MWM: Map-based World Model for Wireless Sensor
Networks
Abdelmajid Khelil, Faisal Karim, Brahim Ayari,
Neeraj Suri
AUTONOMICS’ 08, Turin, Italy
Dependable Embedded Systems & SW Group
© Neeraj Suri
www.deeds.informatik.tu-darmstadt.de
EU-NSF ICT March 2006
Wireless Sensor Networks (WSN): Bridge to Physical World
Example: Detect forest fires
Alarm
Sink:
If report(s) received
 fire  notify user
Else: no fire
Users,
Admins..
Sink
How to convert
raw data into
information?
Sensor nodes:
If (avg)temp > threshold
report fire
Else: no report
App.
info.
Deploy wireless
battery-powered nodes
with temperature sensors
2
Event
Query
World model
@
Sink
Update
model
Query
model
World model
@
Network
Sensor
network
Physical
world
.. Independent from raw data, application and users!
© A. Khelil
User
info.
Create
model
Raw
Raw
Raw
data
data
data
Change
world
Three Main System-level Design Paradigms
 WSN as Network
 Inherent node
redundancy
 Convergecast, filtering
 Limited resources
 Cross-layer
1
energy
Query
7°
60%
5°
20%
..
 Query dissemination
 In-network aggregation
 E.g. tinyDB
Node
ID
temp
 WSN as Database
WSN
N
Result
 WSN as Event Service
 Nodes provide/consume
services
 E.g. pub/sub
Abstraction level
 These paradigms still address single sensor nodes and ignore
spatial correlation of sensor readings  less accepted
© A. Khelil
3
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
• Network world
 Frugal and lightweight (creation, management etc.)
 Our Approach: Map-based World Model (MWM)
© A. Khelil
4
Outline




System Model
Map-based World Model
Design Methodology
Two Case Studies
 Detecting and predicting fires
 Predicting network partitioning
 Related Work
 Conclusions
© A. Khelil
5
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
SN
 Nodes Functionality
 SNs create the model
 ANs manage the model
 Sinks represent operator(s)
© A. Khelil
AN
6
The MWM Approach
 Appropriately Group SpatiallyCorrelated Readings into
Regions and Maps
Region
border
nodes
 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
© A. Khelil
7
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.
© A. Khelil
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The MWM Architecture
 Main Idea: Address Regions
Instead of Nodes
 Substitute node by a region
• TinyDB (database)
• Pub/sub (service)
• Cross layer (network)
Query
Query
service
Query
tinyDB
 Design of application,
 Design of network
 Etc.
Notification,
prediction
Event
service
Event
specification
Result
uMAPs
uMAPs
uMAPs
 Architecture Simplifies
© A. Khelil
Interest
Result
MWM
uMAPs
uMAPs
nMAPs
Map
construction
Notification,
prediction
MWM Mgmt
Pub/sub
Sensor data comm. (geographic routing,
broadcast, geocast, convergecast, directed
diffusion, in-netw-aggr. etc.)
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Location, Time
 Architecture Retains Existing
Abstractions
Applications (e.g. predictive world and network monitoring)
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]
event
 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
event
attr2 > th2
[1] R. Sarkar et al. Iso-Contour Queries and Gradient Routing with Guaranteed Delivery in Sensor Networks. infocom’08.
© A. Khelil
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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
© A. Khelil
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Case Study 1: Detecting and Predicting Fires
Step 1: Fire and pre-fire regions
Step 2: Temperature map.
Step 3: Fire-temp threshold, prefire-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
fire
Border nodes of
high temperature
regions
Isomap@sink
fire
Sink
WSN
(Not all sensor nodes are illustrated)
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.
© A. Khelil
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Case Study 2: Predicting Network Partitioning
Step 1: Predict coverage drops and
isolated regions
Border nodes of
energy weak
Step 2: Starting with connected
regions
network we require the residual
energy map
Step 3: Regions of weak energy
Isomap@sink
should report to sink; Sink
predicts partitioning
Step 4: Border nodes report position
and energy value
Step 5: Local flood for isoline
Sink
WSN
construction; Each border node
(Not all sensor nodes are illustrated)
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.
© A. Khelil
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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 degradation
- E.g., by delaying network partition
 MWM simplifies
• Spatial intervention
• Event-triggered autonomous reconfiguration
 Predictability and pro-activeness enhance system autonomicity
© A. Khelil
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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
© A. Khelil
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Conclusions




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
 The Ongoing Evolution of the Web Map
 Interoperability/standardization
between WSNs: SensorWeb,
SensorGrid etc.
 Enhances autonomicity of sensing and
reacting
Queries
 Implementation in OMNET++ simulator
© A. Khelil
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WSN
1
WSN
2
WSN
4
WSN
3
events
Thanks for your attention!
Abdelmajid Khelil, Faisal Karim Shaikh, Brahim Ayari,
Neeraj Suri
Department of Computer Science
TU Darmstadt, Germany
{khelil, fkarim, brahim, suri}@informatik.tu-darmstadt.de
Dependable Embedded Systems & SW Group
© Neeraj Suri
www.deeds.informatik.tu-darmstadt.de
EU-NSF ICT March 2006