The next generation of communication networks is predicted to provide an Internet of Things (IoT) interconnecting up to 1 trillion sensors, products, machines, and devices by 2022. This research addresses the critical problem of security in these important emerging networks. Due to the limited capability of typical IoT terminals, the strict delay requirements of anticipated real-time IoT applications, and the broadcast nature of wireless channels, this project investigates physical layer and low complexity cryptographic methods to prevent eavesdroppers from gaining access to IoT data. IoT terminals will often communication via short messages, requiring new theory beyond the existing theory based on infinitely long messages. Since inference takes a central role in IoT systems, this research focuses on inference-based metrics. This project enables student training at the three participating universities, organization of workshops at major conferences, and development of course materials.
Building on very recent work by the members of the project team, the team is developing bounds and approximations to fundamental performance indices in order to understand and design inferential networks based on short packet communications. To gain insight into large-scale inference networks, the team is investigating scaling laws for inferential performance which achieve optimum scaling behavior as one varies the ratio of the number of legitimate users to the number of eavesdroppers. New practical low-complexity cryptographic methods employing non-binary quantization, symbol flipping, channel state information and related approaches are under development and are expected provide significant advantages over existing methods of protecting IoT data. Distributed inference and learning methods that complement the developed approaches are also under development. Fast bootstrapping methods and low-complexity estimation, decision making and classification methods for distributed sensors in IoT are being derived as well.
