Wireless Sensor Networks 2006.11.01 Young Myoung,Kang (INC lab) ([email protected]) MOBICOM 2002 Tutorial (Deborah Estrin, Mani Srivastava, Akbar

Wireless Sensor NetworksWireless Sensor Networks

2006.11.01Young Myoung,Kang (INC lab)([email protected])

MOBICOM 2002 Tutorial(Deborah Estrin, Mani Srivastava, Akbar Sayeed)

SNU INC lab.SNU INC lab. 2

Contents

Part I : Introduction Part II : Sensor Node Platforms & Energy Issues Part III: Time & Space Problems in Sensor

Networks

Part IV: Sensor Network Protocols Part V : Collaborative Signal Processing


Part IV Sensor Network Protocols


Introduction

WSN protocols – Primary theme

• long-lived

• massively-distributed

Minimize duty cycle and communication– Adaptive MAC

– Adaptive Topology

– Routing


MAC in Sensor Nets

Important attributes of MAC protocols– Energy efficiency– Collision avoidance– Scalability in node density– Latency– Fairness– Throughput– Bandwidth utilization


Identifying the Energy Consumers Major source of energy waste

– Idle listening when no sensing events– Collisions – Control overhead– Overhearing


Sensor-MAC(SMAC)

Major components of S-MAC– Periodic listen and sleep– Collision avoidance– Overhearing avoidance– Message passing

Periodic listen and sleep

– Turn off radio when sleeping– Reduce duty cycle to ~10% (200 ms on/2s off)– Increased latency for reduced energy

sleeplisten listen sleep


SMAC - Collision Avoidance

Collision Avoidance– Problem:

• Multiple senders want to talk

– Solution: Similar to IEEE 802.11 ad hoc mode (DCF)• Physical and virtual carrier sense• Randomized backoff time• RTS/CTS for hidden terminal problem• RTS/CTS/DATA/ACK sequence


Adaptive Topology Goal:

– Exploit high density (over) deployment to extend system lifetime – Provide topology that adapts to the application needs– Self-configuring system that adapts to environment

How many nodes to activate?


ASCENT : Adaptive Self-Configuring sEnsor Networks Topologies

(b) Self-configuration transition(a) Communication Hole (c) Final State

Help Messages

Data Message

SinkSource SinkSource

Neighbor AnnouncementsMessages

Data Message

SinkSource

Active NeighborPassive Neighbor

The nodes can be in active or passive state.– Active nodes

• forward data packets

– Passive nodes• do not forward any packets but may sleep or collect network

measurements.


STEM : Sparse Topology and Energy Management

Major Concept– Need to separate Wakeup and Data Forwarding Planes– Chosen two separate radios for the two planes– Use separate radio for the paging channel to avoid

interference with regular data forwarding– Trades off energy savings for path setup latency

Wakeup plane: f1

Data plane: f2


Routing Goal

– To disseminate data from sensor nodes to the sink node in energy-awareness manner, hence, maximize the lifetime of the sensor networks.

Problem Description– Given a topology, how to route data?– Traditional Ad hoc routing protocols doesn’t fit

Classification of Routing Protocols– Data Centric Protocols

• SPIN , Directed Diffusion– Hierarchical Protocols

• LEACH , TEEN– Location Based Protocols

• GAF , GEAR


Data Centric Routing

The ability to query a set of sensor nodes Attribute-based naming Data aggregation during relaying


Directed Diffusion

Sink node floods named “interest” with larger update interval

Sensor node sends back data via “gradients” Sink node then sends the same “interest” with smaller

update interval Query-driven


Energy Efficient Routing Possible Route

• Route 1: Sink-A-B-T, total PA = 4, total α = 3

• Route 2: Sink-A-B-C-T, total PA = 6, total α = 6

• Route 3: Sink-D-T, total PA = 3, total α = 4

• Route 4: Sink-E-F-T, total PA = 5, total α = 6

Maximum PA route: 4Minimum hop route: 3Minimum energy route: 1


Database Centric Approach

Traditional Approach– Data is extracted from sensors and stored on a front-end

server– Query processing takes place on the front-end

Sensor Database System– Distributed query processing over a sensor network

Warehouse

Front End

SensorDB

SensorDB

Front End

SensorDB

SensorDB

SensorDB


Sensor DB Architecture


Part IICollaborative Signal Processing


Introduction

Sensor Network from SP perspective– Provide a virtual map of the physical world:

• Monitoring a region in a variety of sensing modalities• (acoustic, seismic, thermal, …)

Two key components:– Networking and routing of information– Collaborative signal processing (CSP) for extracting and

processing information from the physical world


Space-Time sampling

Sensors sample the spatial signal field in a particular modality (e.g., acoustic,seismic)

Sensor field decomposed into space-time cells to enable distributed signal processing (multiple nodes per cell)

Time

Sp

ace

TimeS

pace

Uniform space-time cells Non-uniform space-time cells


Single Target Tracking

Initialization: Cells A,B,C and D are put on detection alert for a specified period

Five-step procedure:

1. A track is initiated when a target is detected in a cell (Cell A – Active cell). Detector outputs of active nodes are sent to the manager node

2. Manager node estimates target location at N successive time instants using outputs of active nodes in Cell A.

3. Target locations are used to predict target location at M<N future time instants

4.Predicted positions are used to create new cells that are put on detection alert

5.Once a new cell detects the target it becomes the active cell


Why CSP? More information about a phenomenon can be

gathered from multiple measurements– Multiple sensing modalities (acoustic, seismic, etc.)– Multiple nodes

Limited local information gathered by a single node – Inconsistencies between measurements– malfunctioning nodes

Variability in signal characteristics and environmental conditions– Complementary information from multiple

measurements can improve performance


Various Forms of CSP Single Node, Multiple Modality (SN, MM)

– Simplest form of CSP: no communication burden• Decision fusion• Data fusion (higher computational burden)

Multiple Node, Single Modality (MN, SM)– Higher communication burden

• Decision fusion • Data fusion (higher computational burden)

Multiple Node, Multiple Modality (MN, MM)– Highest communication and computational burden

• Decision fusion across modalities and nodes• Data fusion across modalities, decision fusion across nodes• Data fusion across modalities and nodes

1x 2x

1,1x

Managernode

1,2x

1,3x

Manager node

1,1x 2,1x1,2x 2,2x

1,3x2,3x


Event Detection

Simple energy detector– Detect a target/event when the output exceeds an adaptive

threshold (CFAR) Detector output:

– At any instant is the average energy in a certain window – Is sampled at a certain rate based on a priori estimate of

target velocity and signal bandwidth Output parameters for each event:

– max value (CPA – closest point of approach) – time stamps for: onset, max, offset– time series for classification

Multi-node and multi-modality collaboration


Constant False Alarm Rate (CFAR) Detection Energy detector is designed to maintain a CFAR Detector threshold is adapted to the statistics of

the decision variable under noise hypothesis Let x[n] denote a sensor time series Energy detector:

W is the detector window length Detector decision:

),(N

),(N~]kn[x]n[e

2n

2s

1W

0k

2 Target present

Target absent

]n[e

]n[e Target present

Target absent

)H( 1

)H( 0


Single Measurement Classifier

)(P 1|x

)(P 2|x

)(P 3|x

x C(x)=2

M=3 classes

Event featurevector

Class likelihoods Decision(max)


Multiple Measurement ClassifierData Fusion

)(P 1|x

)(P 2|x

)(P 3|x

C(x)=3

M=3 classes

Event featurevectors from 2 measurements

Class likelihoods Decision(max)

1x

2x

2

1

x

xx

Concatenated event feature vector


Multiple Measurement Classifier – Soft Decision Fusion

)(P 11 |x

)(P 21 |x

)(P 31 |x C(x)=1


FinalDecision(max)

1x

2x)(P 12 |x

)(P 22 |x

)(P 32 |x

Comb.

Comb.

Comb.

Componentdecision combiner


Multiple Measurement Classifier – Hard Decision Fusion

C(x)=1


Finaldecision

1x

2x

)(C 11 x

)(C 22 x

)(C 33 x3x

Majority vote

1

3

1

M=3 classes

Component hard decisions


Summary

WSN protocols– MAC– Routing

WSN CSP– Data Fusion– Decision Fusion

Documents

Wireless Sensor Networks 2006.11.01 Young Myoung,Kang (INC lab) ([email protected]) MOBICOM 2002 Tutorial (Deborah Estrin, Mani Srivastava, Akbar