Networked Cyber Physical Systems at SRI






Case Study1

Case Study2

Reading List



The increasing availability of devices that can sense and affect their environment in different ways and with different levels of sophistication is the starting point for development of a new generation of Networked Cyber-Physical Systems (NCPS). Such systems provide complex, situation-aware, and often critical services. Examples include traffic control, medical systems, home automation, flexible manufacturing systems, automated laboratories, micro-climate control in buildings, structural monitoring and control, self-assembling structures or systems, unmanned vehicles, networked satellite missions, deep space exploration, and instrumented spaces for surveillance, emergency response, or social networking.


A Logical Framework for Self-Optimizing Networked Cyber-Physical Systems
(supported by National Science Foundation Grant 0932397)

Networked Cyber-Physical Systems (NCPS) present many challenges that are not suitably addressed by existing distributed computing paradigms. They must be reactive and maintain an overall situation awareness that emerges from partial distributed knowledge. They must achieve system goals through local, asynchronous actions, using (distributed) control loops through which the environment provides essential feedback. Typical NCPS are open, dynamic, and heterogeneous in many dimensions, and often need to be rapidly instantiated and deployed for a given mission. Although work on cyber-physical systems is often concerned with the engineering challenge of integrating software with the physical world, it is becoming increasingly clear that more fundamental work is needed to address real-world challenges in a uniform and systematic way. 


Our work is motivated by the observation that especially in challenging environments, that exhibit many forms of unreliability and failures, the physical world imposes severe limitations on how distributed algorithms can operate. We believe that traditional models of distributed computing are too abstract to provide an adequate foundation. Inspired by earlier work on delay- and disruption-tolerant networking, we have developed a distributed computing model based on partially ordered knowledge sharing that makes very few assumptions about the underlying network, its topology, and its characteristics. A distinguishing feature of our model is the use of a partial order to exploit the abstract semantics of knowledge and allow it to be replaced in a distributed fashion while it is cached in the network. A prototype of the model has been implemented in what we call a cyber-application framework that can support simulation and analysis of NCPS applications as well as their deployment on, e.g. multi-processor/multi-core architectures, computing clusters of various flavors, mobile ad hoc networks, and combinations.


On top of this loosely coupled distributed computing model, we are currently pursuing a declarative approach to provide an abstraction from the high complexity of NCPS and avoid error-prone and time-consuming low-level programming.  Our long-term goal is to develop a distributed computational and logical foundation that supports a wide spectrum of system operation between autonomy and cooperation to adapt to resource constraints, in particular to limitations of computational, energy, and networking resources.


Related Projects at SRI:


      DTN: Delay- and Disruption-Tolerant Networking

      Centibots: Coordinated deployment of 100 robots for missions such as urban surveillance

      Commbots: Self-organizing team of mobile robots to maximize network performance

      KARTO: SDK for use in robotic navigation, mapping and exploration

      xTune: A formal methodology for cross-layer tuning of mobile real-time embedded systems

      Group communication: A formal specification of the Spread group communication system


Principles and Foundations for Fractionated Networked Cyber-Physical Systems
(supported by Office of Naval Research Grant N00014-10-1-0365)

In this project we adopt a view of cyber-physical systems that goes beyond the conventional definition of a hardware/software system that is interacting with the physical world. Our goal is to explore a new notion of software that behaves itself closer to a physical or biological system. In other words, we aim to address the fundamental problem by reducing the sharp boundary between physics and computation.  Our rationale is that current models of distributed computing are too abstract by not taking into account fundamental physical limitations and hence are not efficiently implementable or scalable.  Once limitations can be explicitly represented, they can be overcome to some degree, which can be quantified, e.g., probabilistically.  Like in biological systems, diversification, redundancy, and randomization should be utilized to overcome physical limitations whenever possible. In particular, distribution is a source of redundancy and diversification that can be turned from an obstacle into an advantage.

In our approach, software is fractionated by design even beyond the distributed nature of the underlying system, with partially ordered knowledge sharing as the underlying model.  Computation and communication is not rigid but guided by the physical resources, e.g. in an opportunistic fashion.  Our vision that that fractionated software operates as an inherently open system in a highly redundant and diversified way avoiding single points of responsibilities and failure.  Being resource-aware, fractionated software operates in the entire spectrum between autonomy to cooperation. Our distributed computing model is based on partially ordered knowledge sharing, and makes very few assumptions but restricts the shape of fractionated software so that it can run on a wide range of platforms. In particular, it does not assume strong primitives that are powerful but not implementable in a scalable way.


Related Project at SRI:


      Pathway Logic: Analysis of biological entities and processes based on rewriting logic






Last updated: July 12, 2010