Angle-of-Arrival (AoA) methods are an Internet of Things (IoT) application, which could be used, for example, in indoor localization. Anchor nodes have an array of antennas and could send the data via Ethernet cable to the cloud that calculates AoA. However, having cable connections means high installation costs, and constantly transferring big chunks of data over some IoT networks, such as mesh, is energy inefficient. The solution of this paper consists in executing AoA locally in anchor nodes. Thus, the paper presents an implementation of a Multiple Signal Classification (MUSIC) algorithm tailormade for embedded system devices. It calculates a complex eigendecomposition via an equivalent real formulation. It has a detailed memory analysis of the implemented solution that shows its memory requirements satisfy commercial embedded systems for IoT, such as Nordic semiconductor System-on-Chip (SoC) of nRF52 Series and all their SoCs with direction-finding capability. Experiments show that reducing the floating-point precision to shrink its memory footprint does not impact the accuracy. It also shows that minimizing the execution time of its time-consuming peak-finding operation has a few effects on accuracy.
This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices.
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