WiFiUS: Collaborative Research: Scalable Edge Architecture for Massive Location-Aware Heterogeneous IoT Systems

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The "Scalable Edge Architecture for Massive Location-Aware Heterogeneous IoT Systems" projectaddresses essential research problems for developing network design and Internet of Things (IoT)systems in both high bandwidth and low bandwidth environments. By defining an efficient securityand scalability-enhancing edge architecture that moves data processing close to users, it minimizesdata transfer latencies and overhead in the network. The fusion of sensor data from different sourcescan significantly improve the efficiency of many existing systems. In some areas, massivebandwidth is about to become available to cellular and WiFi networks in the millimeter wave bands.The Federal Government recently opened up 28 GHz at frequencies above 24GHz and promises to revolutionize wireless systems and enable IoT applications never beforeconceived, particularly in dense urban areas. Smart traffic and connected (autonomous) cars are anexample application area enabled by the new bandwidth, which requires combining these differentapproaches in the architectural design of large-scale IoT systems. Integrating IoT with edge/fogcomputing, millimeter wave (mmWave) technologies and distributed processing enables optimizingthe system level performance considering system capacity, reduced network bandwidth for data andcontrol traffic, increased system level programmability and automation, accurate location-aawareness,virtualization, low latency, scalability, and enhanced security and privacy. One of thenovel aspects of our proposed system is that it transitions seamlessly from an emulated andsimulated environment to actual production deployment, and mixtures of these modes, facilitatingrobust and reliable IoT systems at scale.The project provides contributions in several essential areas of IoT network architectures andsecurity: 1) minimizing the need of manual configuration in large-scale IoT networks; 2) scalableauthentication and key management systems; 3) efficient distributed IoT architecture to performcomplex and delay-sensitive tasks, with rapid deployment and prototyping; 4) secureinteroperability between heterogeneous IoT devices; and 5) positioning and capacity optimizationbased on mmWave communications, considering especially smart traffic and vehicle-to-vehiclecommunications. The project proposes a multi-layered approach for scaling IoT systems insimulation and emulation, allowing to combine physical systems with emulated systems,incorporating new mmWave RF and network models, network emulation, virtual systems, modelsof the physical world and user interfaces. The emulation system allows the team to explore mobileedge and fog computing to enhance efficiency, reduce control-loop delays and assure privacy ofsensitive data. A new authentication model and naming system allows scaling for deployment andprogramming. The prototype open-source IoT emulator, along with the mmWave channel models,developed in the project will allow industrial IoT system developers to more rapidly and reliablydevelop new IoT systems. The authentication and naming components will be submitted forpossible standardization. The new channel models are likely to inform spectrum allocationdecisions for mmWave bands by national regulators.