Analyzing the security of ZigBee communication in smart environments via SDR

2025-10-18T08:23:40+00:00

The development of smart cities is accompanied by the widespread adoption of wireless
networks based on the ZigBee protocol, due to its energy efficiency and compatibility with Internet
of Things architectures. However, the active use of this protocol in an open radio environment
increases the risk of unauthorized radio-frequency interference. The study aims to experimentally
assess the vulnerability of ZigBee networks to targeted jamming using software-defined radio. The
paper presents the stages of preparing a test environment with real devices, identifying the active
data transmission channel, and generating tone interference using the SDR platform HackRF One
and the GNU Radio environment. The conducted experiment showed that, when affecting a specific
frequency, up to 95% of packets may be lost, rendering the network inoperable. The obtained
results confirm the critical vulnerability of the ZigBee protocol at the physical layer and highlight
the need to develop additional protection mechanisms for wireless IoT networks, especially within
urban infrastructure. The proposed methodology can be used to test the resilience of devices in
practical scenarios and to support the development of monitoring systems capable of detecting and
withstanding external attacks.

Optimal C(N)D-recovery of Functions from the Anisotropic Generalized Sobolev Class

2025-10-11T18:43:00+00:00

In this paper, the problem of recovering functions from an anisotropic generalized Sobolev class in the context of the computational (numerical) diameter is completely solved. The anisotropic generalized Sobolev class $W_2^{\omega_{r_1}, \ldots, \omega_{r_s}}$, which arises from the classification of periodic functions according to the rate of decay of their trigonometric Fourier coefficients, represents a finer scale of function characteristics than the anisotropic Sobolev class
$W_2^{r_1, \ldots, r_s}$ in the power scale. The paper presents two-sided estimates, up to multiplicative constants, for the approximation error of functions from the considered class by computational aggregates constructed from information given in the form of linear functionals.
In addition, based on trigonometric Fourier coefficients, the optimal computational aggregate is explicitly constructed. It is noted that the set of computational aggregates $(l^{(N)}, \varphi_N)$ used in the lower bounds is sufficiently wide, containing all partial sums of Fourier series with respect to various orthonormal systems, all possible finite convolutions with special kernels, as well as all finite approximation sums used in orthogonal widths, linear widths, and greedy algorithms. Furthermore, in the second part of the paper, the limiting error $\overline{\varepsilon}_N$ of numerical information in the form of trigonometric Fourier coefficients is obtained, which preserves the optimality of the computational aggregate and cannot be improved in order. In the third part, it is proved that all computational aggregates constructed using trigonometric Fourier coefficients do not have a limiting error greater than $\overline{\varepsilon}_N$.

Bulletin of L.N. Gumilyov Eurasian National University. Mathematics, computer science, mechanics series

Analyzing the security of ZigBee communication in smart environments via SDR

Optimal C(N)D-recovery of Functions from the Anisotropic Generalized Sobolev Class