슬라이드 1

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Wireless Sensor Networks

MOBICOM 2002 Tutorial (Deborah Estrin, Mani Srivastava, Akbar Sayeed) 2006.11.01

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

   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

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Part IV Sensor Network Protocols

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Introduction

 WSN protocols – Primary theme • • long-lived massively-distributed  Minimize duty cycle and communication – Adaptive MAC – Adaptive Topology – Routing

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MAC in Sensor Nets

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

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Identifying the Energy Consumers

 Major source of energy waste – Idle listening when no sensing events – Collisions – Control overhead – Overhearing

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Sensor-MAC(SMAC)

 Major components of S-MAC – Periodic listen and sleep – Collision avoidance – Overhearing avoidance – Message passing  Periodic listen and sleep listen sleep listen sleep – Turn off radio when sleeping – Reduce duty cycle to ~10% (200 ms on/2s off) – Increased latency for reduced energy

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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

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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?

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ASCENT : Adaptive Self-Configuring sEnsor Networks Topologies

 The nodes can be in

active

passive

state.

– Active nodes • forward data packets – Passive nodes •

do not

forward any packets but may sleep or collect network measurements.

Data Message Source Help Messages (a) Communication Hole Sink Neighbor Announcements Messages Source Sink Data Message Source Passive Neighbor Active Neighbor (b) Self-configuration transition (c) Final State Sink SNU INC lab.

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:

f 1

Data plane:

f 2

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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

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Data Centric Routing

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

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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

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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: 4 Minimum hop route: 3 Minimum energy route: 1 SNU INC lab.

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 SNU INC lab.

Sensor DB Front End Sensor DB Sensor DB Sensor DB Sensor DB 16

Sensor DB Architecture

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Part II Collaborative Signal Processing

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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

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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 Uniform space-time cells

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Time 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 to the manager node – Active cell). Detector outputs of active nodes are sent 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

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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

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Various Forms of CSP

 Single Node, Multiple Modality (SN, MM) – Simplest form of CSP: no communication burden • • Decision fusion Data fusion (higher computational burden)

2   Multiple Node, Single Modality (MN, SM) – Higher communication burden • • Decision fusion Data fusion (higher computational burden)

1 , 1 Manager

3 , 1 node 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

1 , 1

1 , 2

3 , 1

3 , 2

2 , 1

2 , 2 Manager node

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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

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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: e [ n ]  W k    0 1 x [ n  k ] 2 ~ N (  s ,  2 ) N (  n ,  2 ) Target present Target absent ( H 1 ) ( H 0 )  W is the detector window length Detector decision: e [ n ]   e [ n ]   Target present Target absent

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Single Measurement Classifier

M=3 classes

Event feature vector P (

|  1 ) P (

|  2 ) P (

|  3 ) Class likelihoods C(

)=2 Decision (max)

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Multiple Measurement Classifier Data Fusion

M=3 classes

  

x x

1 2   P (

|  1 ) P (

|  2 )

2 Event feature vectors from 2 measurements P (

|  3 ) Concatenated event feature vector Class likelihoods C(

)=3 Decision (max)

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Multiple Measurement Classifier – Soft Decision Fusion

P (

1 |  1 )

1 P (

1 |  2 ) Comb.

P (

1 |  3 ) Comb.

2 P (

2 |  1 ) P (

2 |  2 ) Comb.

)=1 Component decision combiner Final Decision (max) Event feature vectors from 2 measurements P (

2 |  3 )

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Multiple Measurement Classifier – Hard Decision Fusion

M=3 classes

1 C 1 (

1 ) 1

2 3 C(

)=1 C 2 (

2 ) Majority vote 1

3 C 3 (

3 ) Final decision Event feature vectors from 3 measurements Component hard decisions

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Summary

 WSN protocols – MAC – Routing  WSN CSP – Data Fusion – Decision Fusion

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슬라이드 1

Transcript 슬라이드 1

Wireless Sensor Networks

Contents

Part IV Sensor Network Protocols

Introduction

MAC in Sensor Nets

Identifying the Energy Consumers

Sensor-MAC(SMAC)

SMAC - Collision Avoidance

Adaptive Topology

ASCENT : Adaptive Self-Configuring sEnsor Networks Topologies

STEM : Sparse Topology and Energy Management

Routing

Data Centric Routing

Directed Diffusion

Energy Efficient Routing

Database Centric Approach

Sensor DB Architecture

Part II Collaborative Signal Processing

Introduction

Space-Time sampling

Single Target Tracking

Why CSP?

Various Forms of CSP

Event Detection

Constant False Alarm Rate (CFAR) Detection

Single Measurement Classifier

Multiple Measurement Classifier Data Fusion

Multiple Measurement Classifier – Soft Decision Fusion

Multiple Measurement Classifier – Hard Decision Fusion

Summary

Directory