In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based medium access control (MAC) protocols. As the scale of the wireless ad hoc network (WANET) increases, traditional MAC solutions are becoming obsolete. Dynamic topology, resource allocation, interference management, limited bandwidth and energy constraint are crucial problems needing resolution for designing modern WANET architectures. In order for future MAC protocols to overcome the current limitations in frequently changing WANETs, more intelligence need to be deployed to maintain efficient communications. After introducing some classic RL schemes, we investigate the existing state-of-the-art MAC protocols and related solutions for WANETs according to the MAC reference model and discuss how each proposed protocol works and the challenging issues on the related MAC model components. Finally, this paper discusses future research directions on how RL can be used to enable MAC protocols for high performance.
Many incentive schemes address the selfishness issue in opportunistic networks and show performance improvement by simulations. However, the insights of incentive schemes that affect network performance are not clear. Network capacity analysis can reveal how factors affect performance, which is a guideline for new designs. To analyze incentive schemes, a well-defined mathematical model is necessary, which cannot be achieved by existing analytical models based on empirical formulas or types of incentive schemes. First, this paper proposes such a model to show the incentive degree with the incentive scheme, cooperation degree, energy usage, buffer usage, and security based on a quantum game model. Verification compares the model with delivery ratios that show impacts on selfish nodes in simulations under two typical incentive schemes. Then, network capacity is analyzed with this model and a sparse clustering regime that has similar mobility to opportunistic networks in order to show factors for future designs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.