Distributed Estimation of Sensor Statistics Using Wireless Networks of Single-Transistor Chaotic Oscillators

Efficient aggregation of readings from numerous nodes is essential for large-scale applications of wireless sensor networks. This study provides the first systematic numerical investigation of a network paradigm in which sensing and distributed computation are embedded directly into the physical layer through the collective dynamics of coupled chaotic oscillators. A minimalist, single-transistor chaotic circuit characterized by rich nonlinear dynamics and high sensitivity to supply voltage variations is considered. This sensitivity enables each node to translate local physical signals from a transducer into perturbations of its oscillatory state. Networks of these oscillators arranged in irregular two-dimensional geometries are simulated with coupling implemented via near-field inductive links. The influence of coupling strength and node-to-node supply voltage variability on collective dynamics is analyzed. While increased coupling elevated the level of partial synchronization, resulting in sharper spectral signatures, variability had a blurring effect; this was accompanied by a gradual shift in spectral shape determined by the average voltage. The statistical properties of a distributed physical variable, namely its mean and variability, could be reliably recovered from these signals using listeners operating either in near-field or far-field antenna configurations, via a neural network-based approach. We further investigated frequency-division and time-division multiplexing techniques as scalable strategies for practically realizing long-range couplings. This study demonstrates the feasibility of embedding distributed sensing based on minimalist chaotic circuits.