Transcript ppt for sensor-networks-101

Quick Look at Sensor Networks
Elke A. Rundensteiner
Based on material collated by
Silvia Nittel, and others.
CS525
Overview – Sensor Networks
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Motivation & Applications
Platform & Power
Networking Underpinning
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Motivation
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Trends:
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Consequences:
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Developments of new sensor materials
Miniaturization of microelectronics
Wireless communication
Embedding devices into almost any man-made and some
natural devices, and
connecting the device to an infinite network of other
devices, to perform tasks, without human intervention.
Information technology becomes omnipresent.
”Pervasive Computing”: The idea that technology is to
move beyond the personal computer to everyday devices with
embedded technology and connectivity as computing devices
become progressively smaller and more powerful.
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Embedded Networked Sensing Potential
Habitat Monitoring
Storm petrels on Maine’s
Great Duck Island
Marine
Microorganisms
• Micro-sensors, onboard processing, and
wireless interfaces all
feasible at very small
scale
– can monitor
phenomena “up
close” in nonintrusive way
• Will enable spatially
and temporally dense
environmental
monitoring
• Embedded &
Networked Sensing will
reveal previously
unobservable
phenomena
Contaminant
Transport
Vehicle Detection
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Multiscale Observation and Fusion: Example, Regional
(or greater) scale to local scale
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Satellite, airborne remote sensing
data sets at regular time intervals
coupled to regional-scale
“backbone” sensor network for
ground-based observations
fusion, interpolation tools based
on large-scale computational
models
Small-scale
Sensor network
images from Susan Ustin, UC Davis
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Overview
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Motivation & Applications
Platforms and Power
Networking
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Sensor Network
• “Sensor Node”:
• Tiny vanilla computer with operating system, onboard sensor(s) and wireless communication (“PC
on a pin tip”)
• Trend towards low-cost, micro-sized sensors
• Use of wireless low range RF communication
• Batteries as energy resource
• “Sensor Network”
• Massive numbers of “sensors” in the environment
that measure and monitor physical phenomena
• Local interaction and collaboration of sensors
• Global monitoring
• Tightly coupled to the physical world to sense and
influence it
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Mica2 and Mica2Dot
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Processor:
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ATmega128 CPU
RAM/Storage:
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Chipcon CC1000
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1 inch
Manchester encoding
Tunable frequency
Byte spooling
Power usage scales
with range
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Mica Sensor Board
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Light (Photo)
Temperature
Acceleration
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Magnetometer
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2 axis
Resolution: ±2mg
Resolution:
134mG
Microphone
Tone Detector
Sounder
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4.5kHz
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A Network
S. Madden, UBerkeley
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Wireless Sensor Networks
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They present a range of computer systems
challenges because they are
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closely coupled to the physical world with
all its unpredictable variation, noise, and
asynchrony;
they involve many energy-constrained, resourcelimited devices operating in concert;
they must be largely self-organizing and selfmaintaining; and
they must be robust despite significant noise, loss,
and failure.
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Architecture
Application layer
Application: Events, Reactions
(temp-spatial)
DB layer
Data model, Declarative queries
Data aggregation, Query processing
Network layer
Adaptive topology, Geo-Routing
MAC, time, location
Physical layer
Phy: comm, sensing, actuation
Source:
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Deborah Estrin,
UCLA
Overview
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Motivation & Applications
Platforms & Power
Networking
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Communication using Radio
Listening &
receiving signals
Broadcasting
radio signals
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PicoRadio and Radio propagation
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Energy required to transmit signals in distance d
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Small energy consumption => short range communication
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Communication is huge battery drain
Indoor has lots of other complications
Multi-hop routing required to achieve distance
Routes around obstacles
Requires discovery, network topology formation, maintenance
 may dominate cost of communication
Energy to receive
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Dominated by listening time (potential receive)
Device has a total “lifespan”
Radio must be OFF most of the time!
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Low-level Networking
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Physical Layer
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Low-range radio broadcast/receive
Wireless (wiSeNets)
MAC: Media Access Control
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Controls when and how each node can transmit in the wireless
channel (“Admission control”)
Objectives:
 Channel utilization
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Latency
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Delay from sender to receiver; single hop or multi-hop
Throughput
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How well is the channel used? (bandwidth utilization)
Amount of data transferred from sender to receiver per time unit
Fairness
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Can nodes share the channel equally?
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MAC Design Decisions
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Energy is primary concern in sensor networks
What causes energy waste?
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Collisions
Control packet overhead
Overhearing unnecessary traffic
Dominant factor
Long idle time
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bursty traffic in sensor-net apps
Idle listening consumes 50—100% of the power for
receiving (Stemm97, Kasten)
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Networking
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Network Architecture: Can we adapt Internet protocols
and “end to end” architecture to SN?
 Internet routes data using IP Addresses in Packets and
Lookup tables in routers
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Many levels of indirection between data name and IP address,
but basically address-oriented routing
Works well for the Internet, and for support of Person-toPerson communication
Embedded, energy-constrained, unattended
system
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cannot tolerate communication overhead of indirection
sensor network architecture needs
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Minimal overhead, and Data centric routing
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Data-centric Routing
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Named-data as a way of tasking motes, expressing data
transport request (data-centric routing)
Basically:
 “send the request to sensors that can deliver the data, I do
not care about their address”
Initial approaches in literature:
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Some form of tree-based routing
Query sent out from server to motes
Sink-Tree built to carry data from motes to server
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Communication In Sensor Nets
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Radio communication
has high link-level
losses
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A
typically about 20% @
5m
B
Ad-hoc neighbor
discovery
Tree-based routing
C
D
F
E
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Tree Routing
Query
Parent Node
A
B
C
Children Nodes
D
E
F
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Tree building
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Queries/Request
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What goes in query?
Where does query go?
Neighbor selection
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How does mote select upstream neighbor for data?
Asymmetric links
Unidirectional links
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Tree building
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Dynamics
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Design tree building protocol
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How often do you send out a new query?
How often do you select a new upstream path ?
From query source to data producer(s) and back
Multihop ad-hoc routing
 reliable routing is essential!
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Basic Primitives
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Single Hop packet loss characteristics -> link quality
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Environment, distance, transmit power, temporal correlation,
data rate, packet siz
Services for High Level Protocols/Applications
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Link estimation
Neighborhood management
Reliable multi-hop routing for data collection
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Neighborhood Management
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Maintain link estimation statistics and routing
information of each neighboring sensor node
Issue:
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Density of nodes can be high but memory of nodes is limited
At high density, many links are poor or asymmetric
Neighborhood Management
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Question: when table becomes full,
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should we add new neighbor?
If so, evict old neighbor?
Similar to
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frequency estimation of data streams, or
classical cache policy
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Reliable Routing
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3 core components for Routing
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Neighbor table management
Link estimation
Routing protocol
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Quick Summary
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Motivation & Applications
Platforms & Power
Networking
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