Collaborative Research: SHF: Small: Runtime Verification at the Edge

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Today's autonomous technologies are now instrumented as limited-resource nodes collecting large amounts of data in real-time to better track and explain their system’s and environment’s behavior. A 2019 Cisco study found that there are 28.5 billion networked devices and connections in the world. Within this massive ecosystem, one class of future critical applications stands out: software applications that use networked nodes to provide detection of safety risks in the system or its physical environment. Example applications that require such monitoring include fleets of autonomous vehicles, health-monitoring wearable devices, search-and-rescue, and climate monitoring. These applications are already transforming lives, but suffer from a lack of timely, reliable and energy-efficient tools to monitor their correct operation. The focus of this project is to provide precisely such a monitoring infrastructure. This requires overcoming several difficulties. First, the monitoring code must be automatically generated, rather than hand-written, as this reduces the likelihood of errors. The monitor must be able to deal with analog/physical signals produced by the observed phenomena, such as wave heights or temperatures. It must also deal with drifting clocks on the different nodes, which do not read the same moment in time. It must also be resilient to node crashes and malicious attacks. Finally, it must be distributed over the nodes, rather than centralized, since this is less prone to catastrophic failures. The project radically extends the reach of runtime monitoring to new and economically important edge applications. This is achieved by implementing three research thrusts. (1) Develop theory and algorithms for distributed monitoring of continuous-time, asynchronous signals. The algorithms perform distributed optimization on the edge nodes themselves, thus eliminating the need for a central monitor. The algorithms incorporate partial knowledge of signal dynamics, where available, to accelerate convergence. (2) Develop theory and algorithms for incremental monitoring, where intermediate calculation results are still usable by the application should some nodes crash. The monitors will also accommodate nodes that intentionally falsify their data. (3) Conduct a rigorous validation of the algorithms on realistic autonomous vehicles, to establish their performance within a full software stack and in the presence of real-world noise and failure conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.