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Towards A Holistic Approach for System Design in Sensor Networks

Chair: Prof. Nael Abu-Ghazaleh

Committee Members: Prof. Kenneth ChiuDr. Tony FountainProf. Wendi HeinzelmanProf. Kyoung-Don KangProf. Michael Lewis

Sameer Tilak

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Outline

WSN Applications and Challenges Summary of Contributions Holistic Principle WSN critical Subsystems and Services Holistic Framework Architecture Abstractions and Virtualization Conclusion and Future Work

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Enablers – Micro-sensors

Small (coin->matchbox->PDA range) Limited resources

Battery operated Embedded processor (8-bit to PDA-class

processor) Memory: Kbytes—Mbytes range Radio: (Kbps – Mbps; often small range) Storage (none to a few Mbits)

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Sensor Network Applications

EmbedEmbed numerous distributed devices to monitor and interact with physical world: hospitals, homes, vehicles, and “the environment”

Network these devices so that they can coordinate to perform higher-level tasks.Requires robust distributed systems of hundreds or thousands of devices.

Disaster Response

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Sensor Node Specific Challenges

Low Battery power Low bandwidth Error-prone air medium Low computing power and memory Heterogeneous software and

Hardware architectures

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Sensor Network Challenges

Large-scale fine-grained heterogeneous sensing 100s to 1000s of nodes providing high resolution Spaced a few feet to 10s of meters apart

Collaborative Each sensor has a limited view

Spatially In terms of sensed data type

Distributed Communication is expensive Localized decisions and data fusion necessary

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Summary of Contributions

WSN critical subsystems and services Information dissemination Storage Management Localization

Holistic Framework Abstraction and virtualization

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Holistic Principle Data centric, resource constrained operation

effective operation requires careful balancing of application level utility and cost  (Principle 1) 

 Communication is expensive Localized interactions -- produce local estimates

of utility and cost (Principle 2) Local estimates of application level utility

and cost can significantly differ from actual value observed globally Information available globally that significantly

impacts local estimates are called context Identify and track context when it is worth it to do

so (Principle 3)

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Non-Uniform Information Dissemination

WSN Critical Subsystems/Services

ICNP 2003, NCA 2004, NCA 2005, TR

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Non-Uniform Information Dissemination

Loss in precision as a function of distance is acceptable

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Intuition Forward packets less aggressively the further

away you are from the event deterministically: e.g., forward every nth packet, n

increase with distance probabilistically: e.g., forward packets with a

probability thatdrops with distance from event

Here, the context is spatial and can be efficiently tracked by tracking the distance from the source on the event packet

Significant energy saving results, while keeping information accurate close to the event source

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Randomized Protocols

Biased protocol: Packet forwarding decisions are

based on a coin toss and relative distance from source.

Forwarding probability is higher for physically closer sources. (Context)

Simple, low overhead and very scalable

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Weighted Energy-Error Study

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High-level concluding remarks

Energy-efficient light-weight protocols that capitalize on non-uniform information granularity

Context embedded in the form of TTL (distance from an event source).

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WSN Critical Subsystems/Services

Storage Management

IJAHUC 2005, Wiley Book chapter 2005, TR

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Intuition

Exploit spatio-temporal redundancy

Coordinate for redundancy control Context is Spatial

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Candidate Protocols

Local Storage Local-Buffer

Clustering CBCS: Aggregate and Store and Cluster

Head Context:

CLS: Coordinate and store locally CCS: Combined CBCS + CLS

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Clustering

• Only CH stores data

• Rotate CH

• Distributed storage, medium fault-tolerance

• Spatial aggregation possible

Round 1 (time = 0)Round 2 (time = 20)

Round 1 (time = 40)

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CCS

Round 1 (time = 0)

Round 1 (time = 40)

Feedback

Data

CH

Feedback provides context for data utility

CH has more global view than individual sensor

Samples

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Storage and Energy Study

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High-level concluding remarks

Collaborative protocols are scalable, light-weight, does load balancing and increase storage lifetime

Context provided in terms of feedback for redundancy control

Context in space

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Localization

IEEE IWSEEASN 2005

WSN Critical Subsystems/Services

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Mobile Sensor Localization

Localization: Determine physical coordinates of a given sensor

Existing research considers static sensor nets.

Mobile sensors Energy versus accuracy trade-offsProtocols

SFR (Static Fixed Rate) DVM (Dynamic Velocity Monotonic) MADRD (Mobility Aware Dead Reckoning Driven)

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Existing Research on Localization

Assumes Static Sensor Network Focus on How to Carry Localization and not

When Range/Direction Based

Calculate distance from anchors and triangulate Received Signal Strength (e.g. RADAR) Time of Arrival (e.g. GPS) Time Difference of Arrival (Cricket, Bat) ProximityBased

Centroid ATIP DV HOPS MDS

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Motivation

What about Mobile Sensor Networks ?Interesting Energy-Accuracy trade off !

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Problem Definition

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SFR Localize every t seconds Very simple to implement Once Localize tag data with those

coordinates till next localization Energy expenditure independent of

Mobility Performance varies with Mobility Existing Projects such as Zebranet use

this approach (3 minutes).

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DVM Adaptive Protocol Sensor Adapts its localization frequency to

Mobility Goal maintain error under application-specific

tolerance Compute current velocity and use it to decide

next localization period Once Localize tag data with those coordinates

till next localization Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

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MADRD Predictive Protocol Estimate mobility pattern and use it to predict

future localization Localization triggered when actual mobility and

predicted mobility differs by application-specific tolerance

Tag data with predicted coordinates (differs from SFR and DVM)

Changes in mobility model affect the performance

Upper and Lower query threshold Energy expenditure varies with Mobility Performance almost invariant of Mobility

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MADRD State Diagram

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High-level Summary of Analysis

Error in non-predictive protocols increase with any mobility that moves the node away from its last localization point

Error in Predictive protocols increase only when the predictive

Model is inaccurate Model estimation in incorrect Model changes (pause, direction change)

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Medium Speed (4-5 m/s)

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Error versus Pause time

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Summary

Studied energy versus accuracy tradeoff in localization for mobile sensors

DVM and MARD are completely distributed scalable protocols DVM and MADRD outperform SFR Context Temporal

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Resource Management

BW Storage

Energy

Context

Application

Local Resources

•Non-Local resources•Data utility•(Principle 3)

HOLISTIC APPROACH

Operation Management Data Samples

Local utility estimate

Principle 1

Principle 2

Initial Idea

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Context

Localized decisions leads to scalability Local estimate of utility of data can

differ from global measurement Absence of relevant global knowledge:

Context Feedback can be used to improve

accuracy of local estimate

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Patterns & Types Context

Patterns Context in time Context in space Application-specific (domain

knowledge) Resource related (src-dest path)

Types Utility context Cost context

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Research Challenges

Data Utility Assessment Resource Cost Assignment Utility-Cost Normalization Tracking, Building, and Maintaining

Context Middleware

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Holistic Framework Architecture

Point in design space

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Holistic Framework Components

Benefit Estimator Cost Calculator Planner

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Benefit Estimator

Data Significance Data scope in Time, Space App-specific (observer interest)

Data Quality Data Freshness, accuracy, resolution, App-specific measures

Output Benefit Vector (BV) MAUT

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Cost Calculator

Sensing, Transmission, Storage, Computational, Reception.

Output Cost Vector (CV): Vector of pre-determined transformations

MAUT

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Planner

Rule based engine. Input BV, CV Incorporates application state Objective: Maximize Benefit/Cost

ratio

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Instance of a Planner Algorithm

IF (Utility == HIGH) {

IF(STATE== NEED_LOCALIZATION) LOCALIZE AND THEN TRANSMIT}If (Utility == MEDIUM)

TransmitIf (Utility == MEDIUM)

Low drop.

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Component Placement Trade-offs

Middleware versus Application Benefit Estimator?

Application-specific knowledge Cost Calculator?

App/infrastructure communication Resource cost in middleware

Planner? Single versus multiple apps

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Abstraction

EESR 2005

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Related Work

Database Abstraction Programming abstractions e.g.

Nesc TinyOS Plan-9, Inferno

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File System Abstraction

Treat entire sensor network as a distributed file system

Application-specific namespaces Well understood interface Heterogeneity Applications get fine grained

control over resources

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Application-specific Namespaces

Making Abstractions efficient

Default resource namespace

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Applications

Monitoring and Calibration Debugging Sense & Respond Data Centric Application etc.Sample Usagesmount /dev/network /networkls /network/cluster1/sensors/cat /network/cluster1/s1/remaining-energyecho 2.5 > /network/cluster1/s1/control

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query /network/cluster1/Location

Query Execution Scenario

• Fine-grained resource control

• Exposes cost and utility explicitly

• Optimized query planner (Below DB)

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Sense & Respond System

Heterogeneity

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Abstraction & Virtualization

WSN Virtualization

NCUS 2005

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Virtual Sensor Networks

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Micro-sensor Hardware

Berkeley Motes (Mica2) Pasta nodes Mantis-nymph WINS

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Software Architectures Operating Systems

TinyOS Linux Windows CE MOS

Programming Languages C nesC JAVA

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Motivation for Resource Discovery Middleware

Mobile and Ad-Hoc sensor networks Rescue operations Battlefield scenarios

Seamless Integrating of sensors to Grids

Sensors control, configuration (remote)

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Mobile Sensor Network

Ad-Hoc Network

Self-Configuration

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Service Discovery Protocols

Scalable to thousands to sensors Energy-efficient Standardization Design Space

Proactive versus reactive Distributed versus centralized Tuned for Sensor network characteristics

Minimize Transmission and reception of messages Sensors have low duty cycle radios

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Our Approach

Register Message

Query Message

Resource Info. message

Cluster-Head

Resource registry

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XML versus proprietary format

<id>10</id> 10<precision>100</precision> 100<minrange>-40</minrange> -40<maxrange>100</maxrange> 100<capacity>-1</capacity> -1

Describing resource in XML consumes 10 times more power than proprietary binary message

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Message Formats

Power efficient

Communication overhead

Standardization

Proprietary format

H L L

XML L H H

XML with compression

M-H M-H H

Binary-XML M-H L- M H

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Conclusions

Application-specific, light-weight, energy-efficient protocols for critical services and subsystems

Holistic principle and framework Abstraction and virtualization

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Future Work

Holistic Framework VSN ICTs for developing regions

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Questions ???

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Thank You

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Direction Change

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Analysis of the Proposed Protocols

Constant Velocity model

SFR and DVM error increases linearly

MADRD estimates location precisely (no error)

Constant Velocity + pause

SFR and DVM error increasely linearly and stays there

MADRD has 0 initial error and then it increases linearly

Contant Vecloty + change in direction

SFR: performs better if the turn is towards the prev

localization point (turn 90deg to 270 deg)

MADRD: otherwise performs better

(DOES NOT increase/decrease linearly)

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Direction Change