1. INT598 Data-Centric Routing Protocols
Silvia Nittel
Spatial Information Science & Engineering
University of Maine
Fall 2006

2. Overview Motivation & Applications Platforms, Operating Systems, Power
Networking
Physical layer, MAC, Protocols
Routing
Data-centric Routing
Routing Protocols
Adaptable, Configurable Systems
Data Collection and Aggregation

3. Data-centric Routing
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”
Two initial approaches in literature:
Derived from multicast-routing perspective where you name a logical group of sensor nodes (Diffusion)
Derived from database query language (TinyDB) with stronger semantics on data delivery, timing, sequencing
Commonality is tree-based routing
Query sent out from microserver to motes
Sink-Tree built to carry data from motes to microserver

4. Tree Routing
Query A B C D F E
Parent Node
Children Nodes

5. Tree building Queries/Request Neighbor selection Multiple microservers
How to specify a query/request?
To which nodes does a query go?
Neighbor selection
How does a mote select upstream neighbor for data?
Asymmetric links
Unidirectional links
Route characterization (like ETX)
Multiple microservers
What about multiple microservers?
How does mote select a microserver?

6. Tree building Dynamics Design Tree building protocol
How often do you send out a new query?
How often do you select a new upstream path
Design Tree building protocol
From query source to data producer(s) and back
Multihop ad-hoc routing
 reliable routing is essential!

7. Basic Primitives Single-hop packet loss characteristics
Environment, distance, transmit power, temporal correlation, data rate, packet size
Services for high level protocols/applications
Link estimation
Neighborhood management
Reliable multihop routing for data collection

8. Reliable Routing 3 core components for reliable routing
Neighbor table management (‘best neighbors’?)
Link estimation (of communication quality to neighbors)
Routing protocol

9. Routing in WiSeNets Communication between nodes in a sensor network
A) sending a request to a specific node
Node: at the right location, sensing the right data
B) sending (sensed) data back to requesting node(s)
Objectives:
Achieve network connectivity (all nodes are reachable)
Find/reach sensor nodes that can contribute data
Establish ‘communication paths’

10. Routing Protocols for WiSeNets
Flooding
Gradient
Clustering and Cellular
Geographic
Energy-aware

11. Flooding
SPIN – Sensor Protocol for Information via Negotiation [HKB99], [HKB02] Heinzelman, Kulik, Balakrishnan
Flooding
Classical flooding:
disseminate all observations to all nodes in the network
SPIN:
Flooding variant
See paper
Reliable, robust communication, but very expensive energy-wise

12. SPIN

13. Routing Protocols for WiSeNets
Flooding
Gradient
Clustering and Cellular
Geographic
Energy-aware

14. Gradient-based Approach
Influential approaches:
Directed Diffusion [IGE00] Intanagonwiwat, Govindan Estrin
GEAR – Geographical and Energy-Aware Routing [YGE01] Yu, Govindan, Estrin

15. Directed Diffusion All communication using named data
Data-centric communication
All communication using named data
Sets of attribute/value pairs are used to identify data
Established gradients in the network are matched with data to determine the next hop along route to sink
Sensor nodes are task-aware
Sensor nodes respond to user specified interests (task descriptions or queries) matching their particular local capabilities (attached sensors)
Task description: specification of which data do to collect, in which sampling intervals
Local gradients are set up through interest propagation from sink to source (establishing path back to sink) (example!)
Path reinforcement to identify best route between nodes
Data cached at intermediate nodes for aggregation and loop prevention

16. Directed Diffusion Interest: type = 4 legged animal interval = 1 s
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Interest Transmission
Established Gradient 8 20

17. Interest Transmission Established Gradient
type = 4 legged animal
interval = 1 s
rect = [0, 75, 25, 100]
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Interest Transmission
Established Gradient 8 20

18. Interest Transmission Established Gradient
type = 4 legged animal
interval = 1 s
rect = [0, 75, 25, 100]
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Interest Transmission
Established Gradient 8 20

19. Interest Transmission Established Gradient
type = 4 legged animal
interval = 1 s
rect = [0, 75, 25, 100]
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9 8
Interest Transmission
Established Gradient 20

20. Interest Transmission Established Gradient
type = 4 legged animal
interval = 1 s
rect = [0, 75, 25, 100]
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Interest Transmission
Established Gradient 8 20

21. Interest Transmission Established Gradient
type = 4 legged animal
interval = 1 s
rect = [0, 75, 25, 100]
timestamp = 1:20:40
expires = 1:30:40
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Interest Transmission
Established Gradient 8 20

22. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

23. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

24. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

25. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

26. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

27. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

28. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

29. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

30. Interest Transmission Reinforced path
(0,100)
(25,100) 3 2 1
(0,75)
(25,75) 4 5 6 7 9
Data
Interest Transmission
Reinforced path 8 21

31. Directed Diffusion
Multiple Sources
Multiple Sinks
Link Failure

32. Directed Diffusion Advantages: Disadvantages:
Energy: much less traffic than flooding & data aggregation
Latency: usually transmitting data along best path
Scalability: local interactions only
Robust: retransmission of interests and low data rate gradients
Disadvantages:
Gradient setup phase expensive
Retransmission of interests and alternate path maintenance required*
Not energy aware – all messages traverse the primary path

33. Routing Protocols for WiSeNets
Flooding
Gradient
Clustering and Cellular
Geographic
Energy-aware

34. Clustering Approach Cluster-based hierarchical approach
LEACH – Low Energy Adaptive Clustering Hierarchy [HCB00], [HCB02] Heinzelman, Chandrakasan, Balakrishnan
Cluster-based hierarchical approach
Nodes elect themselves to be cluster heads at the beginning of each round based on a probability function
Localized coordination for set-up
Cluster heads randomly rotated to increase network lifetime
Cluster membership adaptive
Members communicate with cluster head using TDMA MAC
Data aggregation at cluster heads
Cluster heads communicate directly with user
Time Division Multiple Access

35. Cluster Head Rotation:
LEACH
Cluster head
Cluster Head Rotation:
Round r
Round r + 1

36. LEACH Positive Negative
Energy: balances energy usage among nodes and allows nodes to shut down radios
Latency: only two hops to user
Straightforward: aggregate data at cluster head and send to user
Scalability: distributed hierarchical approach
Negative
Cluster head failure a problem
Cluster head selection questionable (LEACH-C)
Assumes all nodes capable of long range transmissions

37. Routing Protocols for WiSeNets
Flooding
Gradient
Clustering and Cellular
Geographic
Energy-aware

38. GEAR – Geographic and Energy Aware Routing [YGE01]
Greedy geographic query routing technique
Cost function based on destination location and neighbor node energies used to determine next hop
Improvement over Directed Diffusion’s ‘interest flooding’ technique
Less partitioning than GPSR [KK00]
Greedy Perimeter Stateless Routing
Restricted broadcast within sampling region
Greedy Perimeter Stateless Routing

39. Summary Routing Protocols
Critical elements of a sensor network protocol
Energy efficient
Energy usage distributed among nodes
Robust – fault tolerant
Scalable – distributed control & local interactions
Small footprint
Low latency
Questions
How to avoid draining sensor nodes near sinks?
How feasible is data aggregation beyond duplicate suppression?
Better way to categorize network models?
Good way to evaluate and compare protocols?
How effective are the protocols in the real world?

40. Collaborative Processing
Data acquired at single node do not have enough information for certain task (e.g. target localization/tracking), collaboration across nodes is required
Collaboration makes processing more robust to noise, interference
Collaboration can be optimized to involve no more nodes than necessary
Microphone network for tank tracking

41. Time and Location Each sensor node creates local observations.
Each observations is ‘tagged’ with the observation time and sensor location, otherwise the observation has little usefulness
All weather station data has station location (longitude, latitude) and observation time (YY:MM:DD:HH:MM) associated with it
Audio data for localization need to be more accurately time/location-stamped
Problems: Node localization and time synchronization between nodes

42. Time Synchronization Also crucial in other contexts
Ranging, tracking, security, MAC, aggregation etc.
Global time not always needed
New ideas
Local timescales
Receiver-receiver sync
Multihop time translation
Post-facto sync
Mote implementation
~10 s single hop
Error grows slowly over hops
Sender
Receiver
Receiver
NIC
I saw it
at t=4
NIC
NIC
I saw it
at t=5
Propagation Time
Physical Media 1 3 2 A 4 8 C 5 7 6 B 10 D 11 9
NIC:

43. Node Localization Snd_travel_time = Range/Snd_velocity
RF_travel_time = Range/RF_velocity
Snd_arrival_time – RF_arrival_time = Snd_travel_time – RF_travel_time
receiver RF
Sound
Sound vs. RF travel time
RF/Sound ranging system
RF & Sound signal are sent simultaneously, their arrival time are recorded by the receiver

44. Node Localization (II)
When range among many nodes is known, we can uniquely assign coordinates to each node relative to a reference point
(10,10)
(12,11)
(0,0)

45. Other topics Distributed Control and Signal Processing Tracking
Coverage and Security
Privacy
Emerging Standards (ZIGBEE)