Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.

The exponential growth in connected devices with Internet-of-Things (IoT) and next-generation wireless networks requires more advanced and dynamic spectrum access mechanisms. Blockchain-based approaches to Dynamic Spectrum Access (DSA) seem efficient and robust due to their inherited characteristics such as decentralization, immutability, and transparency. However, conventional consensus mechanisms used in blockchain networks are expensive to be used due to the cost, processing, and energy constraints. Moreover, addressing spectrum violations (i.e., unauthorized access to the spectrum) is not well-discussed in most blockchain-based DSA systems in the literature. In this work, we propose a newly tailored energy-efficient consensus mechanism called “Distributed-Proof-of-Sense (DPoS)” that is specially designed to enable DSA and detect spectrum violations. The proposed consensus algorithm motivates blockchain miners to perform spectrum sensing, which leads to the collection of a full spectrum of sensing data. An elliptic curve cryptography-based zero-knowledge proof is used as the core of the proposed mechanism. We use MATLAB simulations to analyze the performance of the consensus mechanism and implement several consensus algorithms in a microprocessor to highlight the benefits of adopting the proposed system.

Micro-unmanned aerial systems (micro-UAS) pose emerging security risks when operated near critical infrastructure, yet most existing detection solutions rely on active radar, vision, or acoustic sensing that are constrained by line-of-sight, environmental noise, or regulatory limits on transmissions. This paper investigates a passive sensing approach based on extremely low frequency (ELF) magnetic emissions generated by the brushless DC (BLDC) motors of micro-UAS.We design a sensitive ELF circular loop antenna and low-noise analog front-end, and we exploit the cyclostationary structure of the motor drive signals through spectral correlation function (SCF) estimation using the FFT accumulation method. SCF maps are then processed by a VGG16-based convolutional neural network for both detection and multi-class drone-type identification. Experiments with three commercial micro-UAS platforms, at ranges up to 15 m, yield up to 98.5% outdoor detection accuracy and 83.5% overall accuracy for four-class identification (three drone types plus no-drone), with reduced performance for the smallest platform due to weaker ELF emissions. The results demonstrate that ELF passive radio sensing, combined with deep learning on SCF-derived features, is a viable and stealthy solution for micro-UAS surveillance in scenarios where active transmission or optical or acoustic sensing are impractical.

Bearing fault diagnosis in rotating machinery is critical for ensuring operational reliability, therefore early fault detection is essential to avoid catastrophic failures and expensive emergency repairs. Traditional methods like Fast Fourier Transform (FFT) often fail to capture the complex, non-stationary nature of vibration signals. This study leverages the cyclostationary properties of vibration data through Spectral Correlation Density (SCD) images to enhance fault detection and apply deep learning for classification. Using a publicly available dataset with bearing faults seeded in two distinct housings (A and B) under varying load conditions (0 Nm, 2 Nm, 4 Nm), we processed vibration signals into 2D SCD images to reveal fault-specific periodicities, such as broadband spectra (2000--8000 Hz) for larger faults. Three convolutional neural network (CNN) models, Custom CNN, ResNet152V2, and EfficientNetB0, were developed to classify seven bearing conditions. The custom CNN achieved the highest accuracies of 96.58\% and 94.95\% on Housing A and B, respectively, followed by ResNet152V2 at 96.49\% and 95.35\%, and EfficientNetB0 at 94.16\% and 91.65\%, respectively. The models' high accuracies across different housings demonstrate a robust solution suitable for cost-effective condition monitoring deployable near sensing platforms, contributing to applied machine learning for edge intelligence and showcasing effective signal processing strategies for handling complex, potentially large-scale vibration data.