Shahid Sultan¹, Qaisar Javaid¹, Eid Rehman^2,*, Ahmad Aziz Alahmadi³, Nasim Ullah³, Wakeel Khan⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6089-6106, 2022, DOI:10.32604/cmc.2022.021374

Abstract For efficient and robust information exchange in the vehicular ad-hoc network, a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel. In addition, we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards. Unfortunately, there may be some misbehaving nodes and due to their selfish and greedy approach, these nodes may not help others on the network. To deal with this challenge, trust-based misbehavior avoidance schemes are generally… More >

Daniel Godfrey¹, Beom-Su Kim¹, Haoran Miao¹, Babar Shah², Bashir Hayat³, Imran Khan⁴, Tae-Eung Sung⁵, Ki-Il Kim^1,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3671-3692, 2021, DOI:10.32604/cmc.2021.017475

Abstract The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol,… More >