Dr. ASM Bakibillah| Intelligent Transportation Systems | Best Researcher Award

Assistant Professor, Institute of Science Tokyo, Japan

Dr. A. S. M. Bakibillah is a distinguished researcher in Mechatronics Engineering, specializing in Intelligent Transportation Systems (ITS). With a strong academic foundation and a passion for sustainable mobility solutions, he has contributed extensively to eco-driving strategies, intelligent vehicle control, and cyber-physical frameworks for connected and automated vehicles. His research integrates machine learning, optimization, and control strategies to enhance energy efficiency and safety in transportation.

Publication Profile

🎓 Education

Dr. Bakibillah earned his Ph.D. in Mechatronics Engineering from Monash University, Australia, in collaboration with Tokyo Institute of Technology, Japan, focusing on intelligent vehicle control strategies for cooperative eco-driving 🚗🌿. He completed his M.Sc. in Information Technology (INFOTECH) from the University of Stuttgart, Germany, specializing in Micro and Optoelectronics, where he worked on silicon tunnel field-effect transistors 🏭🔬. His academic journey began with a B.Sc. in Electrical and Electronic Engineering from Rajshahi University of Engineering and Technology (RUET), Bangladesh, where he specialized in control systems and designed a temperature sensor-based speed controller for induction motors ⚡🛠️.

💼 Experience

Dr. Bakibillah has a dynamic research background in intelligent transportation and control systems, with extensive experience in academia and industry. His work has focused on eco-driving optimization, vehicle trajectory planning, smart parking solutions, and cyber-physical systems for transportation 🚦🔍. He has collaborated with top institutions in Australia, Japan, and Germany, contributing to cutting-edge innovations in automated driving and vehicle intelligence.

🏆 Awards and Honors

Dr. Bakibillah has received numerous prestigious awards, including the Monash International Postgraduate Research Scholarship (MIPRS) 🏅, the Monash Merit Certificate for Engineering Entrepreneurship 🏆, and the Monash School of Engineering Publication Award (SEPA) 🎖️. He was also honored with the Society of Instrument and Control Engineers (SICE) Student Travel Grant and International Award ✈️📜.

🔬 Research Focus

His research primarily revolves around sustainable transportation and intelligent mobility solutions 🚘💡. He specializes in energy-efficient eco-driving, cooperative automated vehicle control, and cyber-physical frameworks for traffic optimization. His work bridges the gap between artificial intelligence, control systems, and sustainable mobility, significantly impacting the future of intelligent transportation.

🔚 Conclusion

Dr. A. S. M. Bakibillah is a dedicated researcher in ITS, advancing the frontiers of smart mobility and eco-friendly driving solutions 🌍🚦. His innovative research, academic excellence, and multiple high-impact publications make him a valuable contributor to the field of intelligent vehicle technologies.

📚 Publications

Advancements and Challenges of Visible Light Communication in Intelligent Transportation Systems: A Comprehensive Review – Photonics, 2025, Cited by 📑

Cooperative Look-ahead Lane Change System for Improving Driving Intelligence of Automated Vehicles in Critical Scenarios – IEEE Transactions on Intelligent Vehicles, 2024, Cited by 📑

Electric Vehicle Eco-Driving Strategy at Signalized Intersections Based on Optimal Energy Consumption – Journal of Environmental Management, 2024, Cited by 📑

Optimal Eco-Driving Scheme for Reducing Energy Consumption and Carbon Emissions on Curved Roads – Heliyon, 2024, Cited by 📑

Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach – Actuators, 2024, Cited by 📑

Eco-Friendly Smart Car Parking Management System with Enhanced Sustainability – Sustainability, 2024, Cited by 📑

Robust Vehicle Mass Estimation Using Recursive Least M-Squares Algorithm for Intelligent Vehicles – IEEE Transactions on Intelligent Vehicles, 2023, Cited by 📑

A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways – Sensors, 2023, Cited by 📑

Robust Estimation of Traffic Density with Missing Data using an Adaptive-R Extended Kalman Filter – Applied Mathematics and Computation, 2022, Cited by 📑

Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme – Sustainability, 2022, Cited by 📑

Mr. Mohammad Naderi | Vehicular Communications | Smart Cities Technologies Award

Part-time lectrutre, self-employed, Iran

Mohammad Naderi is a dedicated computer engineer and researcher from Iran, specializing in wireless communications and networking. With a strong academic background and practical expertise, he has contributed significantly to the fields of the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs), Mobile Ad Hoc Networks (MANETs), and Software-Defined Networking (SDN). His innovative approaches to opportunistic routing and traffic-aware networking solutions have led to impactful publications in high-ranking journals. Alongside his research, he has mentored master’s and Ph.D. students, providing guidance in simulation and network-related studies.

🎓 Education

Mohammad Naderi pursued his Bachelor of Science (BSc) in Computer Engineering at Hamedan University of Technology, Iran, graduating in 2013. He continued his academic journey with a Master of Science (MSc) in Computer Engineering from Azad University, Science and Research Branch, Tehran, where he achieved an outstanding GPA of 3.44. His academic excellence placed him among the top 15 national rank holders, reflecting his strong grasp of computational and networking concepts.

💼 Experience

With a passion for both academia and practical research, Mohammad Naderi has served as an advisor and lecturer, guiding master’s and Ph.D. students in IoT, VANETs, MANETs, UAV Communications, and SDN Security. Since 2016, he has been actively involved in mentoring students, helping them develop innovative research ideas and simulation models. Additionally, he has worked as a part-time lecturer at Danesh Institute, specializing in NS-2 simulation tools. In 2023, he took on a lecturing role at Islamic Azad University, Pardis Branch, where he taught computer networks and network lab courses, strengthening his expertise in teaching and research.

🔬 Research Focus

Mohammad Naderi’s research primarily revolves around opportunistic routing, VANETs, MANETs, IoT, UAV communications, and software-defined vehicular networks. His work integrates artificial intelligence techniques, including reinforcement learning and fuzzy logic, to optimize vehicular communication protocols. He has also explored hierarchical Q-learning-enabled neutrosophic AHP schemes, adaptive beaconing strategies, and routing efficiency in wireless networks, paving the way for more intelligent and reliable vehicular networking solutions.

🔚 Conclusion

Mohammad Naderi’s expertise in wireless networks, VANETs, SDN, and AI-driven communication systems has positioned him as a leading researcher in the field. His contributions to opportunistic routing and adaptive vehicular networking strategies are highly regarded, making a significant impact on next-generation communication technologies. With a strong commitment to both academic and practical advancements, he continues to push the boundaries of intelligent networking solutions. 🚀

📚 Publications

A 3-Parameter Routing Cost Function for Improving Opportunistic Routing Performance in VANETs – Published in Wireless Personal Communications (2017), this paper explores routing optimizations in VANETs to enhance communication reliability. 🔗 Read more.

Adaptive beacon broadcast in opportunistic routing for VANETs – Featured in Ad Hoc Networks (2019), this study introduces adaptive beaconing techniques to optimize data forwarding efficiency in vehicular environments. 🔗 Read more.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs – Published in Ad Hoc Networks (2023), this research enhances routing protocols using adaptive prioritization mechanisms. 🔗 Read more.

Hierarchical traffic light-aware routing via fuzzy reinforcement learning in software-defined vehicular networks – This 2023 Peer-to-Peer Networking and Applications publication introduces an AI-driven hierarchical traffic-aware routing strategy. 🔗 Read more.

Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks – A Computer Networks (2023) publication that leverages Q-learning and neutrosophic AHP techniques to improve vehicular communication. 🔗 Read more.