The bio-edge: a survey and research agenda for the Internet of Bio-Nano Things, 2026–2035 Serhiy O. Semerikov, Tetiana A. Vakaliuk Journal of Edge Computing, 2026 The Internet of Bio-Nano Things (IoBNT) extends the Internet of Things into the biochemical domain of living systems through nanoscale bio-engineered devices that sense, actuate, and communicate primarily via molecular signalling. Eleven years after the founding vision of Akyildiz et al. [4], the field has accumulated working architectures, microfluidic testbeds, and mature channel models, but its system-integration challenges - latency, privacy, and energy - are substantially edge-computing challenges: in-body decision loops cannot tolerate cloud round-trip latency, biomolecular data cannot safely stream to remote servers, and harvested-power devices cannot continuously transmit raw high-rate signals. This survey reframes the IoBNT layer stack as a five-layer bio-edge reference architecture in which the bio-cyber interface (BCI, distinct from brain-computer interface) is upgraded from a transduction gateway to a first-class compute layer with its own latency, energy, and trust accounting. We construct a DOI-deduplicated bibliometric snapshot of 311 entries, identify three under-occupied subtopics that constitute the field's strategic white space - TinyML on harvested power, federated learning across edge gateways, and Bio-SDN orchestration - and survey the technical state of each. The centrepiece is a ten-prediction research agenda for 2026-2035 with each prediction stated as a dated metric, a causal mechanism, and a falsifier, designed to give the IoBNT community a structured object that subsequent work can measure itself against.
SIMPLIFIED CONVOLUTIONAL MODEL FOR DETECTING VIBRATION ANOMALIES IN HELICOPTERS OPERATION Yurii Hodlevskyi, Tetiana Vakaliuk Radioelectronic and Computer Systems, 2026 The challenge in vibration analysis for helicopters is to automate anomaly detection to indicate possible equipment failure, especially in high-stakes scenarios where labelled failure data is rare and highly desirable for training modern models such as convolutional neural networks or autoencoders which require a lot of such data and are often used for this kind of analysis. The study focuses on vibration data generated by helicopters, focusing on anomalies in these vibrations that may indicate imminent equipment failure. These vibrations are often complex and change under different operating conditions, making them a valuable source of diagnostic information. Objective and Approach. This study presents a novel model for detecting abnormal vibrations in helicopter operations, minimising dependency on failure data. The model uses Fast Fourier Transform (FFT) and convolutional techniques to reduce high-dimensional vibration data into a three-dimensional vector, enabling effective anomaly detection through distribution metrics and Z-score-based thresholding. Case Study. The model was evaluated using a real-world dataset of helicopter vibration signals. The study successfully differentiated between normal and abnormal conditions without relying on explicit failure labels, validating its applicability in maintenance scenarios where early fault detection is critical. Method. Model was developed using Fast Fourier Transform (FFT) and convolutions to reduce vibration data to a low-dimensional vector representation that allows anomaly detection using distribution metrics. This method increases adaptability and reduces data requirements, making it suitable for expensive equipment with limited or no fault data. In addition, this model can be very flexible and extended to a model with additional training. It uses the Fast Fourier Transform as the first step to get the frequency components of a signal buried in vibrations. After FFT, we can represent the vibration data as a multidimensional vector. An example can consist of around 45000 dimensions after that vector simplifies to reduce the dimensions. The model uses convolutions to get only a 3-dimensional vector from the 45,000-dimensional vector. After convolutions, the data saves a clear correlation, and it is still possible to represent the vibration sample as a vector in 3-dimensional space and save its properties. Three dimensions were chosen for visualisation comfort, and it still works correctly. These vectors are located in space in two separate clusters for 0 and 1 corresponding labels, and they can be used for the next iteration of analysis. On the next iteration, the model gets a mean from x,y, and z and checks the distributions for class 0 and class 1. This step is possible because values after all convolutions for x,y, and z are close to each other. As distributions of these vectors are close to a bell shape, the model uses distribution metrics to predict fault by using a Z-score of the distribution of normal vectors. In this case, the model is close to the zero-shot learning concept because the model requires only data about expected behaviour. Results. The developed model showed high accuracy at a particular configuration when using different matrix convolutions and a different approach to setting the limit of anomalous values. At the final stage of the testing, the model reached an accuracy of 99.568%, which, under specific conditions, is not considered an overfitted model since the initial input parameters were clearly defined and limited by specific data. The model also demonstrated that when changing the input conditions, the model can be easily configured to new parameters. Discussion. The proposed model proved highly adaptable and accurate in detecting anomalies with minimal historical data. While it showed robustness across different configurations, future improvements could focus on refining sensitivity thresholds and integrating additional predictive techniques for enhanced performance. Conclusions. Tests validate the model's adaptability and scalability, recommending its use for fault detection in inexpensive, data-limited environments like helicopters.
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