APOLLO

The IoT is a rapidly growing market able to capture vast amounts of data from fixed and moving sensors. The processing of such data—whether in real time or on data-at-rest—allows to derive key value adding information, which instigate in turn the creation of a large range of private or public services. 

In this context, the autonomy of the IoT device is critical to keep a continuity in the service and minimise maintenance needs. Indeed, since IoT devices are typically untethered, they must survive either on a battery or on power harvested from their environment.

Minimising the energy consumption of IoT devices is thus a major challenge for their manufacturers: while larger systems can afford microwatt – milliwatt average power, millimeter-sized devices must survive on nanowatt power budgets. Moreover, the ability to calculate the GNSS position of IoT objects with a very small energy footprint will pave the way for a market of tens of millions of moving objects each year. This is where the APOLLO project comes into play. 

The APOLLO (Accurate GNSS POsitioning for Low power and Low-cost Objects) project aims at providing a low consumption Galileo-based geolocation solution for the IoT market, dividing the bill of materials of the GNSS function by 4 and by dividing its energy balance by a factor comprised between 10 and 300. 

To reach such performance, the consortium developed a 100% software GNSS receiver able to run on any IoT application processor implementing powerful Cloud distribution methods to host most of the location processing on a remote server.

Challenge and technical solution

A low-cost and low-power GNSS-based software location solution that can be installed and run on generic low-cost IoT Microcontroller (MCU), such as STMicro STM32 or NXP K80 (both using a CORTEX M4 core) and that can be easily optimised depending on the application (e.g., configurable trade-off between energy consumption & accuracy).

The APOLLO System offers unique performance in terms of cost and energy consumption, while providing significant robustness and accuracy, enabling its use in mass-market IoT.