Alaa Abdellatif

Abstract
This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including Radio Frequency (RF) communication, Optical Wireless Communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy dynamically reconfigures RIoT nodes by adapting transmission rates, wake/sleep scheduling, and access technology selection. Results demonstrate that the proposed framework, combining digital twin technology, hybrid optical-radio integration, and data-driven energy modeling, substantially enhances the performance, resilience, and sustainability of 6G IoT networks.

Abstract
This paper presents an optimized framework for Post-Disaster Search and Rescue (PDSR) that leverages multiple Unmanned Aerial Vehicles (UAVs) equipped with integrated radar and communication capabilities to simultaneously address sensing and connectivity requirements. The proposed solution includes a scalable system architecture and an optimization strategy that enable the rapid deployment of UAV swarms with diverse sensing, communication, and edge-enabled coordination features, ensuring enhanced performance in real-world disaster environments. The proposed approach formulates and solves a 3D UAV positioning and power allocation problem to maximize sensing performance and communication efficiency over multiple targets in designated zones. Due to the NP-hard and combinatorial nature of the problem, we propose a Distributed Joint Radar-Communication (DJRC) solution. This solution employs an efficient reward for potential actions and consistently selects the best action that maximizes the reward while ensuring both communications and sensing performance. Simulation results demonstrate significant performance improvements of the proposed solution over state-of-the-art radar- or communication-centric methods, with polynomial complexity dependent on the number of UAVs and linear dependence on the iteration count.

Abstract
Unmanned Aerial Vehicles (UAVs) are a promising solution for next-generation wireless networks due to their mobility, rapid deployment, and ability to provide Line-of-Sight (LoS) connectivity. However, deploying multiple UAVs in realt-ime to meet dynamic, non-uniform traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose the Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) algorithm, a scalable algorithm that jointly minimizes UAV count and optimizes 3D positioning based on real-time user distribution and traffic demand. In contrast to prior works that assume static user patterns or fixed UAV counts, EMTAD dynamically adapts UAV deployment to maximize spectral efficiency and satisfy user-specific Quality of Service (QoS) requirements. Simulation results demonstrate that EMTAD reduces the number of UAVs required and achieves superior aggregate throughput compared to baseline approaches.

Abstract

Abstract