Contact

Phone: 470-578-5613

Email: alee146@kennesaw.edu

Office Location: J 306

Office Hours:
office hours TBD

Research Labs

1) Intelligent Computing & Networking (ICN) Lab

Director: Dr. Ahyoung Lee

  • AI-Driven Autonomous Networked Systems: This research advances self-optimizing and self-healing communication networks using reinforcement learning, distributed intelligence, and adaptive control. It focuses on enabling scalable, resilient, and energy-aware LPWAN, 5G, and next-generation wireless systems operating in dynamic environments.
  • Intelligent Cyber-Physical and Health Systems: This work develops interpretable AI/ML frameworks for predictive modeling and decision support in complex health and cyber-physical systems. Emphasis is placed on uncertainty quantification and trustworthy AI to enhance system reliability and societal impact.
  • Sustainable and Energy-Efficient Communication Architectures: This research designs green communication systems through energy-efficient IoT hardware, adaptive protocols, and network optimization. Applications include smart buildings, industrial systems, and environmental monitoring networks.
  • Optimal Computing and Distributed Resource Management: This work develops scalable load-balancing and resource allocation algorithms for heterogeneous cloud-edge and emerging computing networks. By integrating optimization and AI-based control, it improves efficiency, latency, and system robustness.

 

2) L3BN: Low-Power Low-Cost Long-Range Broadband Networking Lab

Director and Principal Investigator: Dr. Ahyoung Lee

  • This project develops novel energy-efficient and QoS-aware algorithms for next-generation IoT cloud-edge networks by integrating LoRa-based LPWAN technologies with distributed edge computing. By advancing adaptive optimization and software-defined networking frameworks, the research addresses fundamental challenges in energy-constrained, long-range communication systems. The resulting architecture enables resilient, scalable, and rapidly deployable network infrastructures for dynamic and resource-limited environments.

3) Innovation Smart Water Safety in iSTEM Building

Director and Principal Investigator: Dr. Ahyoung Lee

  • This project develops and deploys a scalable, AI-enabled smart water safety platform that integrates real-time environmental sensing, predictive analytics, and data-driven decision support to enhance water quality management and public health outcomes. The system combines distributed IoT sensor networks, machine learning–based bacterial forecasting, and cloud-edge cyberinfrastructure to enable continuous monitoring and proactive risk mitigation in recreational and community water systems.
  • The interdisciplinary research team—spanning computer science, electrical engineering, organic chemistry, molecular biology, and geospatial science—collaborates with municipal agencies and community stakeholders to design, validate, and operationalize a next-generation water quality monitoring framework. The i-BacteriaForecast platform advances the scientific foundations of intelligent environmental infrastructure by addressing challenges in sensor integration, predictive modeling under uncertainty, and equitable data accessibility.
  • By coupling rigorous research with community-engaged implementation, the project establishes a replicable national model for Smart and Connected Communities, strengthening environmental resilience, safeguarding public health, and enabling evidence-based policy and economic decision-making.

 

 

 

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