Transcript CWC Slide Template - University of Oulu

Centre for Wireless
Communications
Media Access and Routing Protocols for
Power Constrained Ad Hoc Networks
Carlos Pomalaza-Ráez
Centre for Wireless Communications – University of Oulu
and
Indiana University - Purdue University, USA
[email protected]
http://www.cwc.oulu.fi
Outline
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Introduction
Main features of power constrained networks
Design considerations
MAC layer
Routing algorithms
Physical layer issues
Cross-Channel design
Final observations
Main Features of Ad Hoc Networks
• Dynamic topology
• Bandwidth-constrained and variable
capacity links
• Energy-constrained operations
• Limited physical security
Mobility in Ad Hoc Networks
Dynamic Routing
Route Maintenance
Wireless Sensor Networks (WSN)
New technologies
have reduced the
cost, size and power
of micro-sensors and
wireless interfaces
Circulatory Net
Sensing
Networking
Computation
Environmental Monitoring
Benefits from 3 technologies
• digital circuitry
• wireless communication
• silicon micro-machining
Applications
•Battlefield
Detection, classification
and tracking
• Habitat Monitoring
Micro-climate and wildlife
monitoring
 Examples:
– ZebraNet (Princeton)
– Seabird monitoring in Maine’s
Great Duck Island
(Berkeley & Intel)
Applications
• Structural, seismic
Bridges, highways, buildings
• Examples: Coronado Bridge San Diego
(UCSD), Factor Building (UCLA)
• Smart roads
Traffic monitoring, accident
detection, recovery assistance
highway
• Examples: ATON project (UCSD)
camera
• Contaminants detection
microphone
Sensor Nodes
Sensor Node Evolution
Mote Type
WeC
Rene
Rene2
Dot
Mica
Date
Sep-99
Oct-00
Jun-01
Aug-01
Feb-02
Microcontroller (4MHz)
Type
Prog. mem. (KB)
RAM (KB)
AT90LS8535
ATMega163
ATMega103/128
8
16
128
0.5
1
4
Communication
Radio
Rate (Kbps)
Modulation Type
RFM TR1000
10
10/40
OOK
OOK/ASK
Typical Features of WSN
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Relatively large number of nodes
Low cost, size, and weight per node
Energy constrained
Prone to failures
Almost static topology
More use of broadcast communications
instead of point-to-point
• Nodes do not have a global ID
• Limited security
Design Considerations
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Fault tolerance
Scalability
Cost
Power consumption
Hardware and software constraints
Topology maintenance
Deployment
Environment
Node Energy Consumption Projections
10,000
Average Power (mW)
• Deployed (5W)
1,000
100
• (50 mW)
10

(1mW)
1
.1
2000
2002
2004
Node Hardware
In-node
processing
Wireless communication
with neighboring nodes
Event
detection
Acoustic, seismic,
magnetic, etc. sensors
interface
CPU
radio
battery
Limited battery supply
Electro-magnetic
interface
Energy Limitations
• Each sensor node has limited energy supply
• Nodes may not be rechargeable
• Energy consumption in
 Sensing
 Data processing
 Communication (most energy intensive)
Power (mW)
20
15
10
5
0
Sensing
CPU
TX
RX
IDLE
SLEEP
Power consumption of node subsystems
A Layered Approach
Network
MAC
PHY
Media Access Control
MAC: Let multiple radios share the
same communication media
• Local Topology Discovery and Management
• Media Partition By Allocation or Contention
• Provide Logical Channels to Upper Layers
Application
Network
MAC
Physical
Code
Time
Channel Access in Multi-hop Networks
Large number of short range radios in a wide area
Cons: Hidden Terminal - CSMA is not appropriate No Global Synch
Pros: Channel Reuse
B
A
C
E
D
Which MAC is Good for WSNs?
• Most existing MACs are targeted for
One-hop, centralized control network: cellular
network, 802.11, Bluetooth…
Bandwidth hungry application, strict QoS
requirement
• Existing MACs are based on existing radios
More than 90% of power is burned when radio
is idle
“Energy efficient” WSNs built using existing
MACs might not be that efficient
Two Possible Approaches
• Modify or enhance current protocols to
make them more energy aware
For example use the power control
mechanism present in the IEEE 802.11
standard
• Develop and implement new protocols
that take into account full consideration
of the network constraints
IEEE 802.11 Standard
• PCF (Point Coordination Function)
Centralized medium access control
• DCF (Distributed Coordination Function)
Distributed medium access control
802.11 DCF: RTS-CTS-DATA-ACK
A (Sender)
B (Receiver)
Four Way Handshake RTS-CTS-DATA-ACK
• A sender node waits for DIFS( Distributed
Inter-Frame Space) before making an RTS
attempt
• A node enters a SIFS ( Short Inter Frame
Space) before sending an ACK, DATA or CTS
frame
• NAV (Network Allocation Vector) indicates the
duration of the current transmission
Four Way Handshake RTS-CTS-DATA-ACK
DIFS
SIFS
RTS
DATA
Sender node
SIFS
SIFS
CTS
Receiver node
NAV(CTS)
NAV(RTS)
Others
NAV- Network Allocation Vector
ACK
Simple Power Control
A
RTS
CTS
B
C
D
Simple Power Control
A
DATA
ACK
B
C
D
Power Levels (simple power control)
Pmax
Pi
0
RTS
CTS
DATA
ACK
Ranges
• Transmission range
Receive and correctly decode packets
• Carrier sensing range
Sensing the signal
• Carrier sensing zone
Sensing the signal, but cannot decode it
correctly
Can interfere with on-going transmission
Ranges
Carrier Sensing
Zone
Transmission
Range
A
B
C
D
Carrier Sensing
Range
E
Variation of 802.11 DCF
SRC
DATA
RTS
SIFS
DIFS
DST
ACK
CTS
SIFS
SIFS
TX range
DIFS
NAV (RTS)
NAV (CTS)
CS zone
NAV (EIFS)
NAV (EIFS)
Defer Channel Access
NAV (EIFS)
Improved Power Control
Less than EIFS
Pmax
Pi
0
RTS
CTS
DATA
ACK
Revisiting the CDMA Multi-Channel Problem
7
6
2
1
5
3
4
8
Nodes use different channels (codes) to transmit data
The codes are locally unique with global reuse
Parallel transmission without synch
Implicit local address is the channel
Channel Assignment in Cellular Networks
• Same frequency can be used in all cells of the same color
• Minimize number of frequencies (colors)
• The topology is static
Code Assignment = Graph Coloring
Graph G = (V,E)
Δ is the maximum
degree
For any node, all its neighbors have different colors
OR
All two-hop neighbors have different colors
Number of colors needed <= min {Δ(Δ -1)+1, |V|}
Brook and Vizing theorem
Code Assignment in Ad Hoc Networks
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There is no base station
Nodes are free to connect or disconnect
Nodes move about
Increase or decrease their transmission range
These features call for
distributed, dynamic, power aware
code assignment algorithms
Routing
Multihop Routing
due to limited transmission range
Routing Issues
Application
Network
MAC
Physical
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Low mobility
Power aware
Irregular topology
MAC aware
Limited buffer space
Routing
Proactive vs. Reactive
Proactive routing
maintains routes to
every other node in
the network
Reactive routing
maintains routes to
only those nodes
which are needed
Regular routing
updates impose
large overhead
Cost of finding routes
is expensive since
flooding is involved
Suitable for high
traffic networks
Good for low/medium
traffic networks
Ad-hoc On-demand Distance Vector
Protocol (AODV)
Source
broadcasts
a route packet
source
neighbors re-broadcast
the packet till it reaches
the destination
RREQ
reply packet follows the
reverse path of the route
request packet recorded
in broadcast packet
nodes discard the
packets having
been seen
destination
RREP
Traditional Reactive Protocols
Finds the best route
Source
Destination
and then uses it as much as possible
But that is NOT a good solution!
Energy depletion in certain nodes
Creation of hotspots in the network
New Approaches
• Application aware communication primitives
(expressed in terms of named data not in
terms of node who requests data)
• Achieve locality for decision making
(and reduce the communication)
• Application centric, data-driven networks
• Achieve desired global behavior through
localized interactions, without global state
Directed Diffusion
Gradient
represents
both
direction
towards
data
matching
Application-aware
communication
primitives
This
Consumer
Nodes
Theprocess
choice
diffuse
of sets
of
data
path
the
up
initiates
interest
gradients
is made
interest
towards
locally
in the
inproducers
at
network
data
every
with
node
tovia
certain
draw
afor
Every
route
has a probability
ofcost
being
chosen
Probability
 1/energy
Collect
energy
metrics
along
the way
and status
of demand
with
desired
expressed
terms
of
namedupdate
data rate
sequence
events
matching
every
ofin
attributes
local
packet
the
interactions
interest
Four-legged
animal
Source
Sink
Directed Diffusion
Reinforcement
negative to
reinforcement
used
Has built and
in tolerance
nodes moving
to converge
to range
efficient
out of
ordistribution
dying
Source
Sink
Directed Diffusion
• Pros
Energy – much less traffic than flooding –
Latency – transmits data along the best
path –
Scalability – local interactions only –
Robust – retransmissions of interests –
• Cons
The set up phase of the gradients is
expensive
SPIN
Sensor Protocol for Information via Negotiation
• Basic idea
Exchange data when needed
Save energy by being resource aware
• Data negotiation
 Meta-data (data naming)
 Application-level control
SPIN
•SPIN messages
 ADV- advertise data
 REQ- request specific data
 DATA- requested data
•Resource management
 Nodes decide their
capability of participation
in data transmissions
ADV
A
B
REQ
A
B
DATA
A
B
SPIN-BC (broadcast)
It
Sensor
sends
broadcasts
meta-data
data
toitself
neighbors
A
Neighbor
node
senses
sends
something
a REQ
listing
“interesting”
of the
data
The
Neighbors
process
aggregate
repeats
data
across
andall
broadcast
the
network
it would likemeta-data
to acquire
(advertise)
ADV
REQ
DATA
SPIN-BC (broadcast)
Advertise meta-data
Advertise
Send data
Send data
I am tired
Send
I need to
sleep …
data
Advertise
meta-data
Advertise
Send
data
Request
Nodes dodata
need
to
Requestnot
data
participate in the
process
Request data
SPIN
• Pros
Energy – more efficient than flooding –
Latency – converges quickly –
Scalability – local interactions only –
Robust – immune to node failures –
• Cons
Nodes always participating
Some Physical Layer Issues
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Frequency selection
Application
Carrier frequency generation
Network
Signal detection
MAC
Modulation
Physical
Binary and M-ary modulation schemes
Binary modulation scheme is deemed to be more
energy-efficient
• Low transmission power and simple transceiver
circuitry make Ultra Wideband (UWB) an attractive
candidate
• Hardware design, e.g. wake-up radio
Wake-up Radio
Sleeping nodes
Communicating nodes
• Sleeping mode based on Ultra Low Power wake-up radio
• Sleeping nodes have to wake-up to broadcast signals, and
not to any signal from surrounding communicating nodes
• Broadcast signals should not disrupt data transmission
Cross-Layer Design
• Optimizing single layer might not be
enough
• Scheduling, adaptability, and diversity
are most powerful in the context of a
cross-layer design
• Energy consumption must be addressed
across all protocol layers
Final Observations
• The constraints imposed by factors
such as power consumption, costs,
fault tolerance, multihop topology,
etc., are more stringent in sensor
type networks than in conventional
ad-hoc networks
• This calls for new techniques and
protocols at different layers of the
protocol stack
References
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I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless Sensor
Networks: A Survey,” Computer Networks, vol. 38, 2002, pp. 393-422.
C.-Y. Chong and S.P. Kumar, “Sensor Networks: Evolution, Opportunities, and
Challenges,” Proceedings of the IEEE, vol. 91, no. 8, August 2003, pp. 12471256.
K.A. Delin and S.P. Jackson, “Sensor Web for In Situ Exploration of Gaseous
Biosignatures,” IEEE Aerospace Conference, Big Sky, Montana, March 2000.
A.J. Goldsmith, S.B. Wicker, “Design Challenges for Energy-Constrained Ad Hoc
Wireless Networks, IEEE Wireless Communications, August 2002, pp. 8-27.
C. Guo, L.Z. Zhong, J.M. Rabaey, “Low Power Distributed MAC for Ad Hoc Sensor
Radio Networks,” IEEE Global Telecommunications Conference, San Antonio,
November 2001, vol. 5, pp. 2944-2948 .
C. Itanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed
Diffusion for Wireless Sensor Networking,” IEEE/ACM Transactions on Networking,
vol. 11, no. 1, February 2003, pp.2-16.
C. Itanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Impact of
Network Density on Data Aggregation in Wireless Sensor Networks,” Proceedings
of the 22nd International Conference on Distributed Computing Systems, Vienna,
Austria, July 2002, pp. 457-458.
References
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C. Itanagonwiwat, R. Govindan, D. Estrin, “Directed Diffusion: A Scalable and
Robust Communication Paradigm for Sensor Networks,” Proceedings of the
ACM/IEEE Conference on Mobile Computing and Networking, Boston, August
2000, pp. 56-67.
E.-S. Jung and N.H. Vaidya, “A Power Control MAC Protocol for Ad Hoc Networks,”
ACM/IEEE Int. Conf. on Mobile Computing and Networking, Atlanta, Georgia,
September 2002, pp. 36-47.
J. Kulik, W. Rabiner Heinzelman, and H. Balakrishnan, “Negotiation-Based
Protocols for Disseminating Information in Wireless Sensor Networks,” ACM/IEEE
Int. Conf. on Mobile Computing and Networking, Seattle, WA, Aug. 1999.
J. Rabaey, J. Ammer, J. da Silva, D. Patel, S. Roundy, “Picoradio Supports Ad-hoc
Ultra-low Power Wireless Networking”, IEEE Computer Magazine, July 2000.
W. Rabiner Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient
Communications Protocols for Wireless Microsensor Networks,” Proceedings of
the 33rd International Conference on System Sciences, January 2000.
K. Sohrabi, J. Gao, V. Ailawadhi, and G.J. Pottie, “Protocols for Self-Organization
of a Wireless Sensor Network,” IEEE Personal Communications, October 2000, pp.
16-27.
C.-K. Toh, “Maximum Battery Life Routing to Support Ubiquitous Mobile
Computing in Wireless Ad Hoc Networks,” IEEE Communications Magazine, June
2001, pp. 2-11.
S. Toumpis and A.J. Goldsmith, “Performance, Optimization, and Cross-Layer
Design of Media Access Protocols for Wireless Ad Hoc Networks,” International
Conference on Communications (ICC), Anchorage, Alaska, May 2003, pp. 22342240.