IoV: the pioneering union of IoT and the automotive industry


No traffic. The lights are always green and accidents are rare. Does this seem utopian to you? It certainly is, but if the Internet of Vehicles (IoV) market takes off, this scenario may soon become a reality. According to Allied Market Research, the global IoV market was worth $ 66,075 million in 2017 and is expected to grow to $ 208,107 million by 2024, with a CAGR of 18.00%.

IoV is closely related to the Internet of Things (IoT), which is made up of devices that are connected so that they can share data and interact with us. IoV is not much different; it focuses on vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication rather than human interaction. As a new technology, IoV has a lot of potential, but what exactly does IoV entail?

What is the Internet of Vehicles?

IoV is the next step in the evolution of vehicular ad hoc networks (VANETs). A VANET is a cluster of mobile nodes (vehicles) using ad hoc on-demand connections to communicate.

The researchers demonstrated that in VANET networks, vehicle-to-vehicle and vehicle-to-road communication networks coexist to provide navigation, road safety and other services on the road. VANETs are an essential component of the framework for intelligent transport systems. VANETs are sometimes referred to as intelligent transport networks. They have evolved into full IoVs that will eventually turn into a global internet of self-driving cars.

Vehicles connect to the internet through a wireless local area network (WLAN) and connected cars are part of IoT and IoV. This allows for remote control of certain aspects and interaction with other devices, traffic infrastructure and third-party products. For example, drivers receive traffic alerts, they can also plan routes, and the connected vehicle can automatically adjust cruise control for better traffic management.

IoV has the potential to improve driver safety, reduce congestion and reduce emissions.

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IoV architecture

There are many ways to implement and design IoV systems, but the IoV architecture must have the following four layers for proper IoT functionality.

  1. Perception

It is important to consider environmental factors when developing IoT systems. For example, an IoV car needs to detect obstacles, other vehicles, and human movement to avoid people or objects that may pose a threat.

If an object approaches too quickly, the driverless car must slow down to avoid collisions. The vehicle itself should also be monitored for any signs of failure or poor performance; it wouldn’t make sense for drivers to rely heavily on IoT if their cars are unreliable.

All vehicle sensors are included in this layer. In addition, the perception layer gathers environmental information to detect events, driving habits and situations. It also has radio frequency identification (RFID) and the ability to detect the environment, vehicle position and other objects on the road.

  1. Network

This is the communication layer that provides all the necessary connectivity. It includes 3G / 4G LTE / 5G cellular technologies, WLAN, Bluetooth and Wi-Fi to communicate between the car, other devices and the infrastructure.

5G, in particular, is essential for the take-off of the IoV. 5G functionality will support high data rates and bandwidth, low latency and reliable connection. In addition, the 5G IoT cellular standard enables a more efficient and efficient transfer of large amounts of data in real time between multiple devices in the car.

  1. Artificial Intelligence (AI)

Once IoV has gathered all the data, it should be used to make decisions and allow the car to react accordingly. This is where AI becomes a key part of IoT as it enables prediction, decision making and execution of actions. It includes big data analysis software, specialized systems (computer vision applications in driverless cars to identify objects on the road) and cloud computing components.

The AI ​​layer has an integrated cloud infrastructure and requires smooth connectivity between processing services and low-level system components.

  1. Application

Finally, the application layer applies the results of the AI ​​layer. This is where the data collected by perception is processed through applications such as collision avoidance software. It is the user side of the IoT and can be used for multiple purposes such as entertainment, GPS navigation, and in-car services.

The application layer includes all network compatible applications within the IoV system. It also includes telematics (GPS), infotainment and general car functions (such as engine performance monitoring).

The IoV also includes the Global Identification Terminals (GID) of all connected vehicles. It solves all the problems associated with RFID, including slow speeds and limited coverage. Additionally, GIDs give vehicles digital identifiers that are essential for vehicle cybersecurity.

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Examples of IoV applications

Below are some examples of specific use cases considered to be pioneers in the IoV automotive industry.


Uber is the most popular ridesharing company. Its IoV and autonomous unit, Advanced Technologies Group (ATG), has made great strides in IoV and autonomous vehicles.

One of the areas where Uber is working to leverage IoV to gain competitive advantage, improve customer service, and return to profitability is transportation costs.

According to projections by research firm Frost & Sullivan, the driver represents 80% of the overall cost per kilometer of non-autonomous carpooling. Fully autonomous cars will dramatically reduce the price of a trip while expanding their addressable market by removing the driver from the equation. Uber is already providing software as a service and plans to take the risk further by cutting travel costs so low (between its fleet of human and robotic cars) that owning a vehicle becomes impractical.

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Tesla, the Wunderkind of the IoV, is on a mission to accelerate the global transition to sustainable energy. The Tesla team aims to achieve this goal by making autonomous electric vehicles safe, affordable and scalable.

Their approach combines multiple technologies on a single platform: an Advanced Driver Assistance System (ADAS), automated controls allowing vehicles to operate without human intervention, and fleet routing using machines controlled by AI (for example, autonomous buses or fleets of commercial trucks).


CarStream is a large data processing system for chauffeured car services. CarStream collects and analyzes various types of driving data, including vehicle condition, driver behavior and passenger travel information. Based on the collected data, various services are provided. In the last three years of industrial use, CarStream has collected over 40 terabytes of driving data.

CarStream also provides critical services for IoV security and has developed a three-tier monitoring system. The monitoring subsystem covers the application layer up to the infrastructure layer. To solve the problems of real-time processing, big data, low data quality, and low-value storage, CarStream uses in-memory caching and stream processing. As a result, a diverse storage system manages a massive amount of driving information.

IoV as an emerging sector

IoV is an emerging sector with the potential to profoundly affect the automotive industry. The applications range from navigation, entertainment, in-vehicle services; all the way to fleet routing using AI-controlled machines. With everything at your fingertips via the IoT (Internet of Things), it’s no surprise that devices like cars are getting smarter too!

Consumers can expect more safety features and lower costs for car ownership from innovations like Uber’s autonomous vehicles. Other benefits include a cleaner environment and a better quality of life.

Read more : Best IoT Device Management Platforms and Software 2021


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