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

如今的自主技术作为有限资源的节点，可以实时收集大量数据，以更好地跟踪和解释系统和环境的行为。思科2019年的一项研究发现，全球有285亿个联网设备和连接。在这个庞大的生态系统中，一类未来的关键应用程序脱颖而出：使用网络节点来检测系统或其物理环境中的安全风险的软件应用程序。需要此类监控的示例应用包括自动驾驶车队、健康监测可穿戴设备、搜索和救援以及气候监测。这些应用已经改变了人们的生活，但缺乏及时、可靠和节能的工具来监控它们的正确运行。这个项目的重点就是提供这样一个监控基础设施。这需要克服几个困难。首先，监视代码必须自动生成，而不是手工编写，因为这样可以减少出错的可能性。监视器必须能够处理由观察到的现象（如波高或温度）产生的模拟/物理信号。它还必须处理不同节点上的漂移时钟，这些时钟在时间上不会读取相同的时刻。它还必须能够抵御节点崩溃和恶意攻击。最后，它必须分布在节点上，而不是集中，因为这样更不容易发生灾难性故障。该项目从根本上将运行时监控的范围扩展到新的、经济上重要的边缘应用程序。这是通过实施三个研究重点来实现的。(1)为连续时间、异步信号的分布式监测发展理论和算法。这些算法在边缘节点本身上执行分布式优化，从而消除了对中央监视器的需求。该算法结合了信号动力学的部分知识，在可用的情况下，加速收敛。(2)开发增量监控的理论和算法，在某些节点崩溃的情况下，中间计算结果仍然可以被应用程序使用。监视器还将容纳故意伪造其数据的节点。(3)在真实的自动驾驶汽车上对算法进行严格的验证，以在完整的软件堆栈中以及在存在真实噪声和故障条件的情况下建立其性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.