Cyber-physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. Research on CPSs is fundamentally important for engineered systems in many important application domains such as transportation, energy, and medical systems. We overview CPS research from both a historical point of view in terms of technologies developed for early generations of control systems, as well as recent results on CPSs in many relevant research domains such as networked control, hybrid systems, real-time computing, real-time networking, wireless sensor networks, security, and model-driven development. We outline the potential for CPSs in many societally important application domains.

http://cesg.tamu.edu/wp-content/uploads/2012/03/ps_files/12-02-10-CPS-Centennial.pdf

Cyber–Physical Systems: A Perspective at the Centennial

Sensor networks can be considered distributed computing platforms with many severe constraints, including limited CPU speed, memory size, power, and bandwidth. Individual nodes in sensor networks are typically unreliable and the network topology dynamically changes, possibly frequently. Sensor networks also differ because of their tight interaction with the physical environment via sensors and actuators. Because of this interaction, we find that sensor networks are very data-centric. Due to all of these differences, many solutions developed for general distributed computing platforms and for ad-hoc networks cannot be applied to sensor networks. After discussing several motivating applications, this paper first discusses the state of the art with respect to general research challenges, then focuses on more specific research challenges that appear in the networking, operating system, and middleware layers. For some of the research challenges, initial solutions or approaches are identified.

https://www.cse.wustl.edu/~lu/papers/pieee03.pdf

Real-time communication and coordination in embedded sensor networks

We consider pervasive computing applications that process and aggregate data-streams emanating from highly distributed data sources to produce a stream of updates that have an implicit business-value. Middleware that enables such aggregation of data-streams must support scalable and efficient self-management to deal with changes in the operating conditions and should have an embedded business-sense. In this paper, we present a novel self-adaptation algorithm that has been designed to scale efficiently for thousands of streams and aims to maximize the overall business utility attained from running middleware-based applications. The outcome is that the middleware not only deals with changing network conditions or resource requirements, but also responds appropriately to changes in business policies. An important feature of the algorithm is a hierarchical node-partitioning scheme that decentralizes reconfiguration and suitably localizes its impact. Extensive simulation experiments and benchmarks attained with actual enterprise operational data corroborate this paper's claims

/pdf/distributed-stream-management-using-utility-driven-self-bitl6k8srm.pdf

Distributed Stream Management using Utility-Driven Self-Adaptive Middleware

SUMMARY The OMG’s Real-Time CORBA (RT-CORBA) and Fault-Tolerant CORBA (FT-CORBA) specifications make it possible for today’s CORBA implementations to exhibit either real-time or fault tolerance in isolation. While real-time requires ap rioriknowledge of the system’s temporal operation, fault tolerance necessarily deals with faults that occur unexpectedly, and with possibly unpredictable fault recovery times. The MEAD (Middleware for Embedded Adaptive Dependability) system attempts to identify and to reconcile the conflicts between real-time and fault tolerance, in a resource-aware manner, for distributed CORBA applications. MEAD supports transparent yet tunable fault tolerance in real-time, proactive dependability, resource-aware system adaptation to crash, communication and timing faults with bounded fault detection and fault recovery. Copyright c � 2005 John Wiley & Sons, Ltd.

/pdf/mead-support-for-real-time-fault-tolerant-corba-130jhm0pdx.pdf

MEAD: support for Real‐Time Fault‐Tolerant CORBA

We consider distributed applications that continuously stream data across the network, where data needs to be aggregated and processed to produce a 'useful' stream of updates. Centralized approaches to performing data aggregation suffer from high communication overheads, lack of scalability, and unpredictably high processing workloads at central servers. This paper describes a scalable and efficient solution to distributed stream management based on (1) resource-awareness, which is middleware-level knowledge of underlying network and processing resources; (2) overlay-based in-network data aggregation; and (3) high-level programming constructs to describe data-flow graphs for composing useful streams. Technical contributions include a novel algorithm based on resource-aware network partitioning to support dynamic deployment of data-flow graph components across the network, where efficiency of the deployed overlay is maintained by making use of partition-level resource-awareness. Contributions also include efficient middleware-based support for component deployment, utilizing runtime code generation rather than interpretation techniques, thereby addressing both high performance and resource-constrained applications. Finally, simulation experiments and benchmarks attained with actual operational data corroborate this paper's claims

/pdf/resource-aware-distributed-stream-management-using-dynamic-4f7ecrfsr9.pdf

Resource-Aware Distributed Stream Management Using Dynamic Overlays

Real-time embedded systems have evolved during the past several decades from small custom-designed digital hardware to large distributed processing systems. As these systems become more complex, their interoperability, evolvability and cost-effectiveness requirements motivate the use of commercial-off-the-shelf components. This raises the challenge of constructing dependable and predictable real-time services for application developers on top of the inexpensive hardware and software components which has minimal support for timeliness and dependability guarantees. We are addressing this challenge in the ARMADA project.

ARMADA is set of communication and middleware services that provide support for fault-tolerance and end-to-end guarantees for embedded real-time distributed applications. Since real-time performance of such applications depends heavily on the communication subsystem, the first thrust of the project is to develop a predictable communication service and architecture to ensure QoS-sensitive message delivery. Fault-tolerance is of paramount importance to embedded safety-critical systems. In its second thrust, ARMADA aims to offload the complexity of developing fault-tolerant applications from the application programmer by focusing on a collection of modular, composable middleware for fault-tolerant group communication and replication under timing constraints. Finally, we develop tools for testing and validating the behavior of our services. We give an overview of the ARMADA project, describing the architecture and presenting its implementation status.

/pdf/armada-middleware-and-communication-services-4da9iaq4ak.pdf

M. Bjorkland

Papers

ARMADA Middleware and Communication Services