Smart IoT • Wireless • AI | Department of IoT, SCH Media Labs

Smart IoT &
Hyperconnectivity Research Group

We are the Smart IoT & Hyperconnectivity Research Group at the Department of Internet of Things, SCH Media Labs, Soonchunhyang University (SIH@SCH). We study Smart IoT, hyperconnected networks, and hyperconnected intelligence to enable an autonomously networked society.

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Latency-aware
URLLC & real-time adaptation
Energy-efficient
Edge/IoT friendly AI
Throughput-driven
Smart scheduling & access
Core idea
AI that fits wireless constraints
Lightweight intelligence
Instead of heavy deep networks…
Our research focuses on designing efficient and distributed AI-driven methods for wireless communications and IoT systems, enabling adaptive optimization across networking, sensing, and physical-layer components under latency, energy, and scalability constraints.
Adaptive Decision Intelligence Distributed Intelligence Wireless Optimization Edge/IoT
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Overview

We are the Smart IoT & Hyperconnectivity Research Group at the Department of Internet of Things in SCH Media Labs, Soonchunhyang University (SIH@SCH). Our mission is to build an autonomously networked society.

Our interest is in Wireless Communications integrated with Artificial Intelligence, particularly for IoT systems and edge computing environments.

Wireless AI for Networks Smart IoT Edge
Motivation
Wireless performance in IoT/edge networks is sensitive to latency, energy, and throughput. Yet many deep learning models are too heavy for resource-limited devices.
Research goal
Build lightweight AI-driven optimization techniques that adapt wireless behavior without heavy computational overhead.
Why Adaptive Decision Intelligence?
Adaptive Decision Intelligence learns decision policies (power allocation, spectrum access, scheduling) without extremely deep architectures.
Why Distributed Intelligence?
Distributed Intelligence distributes intelligence across devices, reduces centralized compute, and can be designed to be communication-efficient over wireless links.
Core idea
Instead of deploying heavy deep neural networks in wireless systems, we develop efficient AI frameworks that optimize latency, energy, and throughput in dynamic wireless environments—making AI practical for real IoT and edge networks.

Research Topics

Lightweight AI-driven optimization for wireless systems, built for IoT and edge constraints.

Collaborate with us
Wireless Resource Optimization
Power control, scheduling, spectrum access, adaptive MAC/PHY decisions.
LatencyThroughputEnergy
Reinforcement Learning for Networks
Efficient RL policies for real-time decisions under dynamic channels.
Policy learningOnlineLow overhead
Federated Learning over Wireless
Communication-efficient FL, heterogeneity, wireless-aware aggregation.
Comm-efficientHeterogeneous IoTPrivacy
Lightweight AI for Edge Devices
Small models, low-memory learning, real-time inference on IoT nodes.
CompressionQuantizationEfficient
URLLC & Reliable Connectivity
Ultra-reliable low-latency design for smart factories & critical IoT.
ReliabilityDelay boundsQoS
Hyperconnected Intelligence
Distributed intelligence across devices for scalable, autonomous networks.
Edge collaborationAutonomyScalability

Selected Publications

Our Published Papers.

Papers
Low-latency edge-enabled digital twin system for multi-robot collision avoidance and remote control
2025/07/28
View
Edge-based semantic feature transmission reduces latency and bandwidth while maintaining collision safety.
MDPI Sensors
Data aggregation and packet bundling of uplink small packets for monitoring applications in LTE
2017/11/27
View
Aggregator-based uplink with packet bundling reduces signaling overhead and improves throughput while lowering outage and latency.
IEEE IEEE Network
Google Scholar / Publications list
View our research contributions in wireless communications, IoT, and distributed intelligence.
View all publications

News

Recent lab updates and milestones.

Updates
March 2026
Paper Selected as Editor’s Choice in Sensors Journal
Our paper titled "Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control" has been selected as an Editor’s Choice Article in the journal Sensors (MDPI), recognizing it as a paper of high interest and impact.
View Paper
March 2026
Maharani Hastuti Poul Mtowe completed his Master’s degree
Maharani Hastuti will continue to pursue his PhD in our laboratory. Congratulations!
February 2026
Minhyeok Park & YouChang completed their Master’s degree
We celebrate and wish them all the best in their future endeavors.
Sept 2019
National research project awarded
Intelligent IoT network design for ultra-reliable low-latency communications (3-year project).
Aug 2019
VR project supported by KOCCA
Collaboration on Virtual/Mixed Reality Education Contents with Romeo & Juliet.
Feb 2019
Paper accepted to IEEE Wireless Communications Letters
UAV-based two-way communication paper accepted. Congratulations!
Sept 2018
SIH@SCH started
Smart IoT & Hyperconnected Research Group launched on September 1, 2018.

People

PI, students, interns, and alumni.

Team
Dong-Min Kim
Prof. Dong-Min Kim
Principal Investigator
RLWirelessIoTAIFL
Daniel Poul Mtowe
Daniel Poul Mtowe
PhD Researcher
RL URLLC Edge
Issamu Fred Ngwalo
Issamu Fred Ngwalo
PhD Researcher
WirelessSchedulingAI
Maharani Hastuti
Maharani Hastuti
PhD Researcher
AIData Simulation
UG
Kwon Bumcheol
Undergraduate Intern
ExperimentData
UG
Kim Junhyung
Undergraduate Intern
ExperimentData
UG
Chae Gyeonghun
Undergraduate Intern
ExperimentData
UG
Sunatullah
Undergraduate Intern
ExperimentData
Join the lab
We welcome motivated students and collaborators.
Contact us

Contact

For collaboration, visiting, or student opportunities, reach us through email or phone.

Email
dmk@sch.ac.kr / don9min@gmail.com
Tel
+82 41 530 1535
Address
ML408, SCH Media Labs Building
22 Soonchunhyang-ro, Shinchang-myeon, Asan-si, Chungcheongnam-do
31538, Republic of Korea