Smart Cars: a practical implementation of M2M communications is becoming a reality ever closer

Machine to machine (M2M) communications, and especially Smart Cars, could help to improve accident prevention. McGill University has developed a pilot to handle remote control cars with Waspmote in order to decrease the number of car accidents caused by human error.

Road traffic fatalities are one of the most important causes of death globally. More than 150,000 people will be killed by 2020 according to the World Health Organization, since cars will be more present in developing countries, increasing the number of vehicles on the world´s roads up to 2 billion. A lot of technological innovations have improved road safety. Yet around 90% of accidents are caused by human error. Figure 1 shows percentage of road traffic accidents depending on the income group.

Fig. 1.- Population, road traffic deaths, and registered vehicles, by income group


Smart Cars, or even driverless cars, would provide further benefits beyond safety. They could drop someone off and then park themselves. Moreover, they would reduce the stress of driving, allowing their occupants to read or even work. Some studies carried out by the Institute of Electrical and Electronics Engineers (IEEE) reveal that by 2040, driverless cars will account for up to 75 per cent of cars on the road worldwide.

Fig. 2.- Smart Car developed by Google


Smart Cars are supposed to be totally developed in a few years. Yet many people do not trust in the technology enough to completely hand over total control. Thanks to small projects like the following, Libelium hopes people start changing their mind.

Research Project


The Department of Electrical & Computer Engineering of McGill University published a paper about a project developed last year. On the one hand, their short term goal was to create a prototype for a sensor car equipped with ultrasonic sensor and ZigBee that can be controlled remotely by user. On the other hand, their long term goal was to implement a mesh network with multiple cars to test Vehicle to Vehicle (V2V) communication.

Fig. 3.- Poster published by McGill University


They created a board to handle the remote control car using Waspmote through a ZigBee network. The ZigBee coordinator was connected to a computer in order to send commands directly to the car. Moreover, the car could work on two modes:

  • Accelerometer mode: remote control car uses the Waspmote´s accelerometer to know how it is moving. These data are sent to other Waspmote attached to a remote control, which answers back with the appropriate instruction. Thanks to the ultrasonic sensor and the instructions returned by the other Waspmote, the remote control car is able to follow a correct route.
  • On screen mode: a map of the room where the car is placed is uploaded to a computer application. This program creates the route for the remote control car and sends the instructions to the Waspmote on the car. Thanks to these instructions and ultrasonic sensor, the remote control car is able follow the route created on the computer.


M2M projects, and especially V2V ones, have a wide range of applications. Waspmote platform also allows to cover a wide range of different scenarios and applications thanks to its horizontality, resulting in a perfect solution to be used in M2M projects.

Waspmote for M2M communications


Machine to machine (M2M) refers to technologies that allow to communicate with other devices of the same ability. M2M uses a device to get data from its environment and sends this data to other device that transforms the received message into meaningful information. There are four basic stages that are common to almost every M2M application:

  • Getting the data from the environment
  • Transmission of selected data through a network
  • Treatment of data
  • Response to received information

Waspmote is Libelium´s device for Wireless Sensor Networks, and as you have seen in the previous part, it can be used for M2M applications. Apart from remotely control a car it can be used for a wide range of applications.

Fig. 4.- Waspmote device

More than 50 sensors are already integrated into our WSN platform. Regarding Smart Cars, an ultrasound sensor could be used to detect the proximity of a car to an object or even to other car. Furthermore, environmental sensors could collect data from car´s environment in order to create real-time pollution maps within a city.

Fig. 5.- a) Events Sensor Board   b) Prototyping Sensor Board  c) Smart Cities Sensor Board   d) Gas Sensor Board


Moreover, integration of new sensors is very easy thanks to Waspmote´s modularity and horizontality. We indeed offer a customer service to help you with this kind of integrations.

For any doubt about how to approach this solution do not hesitate to contact us.