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Distributed Services for Microsensor Networks Sensorware 2/3/2000 Jon Agre Rockwell Science Center.

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Presentation on theme: "Distributed Services for Microsensor Networks Sensorware 2/3/2000 Jon Agre Rockwell Science Center."— Presentation transcript:

1 Distributed Services for Microsensor Networks Sensorware 2/3/2000 Jon Agre Rockwell Science Center

2 Improve Software Infrastructure for Sensor Nets Deterministic Realtime Embedded Environment Distributed Middleware Services Scalable, Deterministic, Efficient, High Performance Mobile Script Control Demonstration of Distributed Services Technologies Target Tracking with Mobile Script (using middleware services) Objectives for Sensorware

3 Related Microsensor Projects - Candidate Software Functions for Middleware DARPA AWAIRS Multihop Routing, TDMA Protocol, Self-organization, Beamforming Algorithms Synchronized Array Data Collection Application, Detection Network Application ARL Sensors and Displays Single-node Target Classification Algorithms for seismic, acoustic and magnetic, Coordinate-based Displays, User Interaction Models, Hand-held Displays Windows gateway (C++) Rockwell PLGR LAN Multiple, slowly mobile user protocol (multihop) GPS and positioning interface ONR Condition-based Maintenance (CBM) Machinery diagnostics with High speed vibration, temperature, pressure Web-based control Interface (Java) ONR Open Systems Architecture for CBM Causal Network Diagnostics Corba-based interface for distributed systems

4 Sensor Node Signal Processing Architecture Continuous sample, HW filter, threshold compare Process single sensor Fuse multiple on-module sensors Query/corroborate with neighbors Fuse features with neighbors Beamformation cooperative Increasing Quality (decreasing false alarm rate, increasing detection rate) Higher Power Expended Alarms may be reported and awaken next layer autonomous

5 User Interaction with Microsensor Network: Examples Inform sensor system to save/expend energy –no enemy activity expected; go to low alert (or vice versa: high alert) –friendlies or noncombatants entering zone; ignore –coverage by another sensor; sleep –unimportant area of operations for some duration Adjust energy expenditure in different dimensions –adjust level of decision detail (coarse - fine), both continuous and event-based –adjust required minimum latency (including heartbeat) –adjust required system lifetime Ask for more detail from one or a small set of select nodes Inform network of likely targets - adjust Bayesian priors Inform network of spatial character expected of targets Inform network of new target type - provide new template Provide network new signal processing software Command network to increase covertness (LPD/LPI) –remain radio silent until T minutes after target leaves area

6 User Interaction with Microsensor Network (continued) Inform network of conditions it cannot autonomously determine –from external sources; weather (hail, snow, etc.), muddy terrain, animal movements, etc. Manually aid decisioning based on explicit knowledge or inference –deduce 2 reported targets are actually one –help resolve target type based on deduction or fusion with external sources Inform network of impending addition of more nodes (overseeding) and when to expect it to occur –network can expend more energy in anticipation of resupply –will adjust network entry access protocol to speed process and save energy –disconnected networks can anticipate bridging (merging) Lifetime Rapidity of info (latency -1 ) Detail and/or Certainty User selects performance:

7 Situation Awareness in MOUT Exercise at Ft Benning McKenna MOUT Facility

8 GPS Receiver Local Area Network (PLGR-LAN) Low cost situational awareness and messaging tool for the warfighter. –Allows for localized C 2 for the squad or platoon in an urban environment Can also be used with distributed sensors to provide additional intelligence gathering and alerting capabilities. Rockwell Collins

9 Condition-based Maintenance Failure Prediction for Individual Units and Complex Systems Condition-based Maintenance/Failure Prediction –Motor Failure Prediction –Process Monitoring –High Value / Critical Asset Monitoring –Systems Monitoring CBM / FP Provides for: –Substantial Maintenance, Logistics and Unscheduled Down-time Cost Savings –Manpower Reduction –Increased Safety

10 Internet-based Demonstration of WINS for CBM WINS Nodes In Chilled Water Pump Room Ten wireless nodes transmit temperature and vibration information to the basestation node -> internet server Basestation Node Web Server

11 Sensor Node Icons present summary color- coded bar graphs of bearing health indicators for quick problem identification. Gray colored icons are motors that are presently turned off. Internet-based Demonstration of WINS for CBM WINS Network - Main Screen Java Applet running on WINS webpage.

12 Ball Pass Frequency, Inner Race Fault frequency and 19 harmonics Bearing Diagnostics Report Baseline Reading Largest Reading Double clicking on icon brings up Bearing Diagnostics Detail Screen Adjustable scale settings Poll sensor nodes for another test result

13 ONR OSA-CBM Overall Program Goals Develop an integrated, condition based monitoring (CBM) system –define the software infrastructure (or middleware) define groups of software interfaces for each OSA- CBM layer –use existing standards if available –define non-existing standards Demonstrate OSA-CBM capability –demonstrate capability and usability of developed standards on different diagnostic platforms (RSC Nodes)

14 TRANSDUCER SIGNAL PROCESSING CONDITION MONITOR The highest layer of machine data DATA ACQUISITION HEALTH ASSESSMENT The lowest layer of embedded human intelligence DECISION SUPPORT PRESENTATION XML 1451.X SENSOR MODULE Presentation layer is the man/machine interface. May query all other layers. Decision support utilizes spares, logistics, manning etc. to assemble maintenance options. Health Assessment is the lowest level of goal directed behavior. Uses historical and CM values to determine current health. Multi-site condition monitor inputs. CM gathers SP data and compares to specific predefined features. Highest physical site specific application. Signal Processing provides low-level computation on sensor data. Data Acquisition- conversion/ formatting of analog output from transducer to digital word. May incorporate meta-data. Ala X Transducer converts some stimuli to electrical signal for entry into system. The Vertical Arrows indicate Process (Logic) flow, the Red Arrows indicate Network (connection) flow. HTML XSL PROGNOSTICS Prognostics considers health assessment, employment schedule, and models/ reasoners that are able to predict future health with certainty levels and error bounds. XML MIMOSA OPC STEP OSA/CBM (PROPOSED) XML

15 Sensorware Progress Task 1: Requirements Definition SensorWare Operating System selected after evaluation MicroC/OS - C-based, open source, lightweight, realtime OS, modifiable Base middleware capabilities being defined in conjunction with SenseIT Several specialized sensor network services defined System Coverage Signal Processing Architecture Stack (from AWAIRS) Synchronized Sampling Spatial-based Communication Implementation of middleware will be in both Windows CE and MicroC/OS Task 2: Low Level APIs Draft version of C-based API document Architecture improvements - (e.g., interrupt handling, radio interface) - Coding in progress API Implementation - Coding initiated Reference Applications (Dlog, Detection Net) - Coding in progress System and software provided to UCLA Port of Gateway DLL to Windows CE - Nearly complete Emulation of PlatformConnect Windows CE to allow execution of SenseIT - Coding API will be supported as feasible

16 Task 3: Middleware Services Mobile Scripting implementation - (UCLA) New Coverage determination algorithm under investigation Task 4: Sensor Node Improvements New Processor Board Increased SRAM (1 Mb) and Flash (2 Mb) Parallel Bus Interface Two RS-232, USB, Watchdog New Sensor Modules Acoustic Accelerometer New Package Design Rechargeable batteries Solar power Task 5: Demonstration and Integration Target tracking with mobile scripts demonstration on track Joint demonstration of capabilites planned with DSN Project (ISI, VaTech, UCLA) Sensorware Progress (cont)

17 Parallel Interface Power Supply Batteries Processor Radio Seismic Magnetic Acoustic Geo Solar Cell Power Supply (TOP) Processor (Bottom) Processor (Top) Serial Interface Sensor Side Improved Sensor Package GPS* *Design Only

18 Middleware Services Base Services (Defined in Conjunction with SenseIT) Communications Protocol Stacks Signal Processing Stack Power Management User Interaction Network Synchronization Query Processing Configuration (Bootup, health status, maintenance) Fault Tolerance Security/Authentication Specialized Services Mobile Script System Coverage (Sensing) Synchronized Sampling Spatial-based Communication

19 Middleware Working Definition: A software function(task) that satisfies: At least two applications use the function, or the applications need to run on at least two different type platforms Middleware has two primary functions Stitch together low-level APIs (Platform Dependent) to implement service transparently to application Provide distributed resource management/allocation (Field Organization) e.g., More global knowledge is necessary Network-wide power management Computation performance optimization What is Middleware?

20 Application Middleware Low Level APIs (at Source) Physical Layer Peer-to-peer Master Cluster SendMsg(A,synch(B)) SendMsg(B,synch(A)) SendMsg(A, RouteData(C)) SendMsg(B,RouteData(C)) Need to Synchronize Samples of A and B and Send to C for Processing SampleSynch(A,B): RouteData(A,C): RouteData(B,C): SendMsg(M2, synch (A,B)) SendMsg(M2, ReturnData((A,B),C) A B A B C C M1 M2 M2:SendMsg(A,B, synch) M2:SendMsg(A,B ReturnData) M2:SendMsg(A,B,Sample)... A:SendMsg(M1, Data) B:SendMsg(M1, Data) M2:Msg(M1,RouteData(C)) A:SendMsg(B, synch) A:SendMsg(B, ReturnData(C) A:SendMsg(B, Sample)... A:SendMsg(i, Data(C)) B:SendMsg(i, Data(C)) D D Middleware Example Low Level APIs (at Node Participants)

21 Coverage Service Sensor Coverage Performance Characterization Boundaries (perimeters) Density Susceptibility to Breach Scalability Bounded answer: quantization/compression Bounded Messaging Throughout Process of Determining Answer Spatially bounded query/response Algorithm Distributed, incremental computation Guaranteed termination Efficient communications network support Instantiation Global characterization executed at bootup User-activated query Generalization Viewpoint-dependent resolution; distributed database updates (Assume that each sensor node knows its position.)

22 Coverage Performance Characterization Coverage Boundaries Given sensor detection range R, find the curve(s) that enclose the area covered by the sensors. Generally there may be many curves due to “holes”; concern for scalability. Density of Sensor Field Define in terms of distances associated with Voronoi vertices. Susceptibility to Breach A breach will be attempted between a given origin and destination. If the path having the least likely detection is taken (a geodesic), what is the detection probability? Optimal path will occur along Voronoi edges. Generalization: Origin/destination selected from point set (e.g., line segment or region) Approximate coverage boundary Breach destination Breach origin Radius of Delaunay circle characterizes Voronoi vertex Sensor node site

23 Conclusion All Tasks on track or ahead of schedule Team in place and working with prototype sensor nodes and user interface software Several new subtasks incorporated to insure integration with other SensIT projects Joint demonstrations with DSN team (ISI, VaTech, UCLA) planned for August FY00, FY01 and FY02 Target Tracking with Mobile Scripts planned for August Demonstration

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