Abdallah Jarwan made a remarkable accomplishment when he defended his Ph.D. thesis as the thesis was nominated for the Gold Medal. Abdallah was one of the distinctive members of our Carleton Sensor Systems and Internet of Things Lab and made great achievements during his period with the lab.
Abdallah has defended his Ph.D. thesis (titled by – Data Management for Enhanced Resource Utilization in Internet of Things Systems) in December 2021 and will be graduating in March 2022. Abdallah has worked on multiple research topics during his period as a Ph.D. student at Carleton University. His research includes the development of data and information management for enhancing the utilization of resources in IoT systems, information-based data routing and reduction, and deep reinforcement learning solutions for IoT network management. He also participated in building a complete IoT testbed solution, where he implemented part of his research algorithms and techniques. His research also includes developing simulation environments for wireless networks and more specifically LTE-based public safety networks. Abdallah is currently working as a Machine Learning Engineer at Lytica Inc., Ottawa.
He has prepared the following list of publications (listed in chronological order):
[PL1] A. Sabbah, A. Jarwan, O. Issa and M. Ibnkahla, ”Enabling LTE emulation by integrating CORE emulator and LTE-EPC network (LENA) simulator,” 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, Quebec, Canada, October 2017, pp. 1-6.
[PL2] I. Al-Shiab, A. Sabbah, A. Jarwan, O. Issa and M. Ibnkahla, ”Simulating large-scale networks for public safety: Parallel and distributed solutions in NS-3,” 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, Quebec, Canada, October 2017, pp. 1-7.
[PL3] A. Sabbah, A. Jarwan, I. Al-Shiab, M. Ibnkahla and M. Wang, ”Emulation of Large-Scale LTE Networks in NS-3 and CORE: A Distributed Approach,” 2018 IEEE Military Communications Conference (MILCOM), Los Angeles, California, USA, October 2018, pp. 1-6.
[PL4] A. Jarwan, A. Sabbah, and M. Ibnkahla, ”Machine Learning in Wireless Sensor Networks for the Internet of Things”, Encyclopedia of Wireless Networks, X. S. Shen, X. Lin, and K. Zhang, Cham, Switzerland: Springer International Publishing, 2018, pp. 1–7.
[PL5] A. Jarwan, A. Sabbah, M. Ibnkahla and O. Issa, ”LTE-Based Public Safety Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1165-1187, 2019.
[PL6] A. Jarwan, A. Sabbah and M. Ibnkahla, ”Data Transmission Reduction Schemes in WSNs for Efficient IoT Systems,” IEEE Journal on Selected Areas in Communications,
vol. 37, no. 6, pp. 1307-1324, 2019.
[PL7] A. Sabbah, A. Jarwan, L. Bonin and M. Ibnkahla, ”A High-Level Parameter Selection Framework for Irregular LTE-Based Mission Critical Networks,” 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, April 2019, pp. 1-6.
[PL8] Z. Bouida, I AlShiab, Y. Rafique, A. Jarwan, M. Moorjmalani, S. K. Kuri, and M. Ibnkahla, ”Carleton-Cisco IoT Testbed: Architecture, Features, and Applications,” 2021 IEEE Globecom Workshops (GC Wkshps), Madrid, Spain, December 2021.
[PL9] A. Jarwan, A. Sabbah and M. Ibnkahla, ”Information-Oriented Tra
c Management for Energy-Efficient and Loss-Resilient IoT Systems,” IEEE Internet of Things Journal, accepted for publication (early access), December 2021.
[PL10] A. Jarwan and M. Ibnkahla, ”Federated Deep Reinforcement Learning for Feedback-Oriented Backhaul Selection,” IEEE Internet of Things Journal, accepted for publication, February 2021.