@Article{cmc.2026.086599,
AUTHOR = {Muhammad Hanzla, Bayan Alabdullah, Mohammad Shorfuzzaman, Mohammed Alonazi, Jasem Almotiri, Ahmad Jalal},
TITLE = {Edge-AI Enabled Multi-Agent Intelligence Framework for Evolutionary Optimization and Mobility Localization in 6G-IoT Smart Sensing Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28329},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.086599}
}