arXiv subjects

Abdullahi Isa Ahmed

Publications and source records attributed to Abdullahi Isa Ahmed.

5 recordsLinked to original sources

arXiv2026

GLo-MAPPO: Multi-Agent Deep Reinforcement Learning for Energy-Efficient UAV-Assisted LoRa Networks

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.

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arXiv2026

Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach

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, creating 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 study Long Range (LoRa) networks supported by multiple unmanned aerial vehicles (UAVs) in an uplink data collection scenario. Our objective is to maximize system energy efficiency by jointly optimizing transmission power, spreading factor, bandwidth, and user association. To address this challenging problem, we first model it as a partially observable stochastic game (POSG) to account for dynamic channel conditions, end device mobility, and partial observability at each UAV. We then propose a two-stage solution: a channel-aware matching algorithm for end device-UAV association and a cooperative multi-agent reinforcement learning (MARL) based multi-agent proximal policy optimization (MAPPO) framework for resource allocation under centralized training with decentralized execution (CTDE). Simulation results show that our proposed approach significantly outperforms conventional off-policy and on-policy MARL algorithms.

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arXiv2026

Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimization

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.

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arXiv2024

A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments

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.

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arXiv2023

Implementation of a Sustainable Security Architecture using Radio Frequency Identification (RFID) Technology for Access Control

Implementation of a sustainable security architecture has been quite a challenging task with several technology deployed to achieve the feat. Automatic IDentification (Auto-ID) procedures exist to provide information about people, animals, goods and products in transit and found several applications in purchasing and distribution logistics, industries, manufacturing companies and material flow systems. This work focuses on the development and implementation of an access control system using Radio Frequency Identification (RFID) technology to enhance a sustainable security architecture. The system controls access into a restricted area by granting access only to authorized persons, which incorporates the RFID hardware (RFID tags and readers and their antennas) and the software. The antenna are to be configured for a read range of about 1.5 m and TMBE kit reader module was used to test the RFID tags. The encoding and decoding process for the reading and writing to the tag as well as interfacing of the hardware and software was achieved through the use of a FissaiD RFID Reader Writer. The software that controls the whole system was designed using in Java Language. The database required for saving the necessary information, staff/guest was designed using appropriate DataBase Management System (DBMS). The system designed and implemented provide records of all accesses (check-in and check-out) made into the restricted area with time records. Other than this system, Model based modeling through the MATLAB/Simulink, Arduino platform, etc. can be used for similar implementation.

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