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
Supervised Machine Learning (ML) models require large amounts of labeled data for training. However, this becomes challenging when dealing with resource- and network-constrained Internet of Things (IoT) devices that collect data. Furthermore, in scenarios where the acquired data is fast-changing and highly temporal, continuous and online learning becomes necessary. In this paper, we address the problem of efficiently training ML models using data from IoT nodes. We specifically focus on two aspects: i) selecting the nodes that provide data for the re/training, and ii) determining the optimal amounts of data to be acquired from these nodes, considering network and time constraints, while minimizing learning errors. To tackle this optimization problem, we propose ONDS: an Optimum Node and Data Selection algorithm with linear complexity in the worst-case. ONDS offers a model-agnostic solution applicable to different data modalities and ML architectures. To evaluate the performance of ONDS, we conduct experiments using various models and real-world datasets. The results demonstrate the effectiveness of ONDS, as it outperforms existing alternatives in both classification and regression tasks.

Abstract
This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain's ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework's ability to adjust to changing conditions, supporting multiple learning paradigms. Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times, aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance, latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.