AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Preliminary results support the value of combining QoS admission, multi-objective path selection, and rapid backup-route switching for reliable communication in dynamic ad hoc networks.
The proposed QoS-Aware Routing (QAR) framework incorporates application-specific QoS requirements directly into the routing process, enabling route selection to better satisfy the communication demands of diverse FANET applications.
Different routing methods for cognitive sensor networks are looked at, including drop ratio, which will assist in lowering the cost of living and guarantee that the data can be handled by nodes with a low dropping ratio.
This paper presents AdaptiveNet SDN, a software-defined networking simulation framework designed to maintain communication when links, nodes, or groups of adjacent links fail. The system combines a multi-objective adaptive routing algorithm with a self-healing controller, heartbeat-based failure detection, bandwidth-aw...
MSQ-RPL leverages a reward-propagation mechanism inspired by Q-learning to dynamically guide forwarding decisions, combining adaptive scoring with lightweight control dissemination to enhance scalability and local responsiveness.
This study proposes a Q-learning-based mobility-aware tree routing protocol (QMTR) for WBANs integrated with vehicular ad hoc networks (VANETs) to support reliable transmission of passengers’ health data to remote medical centers.
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