Wireless Sensor NetworksWireless Sensor Networks
2006.11.01Young Myoung,Kang (INC lab)([email protected])
MOBICOM 2002 Tutorial(Deborah Estrin, Mani Srivastava, Akbar Sayeed)
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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
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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
– Turn off radio when sleeping– Reduce duty cycle to ~10% (200 ms on/2s off)– Increased latency for reduced energy
sleeplisten listen sleep
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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
(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.
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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
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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: 4Minimum hop route: 3Minimum energy route: 1
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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
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Sensor DB Architecture
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Part IICollaborative 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
Sp
ace
TimeS
pace
Uniform space-time cells Non-uniform space-time cells
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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
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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)
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
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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:
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
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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)
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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
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Multiple Measurement Classifier – Soft Decision Fusion
)(P 11 |x
)(P 21 |x
)(P 31 |x C(x)=1
Event featurevectors from 2 measurements
FinalDecision(max)
1x
2x)(P 12 |x
)(P 22 |x
)(P 32 |x
Comb.
Comb.
Comb.
Componentdecision combiner
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Multiple Measurement Classifier – Hard Decision Fusion
C(x)=1
Event featurevectors from 3 measurements
Finaldecision
1x
2x
)(C 11 x
)(C 22 x
)(C 33 x3x
Majority vote
1
3
1
M=3 classes
Component hard decisions
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Summary
WSN protocols– MAC– Routing
WSN CSP– Data Fusion– Decision Fusion