Abdullahi Isa Ahmed

Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud

The rapid advancement of Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa) systems, has positioned them as a cornerstone for Next-Generation Internet of Things (NG-IoT) applications within 5G/6G ecosystems. Despite their long-range and low-power advantages, achieving high energy efficiency in LoRa networks remains a significant challenge in highly dynamic environments. Traditional terrestrial gateway deployments often suffer from coverage gaps and non-line-of-sight propagation, while satellite-based alternatives incur excessive energy consumption and prohibitive latency. To address these limitations, we propose a multi-UAV architecture where unmanned aerial vehicles (UAVs) serve as mobile LoRa gateways to dynamically collect data from ground-based end devices (EDs). We formulate a joint optimization problem to maximize the system's weighted energy efficiency by jointly optimizing spreading factors, transmission powers, UAV trajectories, and ED-UAV associations. This problem is transformed into a partially observable stochastic game (POSG), which we solve using our proposed Green LoRa Multi-Agent Proximal Policy Optimization (GLo-MAPPO). Our framework leverages centralized training with decentralized execution (CTDE) and is enhanced by a gain-based ED-UAV association scheme. Simulation results show that GLo-MAPPO significantly outperforms state-of-the-art multi-agent reinforcement learning (MARL) benchmarks in energy efficiency and power consumption across varying network densities. Furthermore, ablation studies validate the necessity of each optimization component and the effectiveness of the proposed association scheme.

Abdullahi Isa Ahmed, Ana Maria Drăgulinescu, El Mehdi Amhoud

The evolution of Internet of Things (IoT) into multi-layered environments has positioned Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa), as the backbone for connectivity across both surface and subterranean landscapes. However, existing LoRa-based network designs often treat ground-based wireless sensor networks (WSNs) and wireless underground sensor networks (WUSNs) as separate systems, resulting in inefficient and non-integrated connectivity across diverse environments. To address this, we propose Hetero-Net, a unified heterogeneous LoRa framework that integrates diverse LoRa end devices with multiple unmanned aerial vehicle (UAV)-mounted LoRa gateways. Our objective is to maximize system energy efficiency through the joint optimization of the spreading factor, transmission power, and three-dimensional (3D) placement of the UAVs. To manage the dynamic and partially observable nature of this system, we model the problem as a partially observable stochastic game (POSG) and address it using a multi-agent proximal policy optimization (MAPPO) framework. An ablation study shows that our proposed MAPPO Hetero-Net significantly outperforms traditional, isolated network designs, achieving energy efficiency improvements of 55.81\% and 198.49\% over isolated WSN-only and WUSN-only deployments, respectively.

Abdullahi Isa Ahmed, Yaya Etiabi, Ali Waqar Azim et al.

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments.

Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud

As next-generation Internet of Things (NG-IoT) networks continue to grow, the number of connected devices is rapidly increasing, along with their energy demands. This creates challenges for resource management and sustainability. Energy-efficient communication, particularly for power-limited IoT devices, is therefore a key research focus. In this paper, we deployed flying LoRa gateways mounted on unmanned aerial vehicles (UAVs) to collect data from LoRa end devices and transmit it to a central server. Our primary objective is to maximize the global system energy efficiency of wireless LoRa networks by joint optimization of transmission power, spreading factor, bandwidth, and user association. To solve this challenging problem, we model the problem as a partially observable Markov decision process (POMDP), where each flying LoRa GW acts as a learning agent using a cooperative multi-agent reinforcement learning (MARL). Simulation results demonstrate that our proposed method, based on the multi-agent proximal policy optimization algorithm, significantly improves the global system energy efficiency and surpasses the popular MARL and other conventional schemes.