The properties of chaotic signals provide broad application opportunities in different areas. For example, [12] uses chaotic systems with high entropy for image encryption. In [13], chaotic tags and sensors that exploit the high sensitivity response to changes in chaotic circuit parameters are studied. In radar systems [14], chaotic signals also find practical application due to their wideband nature, thus providing low sensitivity to jamming and interference while being relatively simple to implement and process. Also, chaotic oscillators find their applications in digital communication systems [8], including digital link establishment in WSNs [15]. Some recent studies show a trend in developing new communication systems with an emphasis on improving the security of the transmission channel. The authors of [16] propose using a nine-dimensional modified Lorenz chaos generator in a chaotic masking communication scheme. The increased order chaos generator benefits because of the wider band of a chaotic signal, which is harder to distinguish from noise during transmission. Another approach to improve the secrecy of the communication channel is proposed in [17], where a novel symmetry-based chaotic modulation technique exhibits identical spectral characteristics for different bits transmission, making it more difficult to decode by eavesdroppers. The chaos signal sources can be divided into two classes--continuous-time and discrete-time systems. The dynamics of continuous-time chaotic systems are described using differential equations. This group mainly consists of analog chaos oscillators. The minimal requirement for the circuit to exhibit chaotic behavior is at least one nonlinear element, one active element, and three energy storage elements [18]. Examples of well-known chaos oscillators are Chua's circuit [18], the Rossler generator [19], and the Lorenz generator [20]. This work considers the Vilnius chaos oscillator [21] as more suitable for application in WSN and IoT due to circuitry simplicity and low energy consumption properties. This group also includes chaotic circuits for which chaos is a side effect of signal switching, as demonstrated in [22; 23; 24] for DC-DC converters. The discrete-time systems are described with difference equations and include chaotic maps--nonlinear functions that exhibit chaotic behavior [9]. The most notable examples of chaotic maps include the Logistic map [25], the Henon map [26], and the Gaussian map [27]. Discrete chaotic systems are also implemented by transforming the differential equations of continuous-time chaos oscillators into difference equations [28]. These benefits of chaos use in modern communication systems, including WSN and IoT (such as security aspects enhancement, spread-spectrum, multiple access, etc. ), and the variety of available approaches for generating chaotic signals led to the development of various chaos-based modulation schemes. Generally, chaos-based communication systems can be divided into two classes--modulation schemes with coherent and non-coherent detection [29]. Chaotic communication systems with non-coherent detection are based on manipulating chaos signal properties. They are more robust to channel noise than coherent systems but provide less signal protection as information decoding is mainly based on the difference of signals' statistical properties. The modulation schemes of this group are chaos on-off keying (COOK) [30] and differential chaos shift keying (DCSK) [31]. Chaotic communication systems that use coherent detection take advantage of chaotic synchronization--an approach of coupling two chaotic oscillators so that the state variables of the receiver's chaos signal source match those of the transmitter's chaos signal source. To establish chaotic synchronization, at least one of the state variables should be transmitted to the receiver.