Classical sensor fusion approaches require to work directly with the hardware and involve a lot of low-level programming, which is not suited for reliable and user-friendly sensor fusion for Internet of Things (IoT) applications.
In this paper, we propose and analyze Hera, a Kalman filter-based sensor fusion framework for Erlang.
Hera offers a high-level approach for asynchronous and fault-tolerant sensor fusion directly at the edge of an IoT network.
We use the GRiSP-Base board, a low-cost platform specially designed for Erlang and to avoid soldering or dropping down to C.
We emphasize on the importance of performing all the computations directly at the sensor-equipped devices themselves, completely removing the cloud necessity.
We show that we can perform sensor fusion for position and orientation tracking at a high level of abstraction and with the strong guarantee that the system will keep running as long as one GRiSP board is alive.
With Hera, the implementation effort is significantly reduced which makes it an excellent candidate for IoT prototyping and education in the field of sensor fusion.