© Neeraj SuriEU-NSF ICT March 2006
Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de
MWM: Map-based World Model for Wireless Sensor Networks
Abdelmajid Khelil, Faisal Karim, Brahim Ayari, Neeraj Suri
AUTONOMICS’ 08, Turin, Italy
© A. Khelil 2
Raw dataRaw data
Wireless Sensor Networks (WSN): Bridge to Physical World
World model@
Sink
Updatemodel Query
model
Createmodel
Changeworld
Physical world
World model@
NetworkSensornetwork
Users, Admins..
Deploy wireless battery-powered nodes with temperature sensors
Sensor nodes:If (avg)temp > threshold
report fireElse: no report
Raw data
Sink
User info.
Sink:If report(s) received
fire notify userElse: no fire
Exam
ple
: D
ete
ct
fore
st
fire
s
Event Query
Alarm
.. Independent from raw data, application and users!
How to convert raw data into information?
App.info.
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Three Main System-level Design Paradigms
WSN as Network Inherent node
redundancy Convergecast, filtering Limited resources Cross-layer
WSN as Database Query dissemination In-network aggregation E.g. tinyDB
WSN as Event Service Nodes provide/consume
services E.g. pub/sub
Query
Result
Node ID
tem
p
en
er
gy
1 7° 60%..
N 5° 20%
WSN
These paradigms still address single sensor nodes and ignore spatial correlation of sensor readings less accepted
Abstraction level
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Problem Statement and Objectives
Widely Accepted Abstraction Level is Needed How to convert sensor data into information which is:
Understandable, contextual, interactive and actionable.
Abstraction Should Consider Inherent spatial correlation of sensor readings (Inherent node
redundancy in WSN)
Requirements Generalized Unified incorporation of
• Physical world and Physical world and • Network worldNetwork world
Frugal and lightweight (creation, management etc.)
Our Approach: Map-based World Model (MWM)
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Outline
System Model Map-based World Model Design Methodology Two Case Studies
Detecting and predicting fires Predicting network partitioning
Related Work Conclusions
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System Model
Nodes Large number of static resource-limited sensor nodes (SNs):
Motes.. A few static powerful sinks A few mobile resource-moderate assist nodes (ANs): PDAs,
robots..
Nodes Know their Own Geographic Position
Clocks are Synchronized
Nodes Functionality SNs create the model ANs manage the model Sinks represent operator(s)
SN
AN
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The MWM Approach
Appropriately Group Spatially-Correlated Readings into Regions and Maps
Maps Natural way to represent the
physical world (spatio-temporal data)
Efficient techniques exist
MWM: A Set of Relevant Maps User maps (uMAP), e.g.,
temperature map Network maps (nMAP), e.g.,
map of residual energy
Region border nodes
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Existing Map Construction Algorithms
The eScan Approach [1] Map-construction along the aggregation-
tree Map is partial at SNs & complete at sink Data with low time validity (chemicals
etc.)
The Isoline Approach [2] Local flood to label border nodes Map is partial at SNs & complete at sink Data with low time validity
The gMAP Approach [3] AN collects data and construct map Map at AN Data with high time validity (energy etc.)
[1] Y. Zhao et al. Residual Energy Scan for Monitoring Sensor Networks. In IEEE WCNC, 2002.[2] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005.[3] A. Khelil et al. gMAP: An Efficient Construction of Global Maps for Mobility-Assisted WSN, TR, 2007.
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The MWM Architecture
Main Idea: Address Regions Instead of Nodes
Architecture Retains Existing Abstractions Substitute node by a region
• TinyDB (database)TinyDB (database)• Pub/sub (service)Pub/sub (service)• Cross layer (network)Cross layer (network)
Architecture Simplifies Design of application, Design of network Etc.
uMAPsuMAPs
Sensor data comm. (geographic routing,broadcast, geocast, convergecast, directed
diffusion, in-netw-aggr. etc.)
Eventservice
Loca
tion,
Tim
e
Applications (e.g. predictive world and network monitoring)
InterestNotification,prediction
Eventspecification
Notification,prediction
Mapconstruction
Queryservice
Query Result
Query Result
uMAPs
Pub/subtinyDB
uMAPsuMAPsnMAPsMWM MWM Mgmt
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Queries and Events in MWM
Queries SQL-like language, query regions
instead of sensor nodes Example:
SELECT region, temp FROM tempMAP WHERE temp > threshold Trade-offs:
• Centralized vs. decentralized MWM• Pro-active vs. reactive regioning• Query dissemination [1]
Events Event: Predicate P(attr1, .. attrk), attri
of mapk, e.g., attr1 > th1
Event composition ≡ geometric operation, e.g., attr1 > th1 & attr2 > th2 attr1 > th1 attr2 > th2
[1] R. Sarkar et al. Iso-Contour Queries and Gradient Routing with Guaranteed Delivery in Sensor Networks. infocom’08.
event
event
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MWM-based WSN Design Methodology
(Geometric) abstraction level acceptable by users, application designers and network developers Simplifies requirement engineering, debugging, standardization etc.
Step 1: Identify situations and events of interest (Geometric)
Step 2: Identify the required maps (MWM) and define events and their operations in MWM (Geometric)
Step 3: Sketch a solution assuming global MWM (Geometric)
Step 4: Distribute the required MWM knowledge on nodes (Geometric)
Step 5: Select requisite communication primitives
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Case Study 1: Detecting and Predicting Fires
Step 1: Fire and pre-fire regionsStep 2: Temperature map.Step 3: Fire-temp threshold,
pre-fire-temp threshold, regions report to sink
Step 4: Border nodes report position and temp value
Step 5: Local flood for isoline construction. Each border node unicasts to sink Sink
Border nodes ofhigh temperature
regions
Isomap@sink
WSN
fire
fire
Existing techniques [1][2] do not Provide for prediction Deliver fire perimeter
[1] M. Hefeeda et al. Wireless Sensor Networks for Early Detection of Forest Fires. In MASS, 2007.[2] D.M. Doolin et al. Wireless Sensors for Wildfire Monitoring. In SPIE, 2005.
(Not all sensor nodes are illustrated)
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Case Study 2: Predicting Network Partitioning
Step 1: Predict coverage drops and isolated regions
Step 2: Starting with connected network we require the residual energy map
Step 3: Regions of weak energy should report to sink; Sink predicts partitioning
Step 4: Border nodes report position and energy value
Step 5: Local flood for isoline construction; Each border node unicasts to sink
Existing techniques [1][2] do not Provide for prediction Provide important details (partition shape etc.) Support all shapes/types of partitions
[1] N. Shrivastava et al. Detecting Cuts in Sensor Networks. In IPSN, 2005.[2] K.P. Shih et al. PALM: A Partition Avoidance Lazy Movement Protocol for Mobile Sensor Networks. In WCNC, 2007.
Sink
Border nodes ofenergy weak
regions
Isomap@sink
WSN
(Not all sensor nodes are illustrated)
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Predictive Monitoring and Pro-active Reconfiguration
Predictive Monitoring of both Physical and Network Worlds Combine (map) data from spatial and temporal domains Event prediction
Pro-active Network Reconfiguration Examples: Node displacement
• To provide self-healing and graceful degradationTo provide self-healing and graceful degradation- E.g., by delaying network partition
MWM simplifies • Spatial interventionSpatial intervention• Event-triggered autonomous reconfigurationEvent-triggered autonomous reconfiguration
Predictability and pro-activeness enhance system autonomicity
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Related Work
Modeling Technique in WSN Network models, simulation models etc.: Complex and domain-
specific Geographic Information Systems (GIS) and spatial temporal
databases Modeling languages: SensorML, REACTIVEML and LUSSENSOR
MWM specification
Existing Real World Models Context-awareness models: Complex, rely on powerful
infrastructure, and involve user. Sentient computing: Focus on indoor scenarios Augmented and virtual reality models Real-world models in autonomic computing
All models are „embedded“ in the infrastructure ; We argue for a model distribution
All models dynamically involve the user
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Conclusions
The Ongoing Evolution of the Web Map Interoperability/standardization between
WSNs: SensorWeb, SensorGrid etc. Enhances autonomicity of sensing and
reacting
Implementation in OMNET++ simulator
Maps Provide a Widely Accepted Abstraction We Developed Map-based System Architecture for WSNs Unified Model for Both Physical and Network Worlds Powerful Tool for Both Design and Deployment
A novel design methodology Two case studies
WSN 4
WSN 4
Queriesevents
Geogra
phic m
ap
WSN 2
WSN 2 WSN
3
WSN 3
WSN 1
WSN 1
© Neeraj SuriEU-NSF ICT March 2006
Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de
Thanks for your attention!
Abdelmajid Khelil, Faisal Karim Shaikh, Brahim Ayari, Neeraj Suri
Department of Computer ScienceTU Darmstadt, Germany
{khelil, fkarim, brahim, suri}@informatik.tu-darmstadt.de