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.
Publication Profile
🎓 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.
🏆 Awards and Honors
His exceptional work in computer engineering research earned him the Master of Science Thesis Award from the IEEE Iran Section in May 2019. This prestigious recognition underscores his contributions to the advancement of network communications and routing optimizations.
🔬 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.