Facilitating large-scale load-efficient Internet of things (IoT) connectivity is a vital step toward realizing the networked society. Although legacy wide-area wireless systems are heavily based on network-side coordination, such centralized methods will become infeasible in the future, by the unbalanced signaling level and the expected increment in the number of IoT devices. In the present work, this problem is represented through self-coordinating for IoT networks and learning from past communications. In this regard, first, we assessed low-complexity distributed learning methods that can be applied to IoT communications. We presented a learning solution then, for adapting devices’ communication parameters to the environment to maximize the reliability and load balancing efficiency in data transmissions. Moreover, we used leveraging instruments from stochastic geometry to assess the behavior of the presented distributed learning solution against centralized coordinations. Ultimately, we analyzed the interplay amongst traffic efficiency, communications’ reliability against interference and noise over data channel, as well as reliability versus adversarial interference over feedback and data channels. The presented learning approach enhanced both reliability and traffic efficiency within IoT communications considerably. By such promising findings obtained via lightweight learning, our solution becomes promising in numerous low-power low-cost IoT uses.
Large Scale Synchronous/Asynchronous Collaborative Distributed Learning In A Graduate Level Computer Engineering Course