ECE 194C Sensor Networks and Applications ... · HVAC control. (Micro-motes, smart dust.) –...
Transcript of ECE 194C Sensor Networks and Applications ... · HVAC control. (Micro-motes, smart dust.) –...
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ECE 194C Sensor Networks and Applications
Course Format
www.ece.ucsb.edu/Faculty/Iltis/ece194c
• Regular lectures, Faculty/Guest Speakers.
• Choose a sensor topic of interest.
– Think about possible product/markets?
– Problems of energy, distributed estimation,
communications.
• 5-10 page report due at end of course
• ~15 minute ppt. presentation on your ideas and
findings.
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ECE 194C Sensor Networks
• Applications driven– Global environmental monitoring. Temperature, pressure,
long-term climactic changes in atmosphere and in oceans. (e.g. ORION project.)
– Local environmental monitoring. Energy conservation, HVAC control. (Micro-motes, smart dust.)
– Localization of people, animals, objects. Security, search-and-rescue. (Motes, acoustic arrays.)
– Structure monitoring. Stresses/loads in buildings, bridges, roads. (ROADNet)
• Key problems– Wireless networking and protocols.
– Energy consumption.
– Disposal (Motes and batteries in forest problem.)
– Distributed Estimation
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Example of Eco-Sensing – Volcanic Activity
Ref, pictures: Werner-Allen, K. Lorincz et. al.
IEEE Internet Computing April 2006.
Uses Tmote Sky 802.15.4 sensor nodes.
Freewave radio for long-distance
communication (900 Mhz.)
GPS for time synchronization of events.
“Semi-acoustic” seismometers.
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Example of Underwater Eco-Sensing
Source: L. Washburn MSI PISCO/SBC-LTER.
Stearns Wharf
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Sensor Retrieval
Stearns Wharf
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Wireless Undersea Sensor Network (FRONT)
Source: J.A. Rice SPAWAR “Seaweb network for FRONT…” ,
www.benthos.com
Benthos Acoustic Modem
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FRONT Network
Source: J.A. Rice SPAWAR “Seaweb network for FRONT…”
and http://nopp.uconn/edu/ UCONN FRONT program
ADCP = Acoustic Doppler
Current Profiler
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Regional Sensor Network – UCSD ROADNet
Source: roadnet.ucsd.edu
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Generic Protocol Stack Definition – Wireless
Sensors
Application Layer
Network Layer
(Topology selection, routing.)
Medium Access Control (MAC) Layer
(802.15.4, Bluetooth, CSMA/CA)
Physical Layer (PHY)
(802.15.4, FSK/FH)
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Physical (PHY) Layer for Sensor
Networks• Options
– CDMA direct-sequence spread-spectrum (e.g. cdma2000 cellphones)• Complex demodulation and equalization, high power consumption.
– Frequency-hopping/FSK• Used in lower-cost sensor networks e.g. Bluetooth.
• Sensitive to multipath interference.
– Complementary code keying (CCK) 802.11b. • 11 mbps unneeded for most sensor applications, too expensive and power-
hungry.
– Orthogonal frequency-division multiplexing (OFDM).• 802.11a/g
• 54 mbps again not needed in most sensor applications.
• High power consumption due to FFT/equalization operations for demodulation.
– 802.15.4 (Zigbee). Based on direct-sequence type waveforms with simple detection.
• Optimized for sensor networks, supplanting earlier FSK implementations.
• Up to 250 kbps in the 2.4 GHz band.
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Example of Mote Radio
Chipcon CC1000
Carrier Freq. 300 MHz – 1 GHz
FSK data rate up to 76 kbps
PLL Detection
Source:
Chipcon Corp.
www.chipcon.com
www.xbow.com
MICA2
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FSK Signaling
0 50 100 150 200 250 300 350 400-1
-0.5
0
0.5
1
Time
FS
K S
ignal A
mplit
ude
0 50 100 150 200 250 300 350 400-1.5
-1
-0.5
0
0.5
1
1.5
Time
Data
−=+∈
=+∈=
1)(),)1(,[),/4cos(
1)(),)1(,[),/2cos()(
nbTnnTtTt
nbTnnTtTtts
π
π
T
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PLL Detection in FSK Sensor Radios
Loop Filter h(t)
VCO
s(t)
)(tsv
v(t)
))(2cos( ttfA vcv φπ +
))(2cos( ttfA c φπ +
kv
2km
VCO offset frequency is proportional to voltage v(t).
Voltage v(t) tracks FSK frequency deviation.
)()()(
2
1tvkftf
dt
tdvcv
v =−=φ
π
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802.15.4 PHY Layer – 868-915 MHz
Direct-Sequence Spread-Spectrum
1)( ±=nd
300/600 k chips/sec
20/40 k bps -- 15 chips/symbol
BPSK/raised cosine pulse shaping
15 chip PN sequence
300/600 kcps
20/40 kbps
L.O.
~900 MHz
PA
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Simplified 802.15.4 PHY Receiver
LNAQuad
Mixer
PLL/
VCO
∑ Re
Correlator
0<
> )(ˆ nd
15 chip Replica
Sequence
~900 MHz
Zero IF design – conversion directly to baseband but requires
complex-valued signal representation.
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Medium Access Control
• Options include
– Time-division multiple access (TDMA)
• Used in some sensor networks (Bluetooth) – requires central coordination.
– Code-division multiple access (CDMA)
• Too expensive for sensors, used in cellphones, military comms.
– Carrier sense multiple access/collision avoidance (CSMA/CA)
• Least complex to implement (just use RSSI.) Suffers from low throughput, hidden and exposed terminal problems.
• Used in Chipcon FSK/FH radios and 802.15.4 (Zigbee)
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Basic TDMA
1 2 3 1 2 3 1 2 3
Coordinator – transmits sync. beacon.
(Alternative to beacon – use GPS or
atomic clocks in each sensor.)
Time
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Bluetooth TDMA/FH
Coordinator
Client
1600 hops/sec.
710 kbps
79 channels
Time
Fre
q
Transmission between
Coordinator/Client
only
Synchronous mode – 2
slots/client trans.
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CSMA/CA (802.11x, 802.15.4)
Carrier sense multiple access – asynchronous, no central coordination required.
Only need RSSI indicator in sensor radio.
DIFS = Distributed Interframe Space, SIFS = Short Interframe Space.
NAV = Network Allocation Vector, CW = Contention Window
Data
ACK
Source
Dest
Other
SIFS
NAV/CW– Defer for random time
DIFS
Data
DIFS
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CSMA Collisions
Data ASource A
DIFS
Data BSource B
DIFS T
aT Propagation Delay
Destination C A collides with B
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Fundamental CSMA Throughput
Source: Kleinrock and Tobagi
IEEE Trans. Commun. 1975
aG
aG
eaG
GeS
−
−
++=
)21(
Key Problem: Due to
propagation delay a, node B
may not sense node A’s
initiation of transmission –
collision. 0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
G: Offered Load Packets/Packet Duration
Thro
ug
hp
ut
S: Throughput Packets/T
Throughput per sensor
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Simpified Network Layer -- Zigbee
Coordinator
Client
Refs: www.zigbee.org, J. Munk-Stander “Implementing a Zigbee Protocol
Stack…” http://www.diku.dk/~bonnet/ba.zigbee.pdf
Zigbee network needs at least one “full function device” (Coordinator)
FFD establishes networks, routing tables, assigns “bindings” to
devices (application.)
Coordinator knows which client owns which sensor.
64 bit long addresses or 16 bit short addresses for each device.
Sensor 1
Sensor 2
Star Topology
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Star versus Mesh Network -- Zigbee
Star Topology – Similar to Bluetooth. Each client/end device communicates only through
the coordinator.
Mesh Network – Clients/end devices can communicate directly with each other. Need
routing algorithm and tables.
Star Topology Mesh Topology
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Zigbee Routing
• Uses AODV/tree/cluster-based routing.
• Summary of AODV (ref. C. E. Perkins, E. Royer 99.)– Ad hoc on-demand distance-vector algorithm
– Only nodes on an active path maintain routing information (tables).
– Path Discovery
– Node transmits Route Request RREQ to neighbors.
– Neighbor transmits either Route Reply (RREP) if it has a route to the destination, or rebroadcasts RREQ to its own neighbors
– RREQ packets set up reverse path
– As RREQ travels back, forward pointers to origin of RREQ are set up.
– Forward pointers establish forward path to destination.
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AODV Routing/Zigbee
Ref. C. Perkins, E. Roy IEEE Workshop Mobile Comput. 99)
SourceDest.
RREQ (Source, Dest., …)
SourceDest.
RREP (Source, Dest., …)
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GPS-less PositioningMethods: Signal strength – requires mapping.
Connectivity – high sensor density.
DS-CDMA handshaking using round-trip travel time (RTT)
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Radiolocation/Multipath
0 200 400 600 8000
100
200
300
400
500
600
700
800
900
x [meters]
y [m
ete
rs]
1
2
3
4
Transmitter
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Sparse Multipath Estimation Problem
20 30 400
0.1
0.2
0.3
0.4
0.5
0.6
Re
lative
po
we
r
[x 0.1 µs.]
Node4: Multipath Intensity Profile
30 40 50 600
0.05
0.1
0.15
0.2
0.25
Re
lative
po
we
r
[x 0.1 µs.]
Node2: Multipath Intensity Profile
30 40 500
0.01
0.02
0.03
Re
lative
po
we
r
[x 0.1 µs.]
Node3: Multipath Intensity Profile
True ChannelTrue TOAGSIC/MP Estimated ChannelGSIC/MP Estimated TOA
20 30 40 50 600
0.02
0.04
0.06
0.08R
ela
tive
po
we
r
[x 0.1 µs.]
Node1: Multipath Intensity Profile
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Distributed Estimation – Information Graph
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Estimation Problem -- Radiolocation
• Round-trip travel time is nonlinear function
of x-y position.
ijijiij nyyxxc
z +−+−= 22 )()(1
• Estimation techniques
– Pseudo-linearization of zij followed by linear
least-squares
– Extended Kalman filter
– Unscented Kalman, particle filters.
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DS-CDMA Radiolocation Gives GPS-type
Performance
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
40
45
Packets
Avera
ge P
ositio
n E
stim
ation E
rror
[m] 1‘ Round−Robin
3‘ 4/8 Optimal
4‘ 4/8 Suboptimal
7‘ RTT−only Opt.
8‘ RTT−only Sub.
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Localization Using Sensor Networks
Time-of-arrival (line-of-bearing) obtained via beamforming in direction of
strongest acoustic signature.
Figure, Ref: Wang and Chandrakasan, IEEE Sig. Proc. Mag. July 2002.
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Delay and Sum Beamforming
1τ
2τ
3τ
)(1 tr
)(2 tr
)(3 tr
Acoustic
Wavefront
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Delay and Sum Beamforming
cd /sinθτ =
cd /sin2 θτ =
θ
Example: Linear sensor array. d = inter-sensor spacing, c = speed-of-sound.
d
)(tr
)/sin( cdtr θ−
)/sin2( cdtr θ−
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Delay and Sum Beamforming (Cont’d)Delays are functions of angle-of-arrival and sensor index.
)(max
argˆ
))ˆ()(()(0
2
1
θθ
θ
θτθτθ
g
dttrgT N
k
kk
=
+−= ∫ ∑=
∑
∑ ∑−
=
−
−
=
−
=
=
=
1
0
/2
1
0
21
1
)(2
)()(
,)()(
N
l
Nmni
kk
N
n
N
k
ni
k
emrnR
enRgs
k
π
θτπθ
FFT-based beamforming/direction-of-arrival estimation (Wang and Chandrakasan.)
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Energy Issues
• Batteries
– Insufficient lifetime, disposal, effect on environment.
• Solar ( “Power of the Sun” Video)
– What about sensors in buildings, under foliage, caves?
– Underwater?
• Power scavenging
– Reasonable for low data rates, 1 bits/sec.
• Use motion, ambient light, broadcast RF signals?
– Sources for power scavenging underwater? Currents?
Plankton?
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Example of Power Scavenging• Vibration converted to variable capacitance –
(Ref, pictures S. Roundy, P. Wright and J. Rabaey, Computer Commun. July 2003.)
Sources of vibration: Microwave oven, windows next to street, “person nervously tapping heel”, CD drive on laptop computer.
Vibration changes
capacitance.
Fixed charge, V
changes
V = q/C
Piezoelectric
– vibration
directly
generates
voltage.
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