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Open Access

ARTICLE

Edge-AI Enabled Multi-Agent Intelligence Framework for Evolutionary Optimization and Mobility Localization in 6G-IoT Smart Sensing Systems

Muhammad Hanzla1, Bayan Alabdullah2, Mohammad Shorfuzzaman3,*, Mohammed Alonazi4, Jasem Almotiri5, Ahmad Jalal1,6,*
1 Department of Computer Science, Air University, Islamabad, Pakistan
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 Department of Software Engineering, College of Engineering and Advanced Computing, Alfaisal University, Riyadh, Saudi Arabia
4 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
5 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
6 Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea
* Corresponding Author: Mohammad Shorfuzzaman. Email: email; Ahmad Jalal. Email: email
(This article belongs to the Special Issue: Edge AI and Intelligent Systems in IoT)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086599

Received 02 June 2026; Accepted 26 August 2026; Published online 17 September 2026

Abstract

The increasing adoption of wearable Internet of Things (IoT) devices and wireless sensor networks has accelerated the demand for intelligent, low-latency human activity recognition and localization systems. However, dynamic environments, sensor noise, motion variability, and communication delays continue to hinder reliable real-time sensing. To address these challenges, this study presents an Edge-AI enabled multi-agent intelligence framework for human activity recognition and mobility localization within a 6G-enabled IoT edge-cloud continuum. The proposed framework employs Self-supervised Transformer-based Adaptive Sensor Encoding and learning-aware temporal windowing to generate robust representations from heterogeneous sensing modalities. Extracted locomotion and localization features are optimized using the Aquila Optimizer (AO) and classified through a deep neuro-fuzzy inference model. Furthermore, an edge-assisted monitoring platform, EvolveSense, is developed to support real-time visualization and low-latency intelligent analytics. Experimental evaluation on the ExtraSensory and Opportunity datasets achieved activity recognition accuracies of 93.63% and 95.57%, respectively, while localization accuracy reached 91.33% and 92.40%. The results demonstrate the effectiveness of Edge AI, intelligent sensing, and optimized multi-modal learning for next-generation IoT healthcare and smart environment applications.

Keywords

Multi-agent intelligence; Internet of Things (IoT); human activity recognition; mobility localization; edge computing; wearable sensors; smart healthcare; edge AI

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