Coping with uncertainty in mobile wireless networks

Extremely large-scale wireless networks of interconnected mobile devices are inevitable in the near future. Almost all these varied devices are likely to require some form of Internet access. The uncertainty associated with wireless mobile networks produces unique challenges to achieving seamless integration with the Internet while provisioning end-to-end quality of service (QoS). In particular, the uncertainty in wireless channels, and in network topology, due to node mobility, can bedevil protocols more suited to a "classical" Internet structure. Therefore, new protocols have to be designed that must be: (i) robust against the uncertainty in traffic load, host mobility, resource availability and wireless link characteristics; (ii) adaptive to the network dynamics, thus learning and prediction become integral components in the design methodology; (iii) intrinsically on-line so as to make real-time decisions based on temporal and spatial information. In order to cope with the uncertainty, we propose an overarching theoretical framework to represent relevant network information in terms of underlying entropies, entropy rates and their inter-relationships. We demonstrate how to apply information theoretic learning and prediction tools for collecting and disseminating network state information that can be used for robust and adaptive protocol design. Specifically, we investigate the applicability of this novel framework in designing optimal mobility tracking and resource management, while coping with uncertainty in traffic load, topology control and routing.