International Journal of Emerging IoT Technologies in Smart Electronics and Communication

Edge AI and TinyML: Powering the Next Generation of IoT

2026-01-12T11:40:11+00:00

The rapid expansion of the internet of things (IoT) has led to an unprecedented increase in data generation at the network edge, exposing critical limitations of traditional cloud-centric computing models. High latency, excessive bandwidth usage, energy inefficiency, and growing privacy concerns make centralized processing unsuitable for many real-time and safety-critical IoT applications. To address these challenges, intelligence is increasingly being shifted closer to data sources through Edge artificial intelligence (Edge AI) and Tiny machine learning (TinyML). This study presents a comprehensive review of these emerging paradigms and their role in enabling scalable, efficient, and privacy-preserving intelligent IoT systems. Edge AI enables real-time inference directly on edge devices, reducing dependence on remote servers and allowing faster decision-making. TinyML extends this concept further by enabling machine learning models to run on highly resource-constrained hardware such as microcontrollers and sensors, often operating with kilobytes of memory and milliwatt-level power budgets. The study discusses key model optimization techniques—including quantization, pruning, and knowledge distillation—that make it feasible to deploy deep learning models on such constrained platforms with minimal accuracy degradation. In addition, the study examines the importance of hardware-software co-design in Edge AI systems. Specialized hardware accelerators, neural processing units, and optimized system-on-chip architectures are reviewed alongside lightweight software frameworks such as TensorFlow Lite for Microcontrollers, STM32Cube.AI, and Edge Impulse. Benchmark analyses are used to highlight trade-offs between inference latency, memory footprint, and energy consumption across different deployment platforms.

A Self-supervised Framework for Generalized RF Fingerprinting and Zero-shot Anomaly Detection in Dynamic IoT Networks

2026-03-07T06:06:13+00:00

Radio frequency (RF) fingerprinting leverages hardware-induced signal imperfections to uniquely identify devices, but conventional supervised deep learning methods degrade sharply under realistic conditions, including few-shot labeled data, open-set environments, channel distortions, temporal drift, modulation diversity, and dynamic operational settings, challenges prevalent in large-scale IoT, UAV swarms, and tactical networks. This work introduces RF-SSL, a semi-supervised framework designed for robust few-shot device identification and zero-shot anomaly detection amid extreme data scarcity and diverse RF impairments. RF-SSL integrates domain-informed augmentation (DIA) simulating channel effects, hardware imperfections, operational variations, and invariance-preserving transforms. It combines multi-task and contrastive self-supervised pretraining on unlabeled RF signals with shallow-to-medium CNN fine-tuning, with hyperparameters optimized for contrastive temperature (τ ≈ 0.1) and learning rate (1×10⁻³). Evaluation spans few-shot accuracy (1–20 shots), zero-shot anomaly AUC, and robustness to SNR, channel models, temporal drift, unseen modulations, convergence speed, and ablation studies. RF-SSL uniquely unifies physics-informed, multi-category augmentation with multi-task contrastive pretraining, providing systematic, multi-dimensional benchmarking under simultaneous data scarcity, drift, and modulation shifts. Results show ~3× faster convergence, 15–40% improved robustness under impairments and drift, and better preservation of performance on unseen modulations (few-shot accuracy up to 0.95, anomaly AUC ~0.88). Ablations confirm DIA and contrastive pretraining as principal contributors, while shallow CNNs optimize speed, accuracy, and robustness. Overall, domain-informed semi-supervised learning significantly enhances data efficiency, temporal stability, and generalization in RF fingerprinting, though absolute performance remains constrained in extreme impairment scenarios.

An IoT-enabled Intelligent Indoor Plant Health Monitoring and Recommendation Framework

2026-03-06T07:20:45+00:00

Indoor gardening has gained popularity due to its positive impact on mental well-being, stress reduction, air purification, and environmental sustainability. Indoor plants enhance emotional health, improve concentration, and create a calming atmosphere. However, maintaining healthy indoor plants is often challenging. Busy lifestyles limit the time available for plant care, and many individuals lack sufficient knowledge about proper watering, lighting, temperature, and humidity management. As a result, plants may suffer from improper care, leading to poor growth or early damage. To address these challenges, the smart mindful garden is proposed as an IoT-based system designed to monitor and support indoor plant health. The system uses an ESP32 microcontroller integrated with soil moisture, temperature, humidity, and light intensity sensors to collect real-time environmental data. This data is transmitted through Wi-Fi to a cloud-connected web platform for monitoring and analysis. The system applies rule-based artificial intelligence using predefined threshold values based on plant care guidelines. When environmental conditions deviate from optimal ranges, the system generates alerts and recommendations, such as watering reminders or light adjustment suggestions. A web-based dashboard displays real-time readings, historical trends, and notifications in a user-friendly format, enabling remote monitoring and informed decision-making for effective indoor plant care.

Performance Investigation of UAV-aided FSO Communication System under Diverse Weather Conditions

2026-04-22T11:44:36+00:00

The demand for unmanned aerial vehicles (UAVs) has surged significantly, primarily due to their ability to access remote and restricted areas. UAVs are extensively utilised in applications such as military surveillance, disaster response, backhaul communication, and geospatial mapping. In addition to that, free space optical (FSO) communication offers a promising high- bandwidth, line-of-sight (LoS) high-speed data transfer with unlicensed spectrum and secure connections as an alternative to traditional wireless systems and conventional radio frequency (RF) systems. Thus, integrating UAVs with FSO technology offers a versatile, dependable, and high-speed communication platform. However, its performance is highly susceptible to atmospheric conditions, like fog, snow, and rain. Through detailed simulation and analysis, the performance of the UAV-integrated FSO system has been analysed in this study under diverse atmospheric conditions, employing eye diagrams to visualise the impact of signal attenuation and degradation. The findings indicate that system performance is significantly influenced by atmospheric conditions. Among these, foggy conditions have the most detrimental impact on system performance due to Mie scattering, followed by snow and then rain.