Emerging Trends in Distributed AI for Smart Environments

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1172

Special Issue Editor


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Guest Editor
Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Meknes 50050, Morocco
Interests: AI; software engineering; computer communications (networks); big data analysis; data mining
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements in distributed artificial intelligence (AI) and its transformative impact on smart environments. As AI continues to expand into distributed and decentralized systems, innovative solutions for managing data, optimizing resources, and enhancing decision-making processes are shaping the future of smart environments. This includes the development of intelligent infrastructures in smart cities, IoT networks, autonomous systems, and cloud–edge computing frameworks.

Researchers are encouraged to submit cutting-edge work on topics including real-time data processing, privacy-preserving AI techniques, federated learning for distributed systems, and AI-driven optimization in smart environments. The aim is to highlight methodologies that push the boundaries of scalability, reliability, and performance while addressing key challenges such as security, privacy, and energy efficiency.

The Special Issue seeks to bring together original research and review papers that explore both theoretical developments and practical applications in smart environments enhanced by distributed AI, providing a comprehensive overview of emerging trends and future directions.

Topics of interest include, but are not limited to, the following:

  • Distributed AI frameworks for IoT and smart devices;
  • AI-driven smart city infrastructures;
  • Privacy and security in distributed AI systems;
  • Edge computing and decentralized learning;
  • AI for autonomous systems and real-time decision making;
  • AI-enabled optimization in smart homes and industries;
  • Integration of AI and cloud–edge computing;
  • Applications of federated learning in smart environments.

Dr. Yousef Farhaoui
Guest Editor

Manuscript Submission Information

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Keywords

  • distributed artificial intelligence (AI)
  • smart environments
  • edge computing
  • Internet of Things (IoT)
  • machine learning (ML)
  • deep learning
  • federated learning
  • autonomous systems
  • AI-driven optimization
  • smart cities
  • real-time data processing
  • privacy-preserving AI
  • AI for IoT devices
  • cloud computing
  • AI in smart infrastructure

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Published Papers (2 papers)

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Research

13 pages, 1369 KiB  
Article
Algorithm-Based Real-Time Analysis of Training Phases in Competitive Canoeing: An Automated Approach for Performance Monitoring
by Sergio Amat, Sonia Busquier, Carlos D. Gómez-Carmona, Manuel Gómez-López and José Pino-Ortega
Algorithms 2025, 18(5), 242; https://doi.org/10.3390/a18050242 - 24 Apr 2025
Viewed by 142
Abstract
The increasing demands in high-performance sports have led to the integration of technological solutions for training optimization. This study aimed to develop and validate an algorithm-based system for analyzing three critical phases in canoe training: initial acceleration, steady-state cruising, and final sprint. Using [...] Read more.
The increasing demands in high-performance sports have led to the integration of technological solutions for training optimization. This study aimed to develop and validate an algorithm-based system for analyzing three critical phases in canoe training: initial acceleration, steady-state cruising, and final sprint. Using inertial measurement units (WIMU PRO™) sampling at 10 Hz, we collected performance data from 12 young canoeists at the Mar Menor High-Performance Sports Center. The custom-developed algorithm processed velocity–time data through polynomial fitting and phase detection methods. Results showed distinctive patterns in the acceleration phase, with initial rapid acceleration (5 s to stabilization) deteriorating in subsequent trials (9–10 s). Athletes maintained consistent stabilized speeds (14.62–14.98 km/h) but required increasing space for stabilization (13.49 to 31.70 m), with slope values decreasing from 2.58% to 0.74% across trials. Performance deterioration was evident through decreasing maximum speeds (18.58 to 17.30 km/h) and minimum speeds (11.17 to 10.17 km/h) across series. The algorithm successfully identified phase transitions and provided real-time feedback on key performance indicators. This technological approach enables automated detection of training phases and provides quantitative metrics for technique assessment, offering coaches and athletes an objective tool for performance optimization in canoeing. Our aim is to automate the analysis task that is currently performed manually by providing an algorithm that the coaches can understand, using very basic mathematical tools, and that saves time for them. Full article
(This article belongs to the Special Issue Emerging Trends in Distributed AI for Smart Environments)
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15 pages, 2209 KiB  
Article
Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
by Zakaria Elbayed and Abdelmjid Qadi EI Idrissi
Algorithms 2025, 18(3), 161; https://doi.org/10.3390/a18030161 - 11 Mar 2025
Viewed by 637
Abstract
This paper explores the application of deep neural networks (DNNs) as an alternative to the traditional Black–Scholes model for predicting European put option prices. Using synthetic datasets generated under the Black–Scholes framework, the proposed DNN achieved strong predictive performance, with a Mean Squared [...] Read more.
This paper explores the application of deep neural networks (DNNs) as an alternative to the traditional Black–Scholes model for predicting European put option prices. Using synthetic datasets generated under the Black–Scholes framework, the proposed DNN achieved strong predictive performance, with a Mean Squared Error (MSE) of 0.0021 and a coefficient of determination (R2) of 0.9533. This study highlights the scalability and adaptability of DNNs to complex financial systems, offering potential applications in real-time risk management and the pricing of exotic derivatives. While synthetic datasets provide a controlled environment, this study acknowledges the challenges of extending the model to real-world financial data, paving the way for future research to address these limitations. Full article
(This article belongs to the Special Issue Emerging Trends in Distributed AI for Smart Environments)
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