Borzoo Bonakdarpour, Associate Professor, receives NSF grant
Borzoo Bonakdarpour, Associate Professor in Computer Science and Engineering, and Dr. Houssam Y Abbas from Oregon State University have been awarded a 3-year NSF grant. The amount of the award is $500,000, which is equally divided between MSU and OSU. The focus of the project will be on runtime monitoring of edge applications with emphasis on IoT and networks of UAVs. Here is the abstract.
The environments we live in, autonomous technologies we are developing, and even our bodies, are now instrumented: limited-resource nodes collect 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 wearables, 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 will require 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.
In this project, we will be radically extending the reach of runtime monitoring to new and economically important edge applications. This will be achieved by implementing three research thrusts: (1) the first theory 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. Convergence proofs establish soundness. The algorithms incorporate partial knowledge of signal dynamics, where available, to accelerate convergence. (2) A 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) 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.
(Date Posted: 2021-08-06)