyzing data in real-time in smart cars are:-

• Azure Sphere: A solution from Microsoft providing securing and scalable IoT connectivity for smart cars. It consists of a microcontroller unit (MCU) running a Linux-based OS, a cloud-based security service, and a development platform. Azure Sphere can be used in sending telematics messages (such as speed, location, and fuel level) from the car to the Azure IoT hub, it can be processed by Azure Stream Analytics and stored in Azure Cosmos DB or Azure SQL Database. It can also be connected to the car’s OBD-II port to stream OBD-II data ( such as engine temperature, oil pressure, and emissions) to Azure IoT Edge and Azure IoT hub.
• Michelin: A global tire manufacturer uses IoT to improve its products and services. Michelin also introduced RFID tags in its commercial truck tires, providing detailed and accurate reporting and insights on tire condition, pressure, temperature, and wear. It also uses a cloud-based platform called Michelin Track Connect, collecting and analyzing data from sensors embedded in the tires of racing cars. It helps drivers to optimize their performance and strategy.
• Capgemini: A global consulting and technology company providing IoT solutions in the automotive industry. It also developed a real-time data ingestion/processing pipeline ingesting and processing messages from IoT devices into a big data analytic platform in Azure. The pipeline also uses AzureSphere and Azure IoT Hub for managing telematics messages, and Azure Stream Analytics to process the messages. The pipeline also uses AI technologies to enable predictive maintenance, customer and employee experience, and autonomous vehicles.

As we dive into the process of knowing how real-time data are collected, let’s explain it in a more simple and detailed way. The steps in collecting data in real-time in smart cars are:-

1. Smart cars with various sensors and devices can see, hear, and sense everything around them. For example, with cameras, radar, lidar, ultrasonic sensors, and GPS.
2. The data is collected from these sensors and devices from the car’s surroundings, such as the speed, location, traffic, road conditions, obstacles, and other vehicles.
3. In the next step, data is then sent to the car’s computer, processing and analyzing the data in milliseconds. The computer also uses AI algorithms to make decisions based on the data, such as adjusting the speed, changing the route, or avoiding collisions.
4. In the real-time process, the data is also sent to the cloud, where it can be stored, shared, and accessed by other smart cars, devices, or services. It also helps improve the car’s performance, design, and user experience.

As we understand the role of Azure Sphere in real-time data collection as an example, let’s scrutinize the role of Azure Sphere and Azure IoT Hub in managing telematics messages, and Azure Stream Analytics in Capgemini.

• Azure Sphere, is a solution providing secure and scalable IoT connectivity for smart cars. It consists of a device running on a Linux-based OS, a cloud-based security service, and a development platform. The main role is its ability to send telematics messages (such as speed, location, and fuel level from the car to Azure IoT Hub, which is a service enabling reliable and secure communication between IoT devices and the cloud.
• Azure IoT Hub also receives OBD-II data (such as engine temperature, oil pressure, and emissions) from the car’s OBD-II port, connected to an Azure Sphere device. Azure IoT Hub also routes the messages to a different destination, such as Azure Stream Analytics, a service that can process and analyze large volumes of streaming data in real-time.
• Azure Stream Analytics uses SQL queries to perform various operations on the streaming data, such as filtering, aggregating, joining, and transforming. It also uses AI technologies enabling predictive maintenance, customer and employee experience, and autonomous vehicles. It can send the processed data to various storage systems or services, such as Azure SQL Database, Azure Cosmos DB, Azure Synapse Analytics, or Power BI.

So, the total package is used by Capgemini to provide IoT solutions in the automotive industry. It developed a real-time data ingestion/processing pipeline using Azure Sphere, Azure IoT Hub, and Azure Stream Analytics to ingest and process messages from IoT devices into a big data analytic platform in Azure.

The pipeline provides the advantage to Capgemini to help clients to improve their products and services such as vehicle performance, design, and user experience.

Also, the term OBD-II data is used quite often in the automotive industry. It means the data that comes from the On-board diagnostics (OBD) system of a car. The OBD system is a standard feature in most modern cars that monitors the performance and condition of various parts of the car, such as the engine, emissions, fuel system, and brakes.

The OBD systems also report any problems or faults in the car using diagnostic trouble codes (DTCs). Usually, the OBD system has a data link connector (DLC), accessed by an external device, such as a scanner, a dongle, or an Azure Sphere device. The DLC communicates with the external device using different protocols, such as CAN, ISO, or PWM.

The role of Azure IoT Hub is to enable reliable and secure communication between IoT devices and the cloud. Azure IoT Hub receives OBD data from external devices and routes it to different destinations such as Azure Stream Analytics, Azure SQL Database, Azure Cosmos DB, or Power BI. It’s quite powerful as it can also manage the devices, such as updating their firmware, sending commands, or monitoring their status.

In a nutshell, OBD-II data comes from the car’s OBD system, and Azure IoT Hub is the cloud service receiving and managing the OBD data from the external device.

Sources:- capgemini, SmartdataCollective, intellias, Microsoft, aimprosoft