Taha Mahmood, Mudassar Shakeel, Ahmed Naeem, El Mehdi Amhoud, Rizwan Ahmad, Salman Ghafoor
Hierarchical federated learning (HFL) enables communication-efficient training in unmanned aerial vehicle (UAV) swarms, but performance degrades under changing and heterogeneous environments. This paper proposes domain-aware HFL (DA-HFL), integrating mobility-informed clustering, cluster-level aggregation, cloud-based Fisher-weighted merging, a domain-adversarial shared backbone, environment-specific heads, and gated inference with local adaptation. A mission-weighted optimization formulation incorporates energy, latency, and cluster-head eligibility constraints. Theoretical analysis establishes resource feasibility, a conditional backbone-stationarity bound, the quadratic optimality of Fisher-weighted merging, and communication and computation complexity bounds. Simulations across four heterogeneous environments demonstrate faster convergence, improved accuracy during environmental transitions, and more stable, purer clusters than the evaluated baselines, with modest energy overhead over resource-aware HFL. Ablation studies validate the contributions of the proposed components.
Yaya Etiabi, Wessam Ajib, El Mehdi Amhoud, Halima Elbiaze
The evolution from 5G to 6G networks marks a transition toward the "intelligence of everything." While 5G enables enhanced connectivity, 6G aspires to embed intelligence directly into communication infrastructures, creating systems that are adaptive, context aware, and capable of autonomous decision making. Realizing such systems depends on harmonizing three fundamental functions that compete for shared network resources and often pursue conflicting goals: (i) sensing for environmental and contextual information acquisition, (ii) communication for reliable and timely information exchange, and (iii) computation for information processing and decision making. This harmonization necessitates shifting from independent optimization to their joint design as a unified system, a paradigm known as Integrated Sensing, Communication, and Computation (ISCC). In parallel, AI has emerged both as an enabling tool for ISCC optimization and as a workload whose requirements influence ISCC system design. This survey comprehensively examines ISCC from this dual perspective by distinguishing between AI-aided ISCC, in which learning-based methods support the joint design and optimization of sensing, communication, and computation, and AI-oriented ISCC, in which these functions are jointly configured to support learning, inference, and distributed intelligence. The survey further examines the co-design of AI and ISCC, where AI requirements and ISCC constraints jointly shape system and model decisions. It further provides a comparative synthesis of the resulting design approaches, system architectures, performance metrics, and evaluation practices, relates these developments to application-specific requirements across different domains, and identifies open research problems toward scalable, adaptive, and trustworthy AI-ISCC systems.
Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud
The rapid advancement of Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa) technology, has positioned them as a cornerstone of Next-Generation Internet of Things (NG-IoT) applications. Despite their long-range and low-power advantages, achieving high energy efficiency (EE) 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 (NLoS) propagation, while satellite-based alternatives incur excessive energy consumption and prohibitive latency. To address these limitations, we propose a multi-UAV architecture in which unmanned aerial vehicles (UAVs) serve as mobile LoRa gateways to dynamically collect data from LoRa end devices (EDs). We formulate a joint optimization problem to maximize the weighted system EE 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). We adopt PPO because its clipped surrogate objective discourages excessively large policy updates, thereby promoting stable learning as multiple UAV agents simultaneously adapt their behaviors. Combined with centralized training and decentralized execution (CTDE), this enables coordinated policy learning while allowing each UAV to make decisions independently during execution. Simulation results show that GLo-MAPPO consistently outperforms state-of-the-art multi-agent reinforcement learning (MARL) benchmarks in terms of EE and power consumption across varying network densities. We further validate the trained policy through closed-loop, packet-level co-simulation using the OMNeT++/FLoRa LoRa simulator. Finally, ....