© 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.

© A. Khelil 3

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)

© A. Khelil 5

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

© A. Khelil 7

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

© A. Khelil 8

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

© A. Khelil 10

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

© A. Khelil 12

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