Post on 08-Feb-2016
description
Centre for Wireless Communications
Media Access and Routing Protocols for Power Constrained Ad Hoc Networks
Carlos Pomalaza-RáezCentre for Wireless Communications – University of Oulu
andIndiana University - Purdue University, USA
carlos@ee.oulu.fihttp://www.cwc.oulu.fi
Outline
• 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
Wireless Sensor Networks (WSN)
Sensing
Computation
Networking
Circulatory Net
New technologies have reduced the cost, size and power of micro-sensors and wireless interfaces
Environmental Monitoring
Benefits from 3 technologies• digital circuitry• wireless communication• silicon micro-machining
Applications•BattlefieldDetection, classification
and tracking
• Habitat MonitoringMicro-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 roadsTraffic monitoring, accident
detection, recovery assistance• Examples: ATON project (UCSD)
highway
camera microphone
• Contaminants detection
Sensor Node EvolutionMote Type WeC Rene Rene2 Dot Mica
Date Sep-99 Oct-00 Jun-01 Aug-01 Feb-02
Microcontroller (4MHz)
Type AT90LS8535 ATMega163 ATMega103/128
Prog. mem. (KB) 8 16 128
RAM (KB) 0.5 1 4
CommunicationRadio RFM TR1000
Rate (Kbps) 10 10/40
Modulation Type OOK OOK/ASK
Typical Features of WSN• 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
• Fault tolerance• Scalability• Cost• Power consumption• Hardware and software constraints• Topology maintenance• Deployment• Environment
Design Considerations
Node Energy Consumption Projections
20002000 20022002 20042004
10,0010,0000
1,0001,000
100100
1010
11
.1.1
Ave
rage
Pow
er
(mW
)
• Deployed (5W)
• (50 mW)
(1mW)
Node Hardware
sensors CPU radio
battery
Acoustic, seismic, magnetic, etc.
interfaceElectro-magnetic
interface
Limited battery supply
Eventdetection
Wireless communication with neighboring nodes
In-node processing
Energy Limitations
Power consumption of node subsystems
0
5
10
15
20
Powe
r (m
W)
Sensing
CPU TX RX
IDLE SLEEP
• Each sensor node has limited energy supply• Nodes may not be rechargeable• Energy consumption in
Sensing Data processing Communication (most energy intensive)
Media Access ControlLet multiple radios share the same communication media
MAC:• Local Topology Discovery and
Management• Media Partition By Allocation or
Contention• Provide Logical Channels to Upper
Layers
PhysicalMAC
NetworkApplication
Time
Code
Frequen
cy
Channel Access in Multi-hop Networks
AB
CE D
Large number of short range radios in a wide area
Pros: Channel Reuse
Hidden Terminal - CSMA is not appropriate -No Global Synch
Cons:
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 radiosMore 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 awareFor 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
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
Sender node
Receiver node
Others
RTS
CTS
DIFSDATA
SIFS
SIFSACK
SIFS
NAV(RTS)NAV(CTS)
NAV- Network Allocation Vector
Ranges• Transmission range
Receive and correctly decode packets
• Carrier sensing rangeSensing the signal
• Carrier sensing zoneSensing the signal, but cannot decode it
correctlyCan interfere with on-going transmission
Variation of 802.11 DCFSRC RTS
CTS
NAV (RTS)
DATA
ACK
NAV (CTS)
NAV (EIFS)NAV (EIFS)
NAV (EIFS)
DIFS
DIFSSIFS
SIFS
SIFS
Defer Channel Access
DST
TX range
CS zone
Revisiting the CDMA Multi-Channel Problem
1
3
2
4
5
6 7
8
Nodes use different channels (codes) to transmit dataThe codes are locally unique with global reuseParallel transmission without synchImplicit 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
For any node, all its neighbors have different colors
ORAll two-hop neighbors have different colors
Graph G = (V,E)
Δ is the maximum
degree
Number of colors needed <= min {Δ(Δ -1)+1, |V|}
Brook and Vizing theorem
Code Assignment in Ad Hoc Networks
• There is no base station• Nodes are free to connect or disconnect• Nodes move about• Increase or decrease their transmission range
These features call fordistributed, dynamic, power
aware code assignment algorithms
Routing
Multihop Routing due to limited transmission range Routing
Issues• Low mobility• Power aware• Irregular topology• MAC aware• Limited buffer spacePhysical
MACNetwork
Application
Routing Proactive vs. Reactive
Proactive routing maintains routes to every other node in the network
Regular routing updates impose large overhead
Suitable for high traffic networks
Reactive routing maintains routes to only those nodes which are needed
Cost of finding routes is expensive since flooding is involved
Good for low/medium traffic networks
Ad-hoc On-demand Distance Vector Protocol (AODV)
nodes discard thepackets havingbeen seen
sourcedestination
Sourcebroadcastsa route packet
neighbors re-broadcastthe packet till it reachesthe destination
reply packet follows thereverse path of the routerequest packet recordedin broadcast packet
RREQ
RREP
Traditional Reactive Protocols
Source Destination
But that is NOT a good solution!Energy depletion in certain nodesCreation of hotspots in the network
Finds the best route
and then uses it as much as possible
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
Gradient represents both direction towards data matching and status of demand with desired update rateProbability 1/energy costThe choice of path is made locally at every node for
every packet
Directed Diffusion
Sink
Source
Application-aware communication primitivesexpressed in terms of named data
Consumer of data initiates interest in data with certain attributes
Nodes diffuse the interest towards producers via a sequence of local interactions
This process sets up gradients in the network to draw events matching the interestCollect energy metrics along the wayEvery route has a probability of being chosen
Four-leggedanimal
Directed Diffusion
Sink
Reinforcement and negative reinforcement used to converge to efficient distributionHas built in tolerance to nodes moving
out of range or dying
Source
Directed Diffusion• Pros
Energy – much less traffic than flooding –
Latency – transmits data along the best path –
Scalability – local interactions only –Robust – retransmissions of interests –
• ConsThe set up phase of the gradients is
expensive
SPINSensor Protocol for Information via Negotiation
• Basic ideaExchange data when neededSave energy by being resource aware
• Data negotiation Meta-data (data naming) Application-level control
SPIN
A B
A B
A B
ADV
REQ
DATA
•SPIN messages ADV- advertise data REQ- request specific data DATA- requested data
•Resource management Nodes decide their capability of participation
in data transmissions
The process repeats itself across the network
SPIN-BC (broadcast)
DATAREQADV
A node senses something “interesting”It sends meta-data to neighborsNeighbor sends a REQ listing all of the data it would like to acquireSensor broadcasts dataNeighbors aggregate data and broadcast(advertise) meta-data
SPIN-BC (broadcast)
I am tired I need to sleep …
Advertise meta-data
Request data
Send dataAdvertise
Advertise
Nodes do need not to participate in the process
Request data
Send data
Send data
Advertise meta-data
Request data
Send data
SPIN• Pros
Energy – more efficient than flooding –
Latency – converges quickly –Scalability – local interactions only –Robust – immune to node failures –
• ConsNodes always participating
Some Physical Layer Issues• Frequency selection• Carrier frequency generation• Signal detection• Modulation
Binary and M-ary modulation schemesBinary 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
PhysicalMAC
NetworkApplication
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• 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. 1247-1256.
• 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• 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. 2234-2240.