Hassania School of Public Works

In this paper, we introduce a Fisher–Yates shuffle for the development of the Merkle-Damgård construction while not using any predefined functions or hashing library. Since SHA-1 has been deprecated, we focus on the Secure Hash Algorithm 2 (SHA-2), which remains secure against all known full-round collision attacks. In this work, we introduce and study Fisher--Yates–driven dynamic permutations within this family to enhance resistance against automated cryptanalysis, particularly SAT-based attacks, while preserving SHA-2’s robust design. Finally, we provide a practical explanation of how the use of our approach could be beneficial for the Proof-of-Work (PoW) in blockchain.

This paper presents a scalable framework for virtual soil moisture sensing based on time series data collected from environmental sensors installed at the Rabat site. The proposed architecture leverages a Hadoop-based data pipeline that employs MapReduce for the distributed preprocessing of high-volume sensor data. We investigate the predictive performance of two models—Long Short-Term Memory (LSTM) and Random Forest (RF)—at different soil depths. Experimental results show that the LSTM model efficiently captures temporal patterns in the data, while the RF model demonstrates strong performance with reduced computational cost. These results confirm the effectiveness of combining deep learning and ensemble learning approaches for accurate and scalable soil moisture forecasting in smart farming applications.

Exponential growth in data center energy consumption, driven by artificial intelligence workloads and expanding digital infrastructure, necessitates innovative physical infrastructure management approaches. This systematic review analyzes 30 research papers (2015-2025) across seven interconnected domains in intelligent data center monitoring, revealing clear evolution from basic IoT deployments toward autonomous, AI-driven control systems. Integrated approaches combining IoT sensors, machine learning algorithms, and predictive analytics achieve substantial energy reductions while maintaining operational reliability. Critical gaps persist in holistic system integration, renewable energy intermittency management, and monitoring framework standardization. Emerging trends emphasize Physical AIdriven autonomous operations, comprehensive cooling-computing coordination, and carbon-neutral infrastructure design. This analysis synthesizes current capabilities and provides a roadmap for future research in intelligent data center infrastructure monitoring.

Following natural disasters, the rapid inspection of critical infrastructure is essential for ensuring public safety and guiding emergency response. To evaluate damage and functionality, it is important to quickly assess key sites, such as hospitals, power plants, bridges, and emergency shelters. In this study, we propose an energyconstrained trajectory planning framework for a swarm of Unmanned Aerial Vehicles (UAVs) tasked with inspecting predefined infrastructure locations across a disaster-affected area. The approach begins by partitioning the affected surface using a Voronoi diagram and assigning each UAV a specific region to operate within. Within each region, the trajectory optimization problem is formulated as a mixedinteger linear programming problem (MILP) with an energy-based objective function. A genetic algorithm (GA) is applied to efficiently solve the optimization problem, and its performance is compared to the Nearest Neighbor Algorithm (NNA). The experimental results demonstrate that the GA achieves better energy consumption, whereas the NNA provides faster execution times.

The rapid advancement of IoT and edge computing technologies has opened new horizons for creating assistive solutions tailored to individuals with sensory impairments, particularly those with hearing and vision disabilities. This article presents a novel edge-IoT-based framework that integrates multimodal large language models (MLLMs), multi-object tracking, and scene understanding to develop real-time, responsive assistance for impaired individuals. Our proposed system is designed to enhance accessibility and quality of life by providing educational tools and entertainment options that cater specifically to the needs of this community. The proposed solution leverages the computational power of edge devices to process data locally, ensuring low latency and high responsiveness, which are critical for real-time applications. Furthermore, we explore the potential of generative AI models in improving autonomy, with a particular focus on real-time transcription services for the hearing impaired and scene description services for the visually impaired. This work demonstrates the feasibility and effectiveness of using edge-IoT technologies combined with advanced AI techniques to create inclusive environments that empower disabled individuals, ensuring that technological advancements are leveraged to foster accessibility and independence.

This paper explores the growing global awareness of the urgency of tackling climate change and address the rising costs of conventional energy sources. These challenges have motivated many nations to adopt renewable energy power generation strategies. Solar and wind energy are prominent due to their notable environmental benefits. When integrated with storage units in hybrid systems, these renewable sources enable the production of more reliable and cost-effective electricity. This paper presents a detailed review of recent advances in optimizing energy systems that combine multiple renewable sources, with a focus on leveraging advanced artificial intelligence (AI) techniques. It critically examines the various strengths and weaknesses of different approaches to AI and demonstrates how integrating multiple AI methods can deliver superior optimization results.