Mohsen Guizani’s research while affiliated with Mohamed bin Zayed University of Artificial Intelligence and other places

This paper aims at analyzing and comparing an adaptive algorithm-based method for improving the performance of Internet of Things (IoT) systems through simulation studies. Concentrating on active and complex scenarios, the study presents new proposals for secure and smart learning of routes, activity forecasting for nodes, link stability estimation, and flexible resource management. These methods are benchmarked against conventional algorithms to evaluate the effectiveness of the proposed solution based on routing efficiency, traffic prediction, link, resource consumption, network response time, and energy requirements. The findings are encouraging, the adaptive algorithms do improve dramatically on the standard ones making the system slower and consuming much less power. From the findings of the study it can be concluded that using adaptive algorithms in IoT can have a high impact in terms of improvement in efficiency as well as sustainability. We conclude this work by providing some directions for further research and development in the IoT field.

This study presents a federated learning (FL) framework tailored for UAV-enabled IoT networks, addressing challenges in efficiency, robustness, and scalability. The proposed system improves model learning with a 14.9 percentage point increase in accuracy (75.5% to 90.4%) and a 69.2% reduction in loss over ten training epochs. It demonstrates resilience, limiting accuracy reduction to 7% under simulated attacks, and scalability with a linear increase in processing times as network size grows. High anomaly detection rates (92%) further enhance network security and reliability. These results validate the framework’s effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.

Emergency of DeepSeek R1 and QwQ 32B have broken through performance barriers for running frontier large language models (LLMs) on home devices. While consumer hardware is getting stronger and model quantization is improving, existing end-side solutions still demand GPU clusters, large RAM/VRAM, and high bandwidth, far beyond what a common home cluster can handle. This paper introduces prima.cpp, a distributed inference system that runs 70B-scale models on everyday home devices using a mix of CPU/GPU, low RAM/VRAM, Wi-Fi, and cross-platform support. It uses mmap to manage model weights and introduces piped-ring parallelism with prefetching to hide disk loading. By modeling heterogeneity in computation, communication, disk, memory (and its management behavior), and OS, it optimally assigns model layers to each device's CPU and GPU, further reducing token latency. An elegant algorithm named Halda is proposed to solve this NP-hard assignment problem. We evaluate prima.cpp on a common four-node home cluster. It outperforms llama.cpp, exo, and dllama on 30B+ models while keeping memory pressure below 6%. This brings frontier 30B-70B models, such as Llama 3, DeepSeek R1, Qwen 2.5, and QwQ to home assistants, making advanced AI truly accessible to individuals. The code is open source and available at https://github.com/Lizonghang/prima.cpp.

As a foundation of large language models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human–AI collaboration, surpassing earlier technological advancements. This paper provides a comprehensive overview of large language model (LLM) fine-tuning by integrating hermeneutic theories of human comprehension, with a focus on the essential cognitive conditions that underpin this process. Drawing on Gadamer’s concepts of Vorverständnis, Distanciation, and the Hermeneutic Circle, the paper explores how LLM fine-tuning evolves from initial learning to deeper comprehension, ultimately advancing toward self-awareness. It examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. The paper introduces a new term, “Tutorial Fine-Tuning (TFT)”, which annotates a process of intensive tuition given by a “tutor” to a small number of “students”, to define the latest round of LLM fine-tuning advancements. By addressing key challenges associated with fine-tuning, including ensuring adaptability, precision, credibility and reliability, this paper explores potential future directions for the co-evolution of humans and AI. By bridging theoretical perspectives with practical implications, this work provides valuable insights into the ongoing development of LLMs, emphasizing their potential to achieve higher levels of cognitive and operational intelligence.

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some evidence of higher accident rates in AVs, these incidents tend to result in less severe injuries compared to traditional vehicles due to cooperative safety measures. However, the increased complexity of CAV systems exposes them to significant security vulnerabilities, potentially compromising their performance and communication integrity. This paper contributes by presenting a detailed analysis of existing security frameworks and protocols, focusing on intra- and inter-vehicle communications. We systematically evaluate the effectiveness of these frameworks in addressing known vulnerabilities and propose a set of best practices for enhancing CAV communication security. The paper also provides a comprehensive taxonomy of attack vectors in CAV ecosystems and suggests future research directions for designing more robust security mechanisms. Our key contributions include the development of a new classification system for CAV security threats, the proposal of practical security protocols, and the introduction of use cases that demonstrate how these protocols can be integrated into real-world CAV applications. These insights are crucial for advancing secure CAV adoption and ensuring the safe integration of autonomous vehicles into intelligent transportation systems.

Symbiotic communication (SC) is known as a new wireless communication paradigm, similar to the natural ecosystem population, and can enable multiple communication systems to cooperate and mutualize through service exchange and resource sharing. As a result, SC is seen as an important potential technology for future sixth-generation (6G) communications, solving the problem of lack of spectrum resources and energy inefficiency. Symbiotic relationships among communication systems can complement radio resources in 6G. However, the absence of established trust relationships among diverse communication systems presents a formidable hurdle in ensuring efficient and trusted resource and service exchange within SC frameworks. To better realize trusted SC services in 6G, in this article, we propose a solution called a symbiotic blockchain network (SBN) that combines SC and blockchain. Specifically, we first use cognitive backscatter communication to transform blockchain consensus, that is, the symbiotic blockchain consensus (SBC) so that it can be better suited for the wireless network. Then, for SBC, we propose a highly energy-efficient sharding scheme to meet the extremely low power consumption requirements in 6G. Finally, such a blockchain scheme guarantees trusted transactions of communication services in SC. Through ablation experiments, our proposed SBN demonstrates significant efficacy in mitigating energy consumption and reducing processing latency in adversarial networks, which is expected to achieve a sustainable and trusted 6G wireless network.