toward distributed declarative control of networked cyber-physical systems (ncps)
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Mark-Oliver Stehr, Minyoung Kim, and Carolyn Talcott
Toward Distributed Declarative Controlof Networked Cyber-Physical Systems (NCPS)
1
Website: http://ncps.csl.sri.com
Accepting International Fellows for 2011 !
Key Points
– Partially ordered knowledge-sharing model for loosely coupled distributed computing
– Distributed logic for declarative control
– Simulation case study: Collaborating team of mobile robots
– Implementation of application framework for NCP
Partially Ordered Knowledge Sharing
New Loosely Coupled Distributed Computing Model– Inspired by our earlier work on
delay-/disruption-tolerant networking (DTN)– Minimal assumptions on network
connectivity (can be very unreliable)– Works with dynamic topologies,
network partitions, and mobile nodes– Designed for heterogeneous nodes and
heterogeneous networking technologies– Partial order allows the network to
replace obsolete or subsumed knowledge– Global consistency is not enforced
(impossible in disruptive environments)– Avoids strong non-implementable
primitives, e.g. transactions– Locally each cyber-node uses an
event-based model with local time– Each cyber-node can have
attached cyber-physical devices
Distributed Declarative Control
Key Problem- Traditional logics are not designed for distributed reasoning- Logics are traditionally closed systems, i.e. not interactive
Requirements/Assumptions- Need to consider the NCPS as a single asset- Logical theory/specification is available to all nodes- Nodes contribute resources according to their capabilities
Knowledge is transparently shared- Knowledge = Facts + Goals- Facts can represent observations- Goals can represent control objectives
Distributed logical framework- Integrates forward and
backward reasoning- Partial order is essential part
of the distributed logic
Predicates for Distributed Surveillance
Different Flavors of Predicates– Cyber-facts and cyber-goals serve as interface to environment (user, devices)– Ordinary facts/goals are used internally by the theory
Sample Theory for Distributed Surveillance
Sample Theory for Distributed Surveillance
Interpretation– O1: New observations replace old observations– O2: New control goals replace old goals– O3 & O4: Solved goals (i.e. facts) replace unsolved subgoals
Sample Execution
Visualization of a Distributed Execution– Reasoning can occur anywhere in the network
Cyber-Application Framework
Architecture– Cyber-framework implements partially ordered knowledge-sharing model– Logical framework is implemented as a cyber-application– Can coexist and interoperate with conventional code
Cyber-Application Framework
Implementation– Applications cannot distinguish between simulation and reality
• model-based simulation/analysis mode• real-world deployment/execution mode
System Implementation
– Simulation vs. Real-world for Physical/Network Layer
– Neighbor Discovery
– Knowledge Dissemination Protocols
– Multi-threaded Execution and Simulation
1. Network/Physical Layer
Core Idea– Applications cannot distinguish between simulation and reality
• model-based simulation/analysis mode• real-world deployment/execution mode
Simulation World– SimNode, SimDevice– Comm. among cyber-nodes via
• DTN simulator with abstract mobility• Stage multi-robot simulator with wireless network model
Real World– RealNode, RealDevice– Comm. among cyber-hosts via UDP– Time synchronization
Cyber-framework supports a mechanism that allows same application code to be used for simulation and deployment.
2. Neighbor Discovery
Core Idea– To disseminate knowledge via opportunistic links, each cyber-engine needs to
keep track of its immediate neighborhood– Neighbor list is refreshed between cyber-engines in periodic manner
Implementation– Hello knowledge is posted periodically between cyber-engines (broadcast)– Hello knowledge includes:
• Public/private IP address, hop count, engine ID, expiration time
– It is possible to be explicitly define other engine’s address (unicast)– Multi-hop discovery is supported by forwarding hello knowledge until user-
defined maximum hop count reached– Multi-hop discovery allows some nodes to operate as discovery facilitators
(registry-like service)
Cyber-framework manages up-to-date neighborhood information to disseminate knowledge via opportunistic links.
3. Knowledge Dissemination Protocol
Probabilistic Reflection– Single message protocol
• Window of opportunity can be small
– Minimal assumptions on network• Links can be unidirectional or bidirectional• Error rate can be high• Only needs eventual weak connectivity
– Periodically, for each knowledge item k and for each outgoing link: • If potential receiver is not known to be aware of k it will be sent out• Otherwise k it is sent out with a non-zero probability
defined by a reflection parameter divided by number of outgoing links
A B
k
k k
k
k k
COptimized Deterministic Flooding– Disseminates knowledge to all neighbors
that are not (known to be) aware of the particular unit of knowledge (but only once)
The knowledge dissemination layer will replace and discard all instances of inferior knowledge based on partial order semantics.
4. Multi-threaded Execution
Cyber-framework supports various configurations for parallel execution as well as their arbitrary combinations.
Implementations – Fine-grained parallel execution
• Each cyber-node with its own event queue• A single shared event with a thread pool
– Coarse-grained parallel execution• Multiple cooperating cyber-engines• Can be used at different levels
– On a single host (local communication)– Hosts on the same subnet (broadcast)– Beyond subnets (unicast)
Conclusions
Contributions– Truly distributed logical framework– Cyber-predicates enable interaction with the physical world– Facts and goals treated on an equal footing– Covers entire spectrum between autonomy and cooperation– Tested with abstract mobility model and Stage multi-robot simulator
Related Work– Declarative Networking (P2, DTN, XG)– Modular Robotics (Regiment, Meld)– Fractionated Software/Systems
Future Work– Reasoning performance improvements– Integration with distributed dynamic optimization– Exploring other applications, e.g. cooperative flight control in
UAV testbed consisting of 10 UAVs and additional ground nodes
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