Survey of Dynamic Super-Frame Adjustment Schemes in Beacon-Enabled IEEE 802.15.4 Networks: An Application's Perspective. - Arshad Farhad; Yousaf Zia; Faisal Bashir Hussain (Wirel. Pers. Commun., 2016)

Interference-Aware Spreading Factor Assignment Scheme for the Massive LoRaWAN Network. - Arshad Farhad; Dae-Ho Kim; Pranesh Sthapit; Jae-Young Pyun (ICEIC, 2019)

Scalability of LoRaWAN in an Urban Environment: A Simulation Study. - Arshad Farhad; Dae-Ho Kim; Jae-Young Pyun (ICUFN, 2019)

Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks. - Arshad Farhad; Dae-Ho Kim; BeomHun Kim; Alaelddin Fuad Yousif Mohammed; Jae-Young Pyun (IEEE Access, 2020)

Content-based dynamic superframe adaptation for Internet of Medical Things. - Yousaf Zia; Arshad Farhad; Faisal Bashir; Kashif Naseer Qureshi; Ghufran Ahmed (Int. J. Distributed Sens. Networks, 2020)

Resource Allocation to Massive Internet of Things in LoRaWANs. - Arshad Farhad; Dae-Ho Kim; Jae-Young Pyun (Sensors, 2020)

Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices. - Arshad Farhad; Dae-Ho Kim; Santosh Subedi; Jae-Young Pyun (Sensors, 2020)

Federated learning for AI to improve patient care using wearable and IoMT sensors. - Arshad Farhad; Sandra I. Woolley; Peter Andras 001 (ICHI, 2021)

Feasibility Study of the LoRaWAN blind Adaptive Data Rate. - Arshad Farhad; Dae-Ho Kim; Jeong-Sun Yoon; Jae-Young Pyun (ICUFN, 2021)

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. - Arshad Farhad; Sandra I. Woolley; Peter Andras 001 (MIE, 2021)

Deep Learning-Based Channel Adaptive Resource Allocation in LoRaWAN. - Arshad Farhad; Dae-Ho Kim; Jeong-Sun Yoon; Jae-Young Pyun (ICEIC, 2022)

HADR: A Hybrid Adaptive Data Rate in LoRaWAN for Internet of Things. - Arshad Farhad; Jae-Young Pyun (ICT Express, 2022)

ND-ADR: Nondestructive adaptive data rate for LoRaWAN Internet of Things. - Muhammad Ali Lodhi; Lei Wang 005; Arshad Farhad (Int. J. Commun. Syst., 2022)

R-ARM: Retransmission-Assisted Resource Management in LoRaWAN for the Internet of Things. - Arshad Farhad; Dae-Ho Kim; Jae-Young Pyun (IEEE Internet Things J., 2022)

Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges. - Arshad Farhad; Jae-Young Pyun (Sensors, 2022)

Mobility Adaptive Data Rate Based on Kalman Filter for LoRa-Empowered IoT Applications. - Arshad Farhad; Goo-Rak Kwon; Jae-Young Pyun (CCNC, 2023)

Terahertz Meets AI: The State of the Art. - Arshad Farhad; Jae-Young Pyun (Sensors, 2023)

UWB Positioning System Based on LSTM Classification With Mitigated NLOS Effects. - Dae-Ho Kim; Arshad Farhad; Jae-Young Pyun (IEEE Internet Things J., 2023)

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning. - Arshad Farhad; Jae-Young Pyun (Sensors, 2023)

AI-ERA: Artificial Intelligence-Empowered Resource Allocation for LoRa-Enabled IoT Applications. - Arshad Farhad; Jae-Young Pyun (IEEE Trans. Ind. Informatics, 2023)

A Contextual Aware Enhanced LoRaWAN Adaptive Data Rate for mobile IoT applications. - Muhammad Ali Lodhi; Lei Wang 005; Arshad Farhad; Khalid Ibrahim Qureshi; Jenhui Chen; Khalid Mahmood 002; Ashok Kumar Das (Comput. Commun., 2025)

Enhanced adaptive data rate strategies for energy-efficient Internet of Things communication in LoRaWAN. - Muhammad Ali Lodhi; Lei Wang 005; Khalid Mahmood 002; Arshad Farhad; Jenhui Chen; Saru Kumari (Int. J. Commun. Syst., 2025)