Download Energy Aware Routing in Wireless Sensor Networks

Energy Aware Routing in
Wireless Sensor Networks
Jonathan Tate
19 December 2006
Outline
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Wireless Sensor Networks
Routing strategies
Reducing energy impact of routing
Simulation as a design tool
Wireless Sensor Networks
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A type of MANET
Every node is a router and a data source
Nodes are severely resource-constrained
Rapidly changing topology
May contain thousands of nodes
Resilient to failure of individual nodes
Self-organising
[Akyildiz02, Culler04]
What does a WSN do?
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Nodes monitor the environment
Sensor data has geographical context
Identity of individual node is unimportant
Hostile environments
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Environmental monitoring
Military
Surveillance
Emergency and disaster management
[Akyildiz02, Culler04, Szewczyk04]
Sensor Nodes
MICA [Polastre03]
Spec chip [Berkley03]
MICA 2 [Crossbow06]
Intel mote [Club04]
Topology Control
• No control over physical location of nodes
• Signal strength modulation to control connectivity
• Logical structure overlaid on physical topology
Inter-cluster routing
[Royer99, Beijar02, Chen01, Chiang97]
Node-centric zones of two hops
Energy-Aware Routing
• Maximise network lifetime (no accepted definition)
• Communication is the most expensive activity
• Possible goals include:
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Shortest-hop (fewest nodes involved)
Lowest energy route
Route via highest available energy
Distribute energy burden evenly
Lowest routing overhead
• Distributed algorithms cost energy
• Changing component state costs energy
[Raghunathan02, Jones01, Singh98, Weiser94, Shah02, Stojmenovic01]
Routing Strategies
• Aim to make communication more efficient
• Trade-off between routing overhead and
data transmission cost
• Strategies incur differing levels of
communication and storage overhead
• Hybrid approaches are possible
[Jones01, Beijar02, Royer99, Broch98]
Stateless Routing
• Nodes maintain no routing information
• Flooding
– Messages rebroadcast to neighbours
• Gossiping
– Messages rebroadcast to neighbours, probability <1
• Geographic
– Need to know direction to destination
• Epidemic
– Pairwise exchange of messages between carriers
– Copes with temporary network partition
– No routing state, but message buffering infeasible in WSNs
[Vahdat00, Xu01, Karp00, Ko98, Imielinski96]
Proactive and Reactive Routing
• Proactive routing
– Routes created and maintained in advance
– Low latency, high resource demand
– Does not scale to large networks
• Reactive routing
– Routes created and cached as required
– High latency, lower resource demand
[Johnson96, Perkins94, Perkins97, Das00, Park97]
Data-centric Routing
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Routing application data rather than packets
Node identities unknown to users
Data naming and labelling
Users express interests in named data, protocol
sets up data flows
• Combines routing and distributed data
management
• Data aggregated and summarised in flows
• Well suited to WSN paradigm
[Intanagonwiwat00, Ratnasamy02, Heinzelman99]
Flooding
• Used in data delivery or route discovery
• Very simple algorithm, implicit multicast
• Observed results surprisingly complex
– Stragglers, Backward Links, Long Links, Clustering
• Last 5% of nodes take as much time as preceding
95%, independent of radio power
• Some nodes will never receive the message
• Redundant communications waste energy
[Ni99, Ganesan02]
Flooding Behaviour
[Ganesan02]
1st broadcast
2nd broadcast
3rd broadcast
Final state
Broadcast Storm Problem
• Flooding is appropriate if topology changes
rapidly; other approaches cannot keep up
• Broadcast Storm Problem
– Redundancy
– Contention
– Collisions
• WSN nodes cannot afford energy or computation
cost of wasteful communication
[Ni99]
Solving the BSP
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Cannot ignore problem as flooding is needed
Nodes attempt to determine how much the
network will benefit from rebroadcast
Proposed classes of solution:
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[Ni99]
Probabilistic (gossiping)
Counter-based
Distance-based
Location-based
Cluster-based
WSNs require simple, low-resource solution
Gossiping
• Simple extension of flooding
• Probability of rebroadcast, p<1
• Bimodal behaviour theory
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For given p, results are consistent
Very few nodes receive message, or almost all
Critical probability, pc, at which switch occurs
Significant energy savings by setting p just above pc
• Protocols modified to use gossiping perform
better (e.g. AODV+G, DSR+G)
[Haas02]
Gossiping
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Bimodal behaviour formalised and analysed
pc varies between systems
pc cannot be determined analytically
Determine pc for a system by simulation
– Depends on reliable, accurate simulation
• Simulations find no evidence of phase transition
behaviour at pc, contradicting theory
– Is the theory or simulation result correct?
[Sasson02]
Network Simulation
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Real-world experiments often infeasible
Reproducible conditions
Simulated entities may not yet exist
No simulation is 100% accurate
– Too little detail harms accuracy
– Too much detail harms scalability
[Heidemann01, Johnson99, Kotz03]
Existing Simulators
• Numerous simulators have been used in WSN
and MANET research
• ns2, SeaWind, MaRS, PowerTOSSIM, TOSSF,
Tython, SensorSim, Aeon, EmStar, SENS, Avrora,
Atemu, SWAN, GloMoSim, …
• Few simulators scale to large networks
– Hard to partition problem for parallel simulation as any
given pair of nodes could interact at any time
– Cannot manage level of simulation detail appropriately
[Biaz01, Zeng98]
The ns-2 and ns-3 Simulators
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ns-2 widely used in network research
Does not directly execute mote code
Exponential execution time in the number of nodes
Impractical to model networks larger than 100-150 nodes
ns-3 proposed, but not yet implemented
ns-3 uses parallelisation for scalability, but still won’t scale
to very large networks
– Using multiple processors increases capacity, perhaps to ~1000
nodes at best due to coordination overhead
– Still nowhere near a million node network
[Henderson06, Das02, Naoumov03]
Simulation as a Design Tool
• GP used to evolve cluster head election
algorithm in [Weise06]
• Candidate algorithms evaluated for fitness
in a simulated network
• Offline tuning of algorithm to a network
• Simulation time restricts feasible
exploration of search space
[Weise06]
Possible Future Directions
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Design for analysis
Logical structures with specialist nodes
Online evolution through GP in-network
Hierarchical simulation
Application-level protocols
Distributed scheduling
Distributed knowledge management
Conclusions
• WSNs monitor hostile environments using
resource-constrained nodes
• Communications activity is expensive
• Network lifetime depends on energy
management policy
• Algorithms must suit the target network
• Large-scale simulation is vital in design,
tuning and evaluation of WSN algorithms
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[Perkins97]
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[Johnson96]
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[Ko98]
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[Park97]
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[Henderson06]
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[Ganesan02]
[Hall99]
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[Berkeley03]
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[Jones01]
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[Shah02]
[Stojmenovic01]
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[Intanagonwiwat00]
[Ratnasamy02]
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