Sumant Tambe

We report our experience of developing and using a simple yet an effective flow-based programming language and its distributed execution engine for detecting behavioral anomalies in physical assets in industrial IoT systems. Our stream processing systems is built using the Reactive Extensions (Rx) library for composing asynchronous data streams and the OMG Data Distribution Service (DDS) for publish-subscribe communication over the network.

Condition-based maintenance (CBM) of naval assets is preferred over scheduled maintenance because CBM provides a window into the future of each asset’s performance, and recommends/schedules service only when needed. In practice, the asset’s condition indicators must be reduced, transmitted (off-ship), and mined using shore-based predictive analytics. Real-Time Innovations (RTI), Inc.

The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving, heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers.

The OMG Data Distribution Service (DDS) has been deployed in many mission-critical systems and increasingly in Internet of Things (IoT) applications since it supports a loosely-coupled, data-centric publish/subscribe paradigm with a rich set of quality-of-service (QoS) policies. Effective data communication between publishers and subscribers requires dynamic and reliable discovery of publisher/subscriber endpoints in the system, which DDS currently supports via a standardized approach called the Simple Discovery Protocol (SDP).

Event-driven design is fundamental to developing resilient, responsive, and scalable reactive systems as it supports asynchrony and loose coupling. The OMG Data Distribution Service (DDS) is a proven event-driven technology for building data-centric reactive systems because it provides the primitives for decoupling system components with respect to time, space, quality-of-service, and behavior. DDS, by design, supports distribution scalability. However, with increasing core count in CPUs, building multicore-scalable reactive systems remains a challenge.

This whitepaper describes a powerful C++ template library to allow users to describe their types in plain C++ and use those types directly for data-centric communication over DDS. The library transforms native C++ types into equivalent run-time TypeObject representation as specified in the DDS-XTypes standard. The library obviates the need to describe application-level data-types in external representations, such as IDL, XSD, XML, and DynamicData.