Improving Robustness of Distributed Condition Monitoring by Exploiting the Semantics of Information Change

Robust Autonomy

Volume

Robust Autonomy

Contents

Monitoring complex conditions over multiple distributed, autonomous information agents can be expensive and difficult to scale. Information updates can lead to significant network traffic and processing cost, and high update rates can quickly overwhelm a system. For many applications, significant cost is incurred responding to changes at an individual agent that do not result in a change to an overriding condition. But often we can avoid much work of this sort by exploiting application semantics. In particular, we can exploit constraints on information change over time to avoid the expensive and frequent process of checking for a condition that cannot yet be satisfied. We motivate this issue and present a framework for exploiting the semantics of information change in information agents. We partition monitored objects based on a lower bound on the time until they can satisfy a complex condition, and filter updates to them accordingly. We present a simple analytic model of the savings that accrue to our methods. Besides significantly decreasing the workload and increasing the scalability of distributed condition monitoring for many applications, our techniques can appreciably improve the response time between a condition occurrence and its recognition.