Underwater networking is vital for enabling collaboration between divers, vehicles, and sensors in marine exploration, monitoring, and emergency response. Yet achieving reliable communication in such dynamic, bandwidth constrained environments remains challenging. Acoustic and radio frequency technologies suffer from attenuation, latency, and hardware overhead, while optical wireless systems typically require specialized transceivers or strict alignment, limiting practicality in mobile underwater networks. To address these limitations, we present AquaLink, a QR code–driven Optical Camera Communication (OCC) framework that enables robust underwater messaging using commodity smartphones and tablets. At its core, AquaQR employs blue–green 2-bit color encoding, Low-Density Parity-Check (LDPC) error correction, and geometric augmentations tailored for optical stability in turbid waters. An auto-configuration module adapts parameters before transmission, and a lightweight enhancement pipeline ensures real-time robustness under diverse conditions. Field trials in pool, lake, and coastal environments achieve over 90% decoding success at 5 m and up to 2× higher throughput than prior QR-based systems. By eliminating specialized hardware, AquaLink provides a scalable, low cost foundation for underwater visual networking, supporting message exchange, peer interaction, and localized link formation.

The Internet of Drones (IoD) represents an emerging paradigm of the Internet of Things (IoT), enabling seamless, coordinated communication among drones and integration with other connected systems. This interconnected network enables autonomous decision-making among drones. As this IoD paradigm continues to expand it faces significant challenges due to its reliance on public channel. Therefore, existing methods often suffer from impersonation, cloning, anonymity violation, and fails to offer end-to-end key secrecy. Moreover, they require high computation resources which present challenges of deployment in resource-constrained IoD environment. To address these challenges, we propose a secure and efficient protocol that provides mutual authentication among participating entities. The protocol resists impersonation, cloning, anonymity violation and offers end-to-end key secrecy. The protocol employs Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC), along with a fuzzy extractor, to ensure secure communication. The resilience of the proposed protocol was evaluated through an informal analysis. Its security properties were rigorously verified using formal analysis based on the Real-Or-Random (ROR) model. Evaluation of its performance shows that the proposed protocol outperforms existing solutions, achieving reductions of 20.9% in computation cost and 32.6% in communication overhead. The results demonstrate that the protocol improves security and provides reliability under the evaluated scenarios of IoD systems.

The integration of Artificial Intelligence (AI) with Low Power Wide Area Networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid Tiny Machine Learning (TinyML) and Deep Neural Network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model implemented on the network server, combined with TinyML models deployed on edge devices. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable edge devices to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments.

Machine learning (ML) has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying ML algorithms on resource-constrained edge devices poses significant challenges due to their limited computational capabilities and storage capacities. In this study, we propose leveraging tiny ML (TinyML) techniques to address these challenges and optimize channel allocation within long range wide area network (LoRaWAN) deployments. Our key innovation lies in replacing traditional random channel allocation methods with TinyML-based approaches, wherein each edge device autonomously utilizes TinyML to select the most efficient channel prior to each uplink transmission. Furthermore, we conduct comprehensive comparisons between TinyML and conventional channel allocation techniques implemented on edge devices. Through extensive simulations, our results demonstrate that TinyML outperforms existing channel allocation mechanisms in terms of packet success ratio (PSR). Notably, when evaluating TinyML against conventional ML approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

The use of Unmanned Aerial Vehicles (UAVs) as flying base stations to support various tasks, such as data collection, machine learning (ML) model training, and wireless communication in Internet of Things (IoT) networks, has garnered significant attention in recent years. Nonetheless, several challenges have arisen in this context, including data privacy concerns and limited onboard computational and communication resources. These challenges make the direct transmission of raw data to a central server for training impractical. Moreover, UAV-based networks are susceptible to fluctuating channel conditions and the heterogeneous computing capabilities of IoT devices. Therefore, enhancing the reliability and efficiency of such networks is imperative. In this paper, we introduce a novel framework known as the Asynchronous Federated Framework for IoT-enabled UAV (AF3N) networks. AF3N enables local model training with subsequent parameter transmission to the Mobile Edge Computing (MEC) server. To further enhance learning efficiency, we incorporate a device selection strategy into the AF3N framework. Additionally, we employ a multi-agent Asynchronous Advantage Actor-Critic (A3C)-based joint resource allocation algorithm aimed at reducing latency and energy utilization within the Internet of UAVs (IoUAV) network. Through extensive simulations we comprehensively examine the efficacy and performance of our proposed framework.