Machine learning has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying machine learning 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 machine learning (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 machine learning approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

Wireless local area networks (WLANs) being the most common access technology, the use of software-defined networks (SDN) in WLANs is inevitable as SDN paradigm has been widely accepted and implemented in the networking industry. Currently, the main focus of SDN is on wired networks. OpenFlow, the most widely accepted protocol for SDN, does not fully support wireless medium, and thus, software-defined WLANs (SDWLANs) have not gained enough attention from the research community. We believe that SDN cannot only solve the long-existing issues for WLANs, but also enable researchers to bring more advanced techniques for higher data rates, better security, and quality of experience. In this article, we have emphasized the importance of integrating SDN into WLANs, and the benefits WLANs can gain by adopting SDN. More specifically, we have explained the advantages as well as the implementation details of SDN for WLANs along with the most prominent recent research efforts and contributions.

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 article, 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 multiagent asynchronous advantage actor–critic (A3C)-based joint resource allocation algorithm aimed at reducing latency and energy utilization within the Internet of UAVs (IoUAVs) network. Through extensive simulations we comprehensively examine the efficacy and performance of our proposed framework.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem’s non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.