Sensor Webs An Emerging Concept for Future Earth Observing Systems

1. Sensor WebsAn Emerging Concept for Future Earth Observing Systems An EOS Brown-Bag Lunch Presentation Stephen J. Talabac NASA/GSFC Code 586 April 11, 2003

2. Agenda • Background, terminology, and fundamental concepts • A candidate sensor web definition • Taxonomy and properties of sensor web nodes • Possible sensor web classes and their properties • Representative scenarios that may benefit from the sensor web concept • A survey of related research activities

3. Background “The best way to be ready for the future is to invent it.”John Sculley – CEO, Apple Computer • NASA’s and the Earth-Science Enterprise’s strategic plans identify “sensor webs” as a new paradigm for conducting future science observations. • “We envision multiple cooperative spacecraft that operate in interactive networks to thoroughly explore diverse phenomena” • “…intelligence will become an integral part of future spacecraft, enabling systems to make real-time decisions in the uncertain and unforgiving space environment”. • “Deploy cooperative satellite constellations and intelligent sensor webs.” that will facilitate“information synthesis” and increase our “access to knowledge”

4. Background (continued) • The Sensor Web concept is being refined and various views of it appear to be converging. • We are presently exploring questions such as: • What exactly “is” a sensor web? • What are the specific characteristics that a sensor web should possess? • What are the various behaviors that a sensor web may manifest? • …and most significantly: What are potential applications of sensor webs that can be of significant benefit to the Earth science community?

5. Sensor Webs:A Systems Engineering Approach • Establish a common terminology vocabulary • Identify sensor web nodes, define their properties, and develop a node taxonomy • Describe how nodes might interact and used as building blocks to develop a taxonomy of sensor web classes • Identify science scenarios that may benefit from the various sensor web classes • Identify candidate sensor web architectures and establish evaluation criteria

6. Space Mission Architecture - Today Direct instrument readout or On-board recorder downlink to Ground Station Bent pipe communications Science Processing Center Science Processing Center Graphic Credit: NASA/GSFC 2000 Survey of Distributed Spacecraft Technologies and Architectures for NASA’s Earth Science Enterprise in the 2010-2025 Timeframe

7. Space Mission Architecture - Today • Classic “stovepipe1” science data collection and mission operations • Single or separate spacecraft missions with little or no dynamic planning for opportunistic science observations or handling unexpected observing conditions • Data is often simply recorded and downlinked to ground systems for processing and analysis • “Fire hose” of raw data bits downlinked to the ground with little or no regard to sending just the most meaningful science data • Little, if any, on-board science instrument processing • No real time collaborative information sharing between sensors, spacecraft, or investigators • Interspacecraft communications typically relegated to bent pipe communications to the ground segment • e.g., via TDRSS in support of command uplinks, telemetry downlinks 1stovepipe - a self-standing, narrowly focused application that solves a discrete set of problems

8. Graphic Credit:NASA/GSFC: 2000 Survey of Distributed Spacecraft Technologies and Architectures for NASA’s Earth Science Enterprise in the 2010-2025 Timeframe Space Mission Architecture – “Tomorrow”Distributed Spacecraft Systems & Sensor Webs

9. Space Mission Architecture - “Tomorrow” • High degree of synergy between a diverse suite of platforms • Space-based • Atmospheric (e.g., aircraft, balloons) • Land (e.g., in-situ weather stations) and sea (e.g., buoys) • Subsurface probes • Automated science data collection and mission operations • Multiple spacecraft and platforms perform dynamic planning for opportunistic science observations • Real time collaborative information sharing between sensors, spacecraft, or investigators • Interspacecraft communications becomes an intrinsic characteristic of distributed space platforms

10. Emerging & Evolving Technologies • Micro-electromechanical Systems (MEMS) • Nanospacecraft • Low mass, small footprint in-situ sensors • Advanced processors & high capacity storage provide • Greater opportunity for on-board processing • Embedded software to build “intelligent” processing nodes • Communications • Evolving space-based IP comms protocols and interoperability with terrestrial networks • Ubiquitous wireless comms allows for “ad hoc” Sensor Web networks to be established • Peer-to-peer networking MEMS Microthruster Array - 10-4 Ns Impulse Photo : TRW NASA NMP - ST5 spacecraft

11. A device that measures a physical property of a natural or man-made phenomena that is of interest to Earth- and space-scientists A sensor is an integral part of, and provides its measurements to, a science instrument. Examples A prism and CCD sensor array that captures and measures photons of different energies (i.e., wavelengths) and intensities (i.e., photons per second) A water flow rate sensor Two types of sensor measurements in-situ (e.g., magnetic field strength) remotely sensed (infrared energy reflected from clouds at different heights) Measurements are not necessarily restricted to photons Rainfall amount Acoustic energy Chemistry 1D/2D/3D/6DOF directional measurements Note 1: These are not meant to be strict definitions. Instead they are intended to provide an “informal” definition of fundamental concepts, often expressed in terms of other widely accepted and commonly understood terminology. Terminology1 - Sensor

12. Terminology - Science Instrument • A self-contained infrastructure that • Receives (digital or analog) data from one or more sensors • Provides temporary storage for the measurements • Transmits sensor data to a node • It may have its own infrastructure to sustain its own operation (e.g., power, structure, environmental, etc), or it may rely entirely on the node (e.g., a spacecraft bus) for this infrastructure • Examples • rain gauge • magnetometer • IR observatory • laser interferometer

13. Terminology - Node • A self-contained computing, storage, and communications device. • A node may, but not necessarily, be connected to one or more science instruments. • A node provides mechanical, power, thermal, electrical, communications, control, timing, environmental protection, etc. to support its own operation and possibly for any science instrument directly connected to the node. • A node may be a spacecraft that has one or more science instruments • A node may be a computing or storage system that does not have any associated science instrument(s) • Example of nodes: • A spacecraft bus that supports one or more science instruments • A ground-based radio telescope observatory • A computer that executes a model (e.g., numerical weather prediction; algal bloom formation, growth, and dispersion) • A geolocated database that stores historical information (e.g., seasonal hurricane formation locations; seasonal algal bloom population emergence locations)

14. Instrument Nodes Computing/Data Storage Nodes Science Instrument(s) Sensors Integrated Instrument Stand-alone Instrument Science Instrument(s) No Science Instrument Sensors Node Node Node Communications Fabric interface Communications Fabric interface Communications Fabric interface Science Instruments are “loosely coupled” to a node (e.g., a ground observatory linked via communications link to a remote ground data system) Science Instruments are “tightly coupled” to a node (e.g., a spacecraft bus) A computing node is not connected to any science instrument (e.g., a meteorological forecast model running on a computer system) Node Types

15. Node Concept & ConnectivityAnother view… Sensor 1 Sensor 2 Sensor n Sensor 1 Sensor 2 Sensor m Science Instrument & Sensors Optional Instrument with Sensors Optional Instrument with Sensors Node Platform Mechanical Power Thermal Node Control Instrument Data Processor Instrument Data Storage Comms Fabric I/F Node Platform Communications Fabric This node does not have a Science Instrument/Sensors

16. Terminology - Data • Loosely defined to mean any “string of bits”. • Data bits may represent • Raw sensor or science instrument data • Processed science data • Ancillary information required to perform science data processing • Node or instrument state data (e.g., spacecraft health and safety engineering telemetry data; instrument mode of operation; …) • Commands to the node and/or science instrument to change its operating state • Executable code (i.e., algorithms) to be executed by the node • Sensor web system state messages

17. Terminology - CommunicationsFabric • A communications infrastructure that permits nodes to transmit and receive data between one another • The scope of the communications fabric encompasses • The communications media (e.g., wired vs. wireless; optical vs. RF; baseband signal vs. modulated; etc) • The communication topology (e.g., ring, star, mesh, etc) • Communications fabric protocols

18. So...what “is” a sensor web? “Scientific progress consists in the development of new concepts.”Ernst Mayr – renowned 20th century evolutionary biologist • As is often the case with emerging concepts, there is presently no single, widely accepted definition. • A candidate definition: A Sensor Web is a distributed system of sensing nodes that are interconnected by a communications fabric and that functions as a single, highly coordinated, virtual instrument. It autonomously detects and dynamically reacts to events, measurements, and other information from constituent sensing nodes and from external nodes (e.g., predictive models) by modifying its observing state so as to optimize science information return.”

19. Science instrument photons Science instrument photons Sensor Web Conceptual Diagram Information Fusion/Synthesis Computing Node Database Node Instrument Node Instrument Node Communications Fabric Non-photon science instrument data (e.g., distance measurements from a laser rangefinder) Predictive Model Node (has no science instrument) Instrument Node • Notes: • The communications fabric is not meant to imply just one “network”, nor does it imply any particular medium (RF, wired, fiber optics), nor specific connectivity such as a ring network versus a fully-connected mesh topology. It simply means that nodes use the communications fabric to send and receive data to/from one another. • Some nodes are shown having science instruments whereas others do not have instruments. An example of a node with no instruments is a computer system that executes a numerical meteorological forecast model and that provides its results to one or more other nodes.

20. Sensor Web Components:Autonomous Dynamic Interactions Communications Fabric Sensor Nodes • In situ & remote sensing observations • Individual & collaborative eventdetection and phenomenon recognition • Notification of other nodes • Reaction & Response by nodes • Node reconfiguration • Temporal (e.g., measurement rate), spatial (e.g., new location, higher resolution, form new cluster), spectral (e.g., activate different band) Computing Nodes Data Stores e.g., Historical Information e.g., Predictive models, information synthesis, observational data assimilation

21. “Sensor-web enabled systems are uniquely capable of performing real-time analysis and decision making to autonomously execute complex adaptive observing strategies.” E. Torres-Martinez, M. Schoeberl, M. Kalb; June 2002 IGARSS Ability to “aggregate/synthesize science data by clustering, or some other local data aggregation methods, to generate global high-level interpretations” XEROX PARC CoSense Project Sensor webs will inextricably link in-situ & remotely sensed observations with model outputs and information repositories from geographically dispersed and disparate sources; not possible with stand-alone sensors. “Improve the performance of weather and climate predictive systems and extend useful range of forecasts.” IbidE. Torres-Martinez et al WET VERY DRY DRY Sensor Webs Access to Knowledge Advanced Sensors Information Synthesis WET WET DRY DRY VERY WET COLD WARM COLD Why are Sensor Webs Important?“In the future, the research models of today will be the application models of tomorrow… What kind of observing system will we need?”Dr. Mark Schoeberl, GSFC Science Data Processing Workshop February 2002 Graphic Credit: Earth Science Vision 2002 Access to Knowledge Dr. M. Schoeberl

22. Achieve science objectives unattainable using single nodes Phenomena that occupy a very large spatial domain - “Local data is too weak to form coherent global interpretation” Xerox PARC “CoSense” project Magnetosphere Multipoint, time synchronous observations Arrays of large effective aperture instruments Reduce system response time Monitor rapidly evolving, transient, or variable events/phenomena Conduct time constrained observations without a priori or having incomplete knowledge of conditions at observing time Conduct observations where communication times are too long for humans to make real- or near-real-time decisions Improve utilization of platform & instrument resources Why are Sensor Webs Important?“Ideas won't keep; something must be done about them.”Alfred North Whitehead The phenomena we observe are intrinsically dynamic … as must be the sensor web information systems that will enhance our ability to observe and better understand these phenomena.

23. DARPA: Dynamic Sensor Networks Collaborative Sensemaking of Distributed Sensor Data Simulating highly scalable routing protocols for 10,000 node sensor networks. Credit: DARPA “SensIT“ project Credit: XEROX PARC, DARPA A Groundswell of Sensor Web Research Multi-Resolution Data Fusion Duke University - SensIT Credit: NASA/JPL Sensor Web for In Situ Exploration of Gaseous Biosignatures UAV Research Berkeley SensIT

24. Sensor Web ResearchNASA/JPL A Sensor Web measuring biosignature gases to search for microorganisms living beneath the surface of a planet. • “A Sensor Web consists of intra-communicating, spatially-distributed sensor pods that are deployed to monitor and explore environments.” • “It is capable of automated reasoning for it can perform intelligent autonomous operations in uncertain environments, respond to changing environmental conditions, and carry out automated diagnosis and recovery.”Dr. Kevin Delin, JPL Sensor Web project leader • The "hopped" data is shared by all of the pods, allowing each one to know what is being collected elsewhere on the web.” Images Credit: NASA/JPL JPL Sensor Web “Pod”

25. Xerox/PARC: CoSense Project Using sensor collaboration to make sense of aggregate phenomena that are not local in time and space Leader-follower formations Clustering of enemy forces Track multiple maneuvering targets, without a priori knowledge of paths A data association problem Estimate target position versus time How many nodes are required? What are the impacts of node spacing? How to perform distributed analysis with 100s and 1000s of nodes For very large numbers of nodes How to perform (and perhaps optimize) cluster maintenance and node reconfiguration to ensure efficient node collaboration? Perimeter violation sensing monitor only events within a predefined area; ignore all others. Target tracking and “reasoning” Detecting a particular target signature implies the existence of another target signature Information directed sensor querying Select next sensor to query to maximize information return while minimizing latency & bandwidth consumption How do you infer the properties of a global set of targets vs. individual target properties? Sensor Web ResearchDARPA Sensor Information Technology Program

26. Sensoria Corporation Examining dynamic network assembly to build deterministic networks BAE and Sensoria Auto track a vehicle Create initial estimate of future velocity and location Coordinate all nodes to image the vehicle when it is in view Nodes share event detection info and tracking states Nodes contribute to improved initial tracking estimate Technologies Sensor signal processing Sensor signal/information fusion Seismic Acoustic Infrared Routing algorithm is insensitive to loss of nodes Image trigger estimators Sensor Web ResearchDARPA Sensor Information Technology Program

27. Sensor Web ResearchDARPA Sensor Information Technology Program • MU-Fashion • Multi- Resolution Data Fusion using Agent- Bearing Sensors In Hierarchically-Organized Networks • Duke Univ, LSU, Univ. of TN • Examining problems associated with: • Sensor data fusion • Multi-resolution, fault tolerant target detection & classification • Sensor deployment algorithms to optimize target detection and minimize communications bandwidth • Working with BBN on sensor power management • for devices that operate in multiple power states • Using RT-Linux

28. Sensor Web ResearchDARPA Sensor Information Technology Program • BBN • Large-scale, distributed, intelligent sensor networks • 10,000 nodes + • Use peer-to-peer communications protocols • Ad hoc mobile networks • Scalable architecture • supports numerous diverse and heterogeneous sensor types • XML Messaging Standards • allows sensors to communicate and share information • XML messaging standards • Java-based solution

29. Dynamic Sensor Networks - USC, UCLA, VA Tech Distributed Services for Self Organizing Sensor Networks - Auburn University “Provide services that enable distributed sensor software components to self-organize, adapt to changing requirements, react to network changes, relocate and survive sensor failures in a dynamic ad hoc network” Distributed Cognition through Semantic Information Fusion: Penn State Low bandwidth comms requires abstraction of data Platforms self organize into local neighborhoods and share local data Collaborative signal processing Sensor Web ResearchDARPA Sensor Information Technology Program

30. NASA/GSFCAutonomous Nano Technology Swarm: ANTS • An Artificial Intelligence Approach to Asteroid Belt Resource Exploration: Dr. Steve Curtis • Scientifically categorize all asteroids > 1 km in diameter • “Mission goals are achieved through emergent, collective behavior”. Dr. Steve Curtis • Very large numbers (“swarms”) of picospacecraft (~1kg) with wide variety of instruments • X-ray • Gamma ray • Magnetometer • IR/Vis/UV spectrometers • Swarm heuristics planner and distributed intelligence operations

31. Sensor Web Research at GSFCSensor Web Application Prototype (SWAP) • ESTO FY01 Funded Prototype Research • Meteorological application in collaboration with Dr. Marshall Shepard • Dynamic instrument collaboration for flash flood prediction • Automated response and reconfiguration of simulated NWS Doppler radar array • “Intelligent“ rain gauges automatically initiate a simulated Doppler radar mode change from “sweep“ to “sector scan“. weather simulator Simulated Doppler Radar Doppler Radar “Sector Scan” Command Embedded Processor

32. Solar Arrays Solar Arrays Jet Intake Jet Discharge Payload Tube Batteries Propulsion Tubes Sensor Web Research at GSFCAn Autonomous, collaborative in situ marine fleet observing system (OASIS) • Dynamically control fleet sampling strategy to observe a cold core eddy as it develops from the Gulf Stream and then sheds into the subtropical gyre. • Potential use in real-time data assimilation efforts • Harmful Algal Blooms • Monitor their growth, map boundary, conduct insitu measurements Sensor Suite: - Microsalinograph; Fluorometer; Radiometers - Wind Monitor; RH/Temperature Probe - Precision Barometer; GPS Power: Marine solar panels & marine batteries Iridium: 2-way real-time communications Control: Twin thrusters with internal rudder OBC: G&N and sensor control Planned capabilities: Grid mapping, dynamic surveying, and station-keeping. Dynamic Ocean Processes: Meanders, Frontal Instabilities,eddies

33. OASIS Platform Sensor WebGulf Stream Eddy Mapping and Nutrient Measurement Eddy Properties (e.g., boundary) Coordinate in situ and space based observations “Cold rings trap nutrient-rich water and transports nutrients and plankton into the relatively-barren Sargasso Sea” Credit: Gulf of Maine Aquarium home page Credit:

34. A large algal bloom……could a GSFC OASIS sensor web fleet have helped? Commercial fishermen along the Southwest Florida coast are reporting a massive dead zone that is almost devoid of marine life in an area of the Gulf of Mexico traditionally known as a rich fishing ground. They've dubbed it black water, and they're demanding that local, state and national government agencies find out what's causing it. Scientists who have heard of the phenomenon say they, too, need answers. "It's killed a lot of the bottom because recently a lot of little bottom plants are coming to the surface dead and rotten out in the Gulf," said Tim Daniels, 58, a Marathon Key fish-spotting pilot who has been flying over the Gulf for more than 20 years. Like Daniels, fishermen with decades on the water say they've often seen red tide but they've never seen anything like this — it doesn't have a foul smell, it isn't red tide and it isn't oil. They describe it as viscous and slimy water with what looks like spider webs in it. March 17, 2002 Credit: NASA Earth Observatory - SeaWiFS on Orbview-2

35. Another Potential Sensor Web ApplicationHuman Health and the Environment • Fixed and mobile sensors are placed in areas where humans cannot go or because it is too dangerous • e.g., Exxon-Valdez oil spill • Areas particularly vulnerable to oil spills • Mud flats: can be up to several kilometers wide in the Cook Inlet/Kenai Peninsula region • Can be dangerous environment for humans to work in • A potential sensor web solution? • A fleet of autonomous amphibious vehicles • Perform collaborative mapping and contaminant measurements on exposed tidal/mud flats Sediment plume resulting from oil cleanup

36. GSFC Sensor Web Concept Formulation Node Aggregation Platform ƒ(x) Platform Types & Taxonomy Science Data Processor GIS Terrain Database Orbit Determination Processor Science Data Archive Plan & Sched. Processor Platform Storage Processors Instrument Platform database ƒ(x) Computing/Data Storage Platforms Collector-Reactor Reactor-Processor Collector-Processor Passive Collector Collector-Reactors Active Collector Instrument Platform Collectors ƒ(x) Instrument Platforms Sensor Web Properties Cluster 1 Cluster 2 Cluster 1 Cluster 2 M(i) Cluster 2 Cluster 1 Clustering, Reconfiguration, & Reassignment • Developed candidate sensor web definition • Characterizing • Node taxonomy and properties • Node data reporting properties • Sensor web classes • Required sensor web architectural properties • Identifying • Science needs • Candidate science scenarios and applications • Developing • Prototypes • Models and simulations

37. In space A single or a Distributed Space System (DSS) mission Within Earth’s (or planet’s) atmosphere Aircraft (e.g., UAV), balloon, sounding rocket... On the Earth’s (or planet’s) surface Fixed and mobile nodes Weather station, autonomous land/water craft Beneath the planetary surface or “skin” Submarine, subsurface sensors Deterministic Node outputs information at a priori known times This does not necessarily mean a “fixed” time interval Triggered Node reports information only when a predefined event, condition, phenomenon, or specified node operating state is detected On-Demand Node reports information when requested by another node Node PropertiesLocation & Reporting Modes

38. Class 1 Nodes Collectors • A science data collector • Has n well defined distinct states (modes) of performing data measurements (i.e., n data collection modes) • Mode miof mnavailable modes may be selected but not modified • Transmits raw instrument data only • Is unable to receive, and therefore cannot react to, data that is transmitted by another node • Three Class 1 node types • Passive Collector • Active Collector • Collector-Processor

39. Input: collects raw science instrument measurements Data Processing: None One data collection mode Simply formats raw data for communications fabric transmission compatibility No science instrument state changes supported Output: raw science instrument measurements and instrument/node state data Node examples: Tipping bucket rain gauge Panchromatic, multispectral downlinked raw data stream with only one resolution, fixed FOV, etc. Class 1 Node The Passive Collector Instrument Node Raw Instrument and Node State Data Raw Sensor Data

40. Input: collects raw science instrument measurements Data Processing: Some Data collection mode changes are supported Science instrument mode (M) selection by the node using a simple function: M(i) Output: raw science instrument measurements and instrument/node state data Node examples: River gauge node reports water level (w) measurements at more frequent time (t) intervals when node’s M(i) detects increasing dw/dt Serves to maximize available comms bandwidth efficiency Send information that is most suitable to the sensed condition Spacecraft selects a sensor best suited to phenomena of interest (e.g., NIR vs. visible vs. UV) M(i) Raw Sensor Data Class 1 Node The Active Collector Instrument Node Raw Instrument and Node State Data

41. Input: collects raw science instrument measurements Data Processing: Fixed algorithm supported Instrument mode changes are supported as with Active Collector node Output: processed science instrument measurements and instrument/node state data Node examples: DMSP imager: records full resolution visible pixels and reduced resolution (averaged) IR pixels during day time passes and vice versa during nighttime passes Instrument Node Processed Instrument and Node State Data ƒ(x) M(i) Raw Sensor Data Raw Sensor Data Class 1 Node The Collector-Processor Instrument Node Processed Instrument and Node State Data ƒ(x)

42. Class 2 Nodes Reactors • Collect and react to data sent to it from a science instrument or from another node • e.g., water level gauge that can report data at one of N possible time intervals (1x per minute, 4x per minute, etc) • Instrument and/or node has more than one mode of operation (i.e., data collection and/or processing) • Two class 2 node types • Collector-Reactor • Reactor-Processor

43. Input: Collects raw science instrument measurements Collects data from other node(s) Data Processing: None Output: Reacts to data/commands from other nodes and outputs raw science instrument measurements Node examples: River gauge reports water level (w) measurements at time (t) intervals In contrast to the Active-Collector node, another node detects a dw/dt condition and commands a data collection rate change in the Collector-Reactor node Raw Instrument and Node State Data Instrument Node Raw Sensor Data Commands & Data Commands & Data Class 2 Node The Collector-Reactor

44. Input: Collects raw science instrument measurements Collects data from other nodes Data Processing: Reacts to input data/cmds and outputs processed data Output: Processed science data and node state data Node examples: Future GOES-x meteorological spacecraft Numerical forecast model (A Science Processor class node) forecasts severe storm and commands GOES-x spacecraft (A Reactor-Processor Node) to begin rapid scan high-resolution imaging mode Class 2 Node The Reactor-Processor Processed Instrument Data and Node State Data Instrument Node Raw Sensor Data ƒ(x) Commands & Data Commands & Data

45. Class 3 Node Processors & Data Storage • Are not connected to a science instrument • Input data from one or more other nodes • Processor nodes transform received data into one or more higher level products • Output stored data or processed science data products • meteorological forecast model results • remapped imagery • synthesized science data (e.g., multispectral imagery and SAR combined to produce 3D terrain map) • There may be many node types in this class • Science processor • Database, science archive... • Orbit and attitude processor • Scheduling and planning processor

46. Input: Collects data only from other nodes Output: Outputs processed science data Processed science data Model or simulation results Data Processing: Yes Node examples: A meteorological numerical forecast model A MHD model of the magnetosphere Class 3 Node The Science Processor Processed Data (Science, Model, Simulation) and Node State Data Node ƒ(x) Commands & Data

47. Input: Collects data only from other nodes Output: Outputs stored data Data Processing: Perhaps. (e.g., Autonomous Data Mining) Node examples: GIS map database Archived science data Historical model runs Results of Data Mining Class 3 Node The Data Storage Node Node Stored Data Commands & Data

48. Hierarchical • Centralized command and control • Perhaps decreasing functionality/capability at lowest nodes/levels • Ring • Store and Forward • Conducive to pipelined information processing = A Sensor Web Node Sensor Web Topologies • Fully connected mesh • Peer-to-peer • Distributed control • Equivalent functionality/capability at all nodes

49. Cluster 1 Cluster 2 Sensor Web Topologies - continued Master • Clustering • Local command and control and sensor web data collection within each cluster • Reports to a “Master” overall control, monitoring, and/or coordinator node • A “cluster” may be a formation flying mission of N spacecraft; or subgroups of robotic explorers

50. Master Sensor Web TopologiesReconfiguration – NodeAggregation Master And the sensor web “aggregates” into a new larger sensor web Sensor Web at time t1 New Platforms Are Added at time t2