Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study

Optimal Linear Fusion Rule for Distributed Detection in Clustered Wireless Sensor Networks

In this paper we consider the distributed detection of intruders in clustered wireless sensor networks (WSNs). The WSN is modelled by a homogeneous Poisson point process (PPP). The sensor nodes (SNs) compute local decisions about the intruder's presence and send them to the cluster heads (CHs). Hence, the CHs collect the number of detecting SNs in the cluster. The fusion center (FC), on the other hand, combines the the CH's data in order to reach a global detection decision. We propose an optimal cluster-based linear fusion (OCLR), in which the CHs' data are linearly fused. Interestingly, the OCLR performance is very close to the optimal clustered fusion rule (OCR) previously proposed in literature. Furthermore, the OCLR performance approaches the optimal Chair-Varshney fusion rule as the number of SNs increases