1. Sensor Network Routing – III Network Embedded Routing

2. Sensor Network Protocols
Taxonomy
Sensor Network Protocols
SPT, AODV, DSR
ID-Based
Data Centric DD
Geo-Routing
LAR, GPSR, GEDIR
Network Encoding
VGR, LCR, BVR, GEM
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Last Two Lectures
Today

3. Routing based on the content
What we have learned (1)
Routing based on the content
DD = + Naming
+ Dissemination
+ Gradient Establishment
+ Reinforcement

4. Routing based on location
What we have learned (2)
Routing based on location
GPSR = Greedy Forwarding +
Face Change (Perimeter )
GG/RNG Planarized Graph (Practical with CLDP)

5. The need for Point-to-Point Routing
Many-to-One is the dominating traffic pattern
Data Centric Storage 1. 2.
Active Query 3.
Directory Service
What else?
Storage
Active Query
Directory Services

6. Discussion the key points used by the authors to argue against
Shortest Path Routing ( distance vector routing)
Hierachical Routing
Geogrphic Routing
Data-Centric Routing

7. Shortest Path Algorithms
Use Dijkstra algorithm to compute shortest path between source and destination.
DSDV, distance vector, AODV
Scalability issue
In a network of n nodes. N x N messages exchanges would be needed to create tables and a size of N table entries are needed for routing.

8. Shortest Path Algorithms
Even in the case of Ad hoc on demand routing – table size is large function of network activity.
Sensor Networks contain 1000’s of nodes. Not enough memory to store the tables alone.
How about data-centric routing?

9. Hierarchical Addressing
But the Internet contains millions of nodes , how does distance vector work there ?
The answer lies in Hierarchy …
Provide some routing information in the destination address.
O(nlogn) message and O(logn) message.
Auto configuration ? Landmark routing
hierarchical set of landmark nodes, send scoped route discovery messages.
Nodes address is a concatenation of closest landmark address.
Suffers from complex landmark selection algorithm
Internet hard codes addresses, ad hoc networks cant.
Communication range in Wireless network is bounded.
Why Hierarchical Structure is
not suitable for wireless networks?

10. GPSR : Greedy Perimeter Stateless Routing
Node address is a GPS x , y coordinate.
Greedy Forwarding :
Next hop node, closer to destination than me
Problem of Local maxima

11. GPSR : Greedy Perimeter Stateless Routing
Method fails when edges cross.
Need Planarized Graph
GG and RNG planarization do not work if the UDG assumption does not hold
Geographic Routing Made Practical  
Young-Jin Kim, Ramesh Govindan, Brad Karp and Scott Shenker NSDI 2005

12. Summary of Limitations
Shortest Path
Scalability O(n2) message and O(n) routing state
Hierachical
Less message. O(nlogn) message and O(logn) message.
Maintainence issue.
Data-Centric Routing
Scalability O(nM) message and O(M) routing states
Geographic Routing
O(1) and O(1)
Valid of UDG assumption and locatization cost.
Directory service (Id-to-Id routing)

13. One papers
Beacon Vector Routing: Scalable Point-to-Point Routing in Wireless Sensornets
R. Fonseca,  S. Ratnasamy, J. Zhao,  C. Tien Ee and D. Culler, S. Shenker, I. Stoica
NSDI 2005

14. Introduction BVR Addressing scheme BVR Routing Scheme
Uses distance from set of nodes termed “Beacons” to represent the address of every node.
BVR Routing Scheme
Greedy Routing is performed by choosing a neighbor whose address is closer to the final destination.
Use scoped flooding to route away from local minima

15. Beacon Vector Routing in a nutshell
Borrow geographic routing scalability
Greedy routing, local state
Virtual coordinate space
Distances in hops to a set of reference nodes
Based on simple tree construction
stateless routing
node address must
contain routing information

16. Coordinate Establishment ( addressing )1 2 1
1,2,3
0,3,3
2,1,3
3,0,3
3,1,2
1,3,2
3,3,0
2,3,1
3,2,1
2,2,2 2 3 1 3 1 2
Tree is build rooted at each beacon; DV like propagation
Nodes know their positions, their neighbors positions, and how to get to the beacons 2 3 3
(Hop#1, Hop#2, Hop#3) B3

17. dist(<3,3,0>,<1,2,3>) =
Simple example
Route from 3,3,0 to 1,2,3 B1 B2
1,2,3
0,3,3
2,1,3
3,0,3
3,1,2
1,3,2
3,3,0
2,3,1
3,2,1
2,2,2
D=4
D=2
D=0
D=2
D=4
D=4
D=6
dist(<3,3,0>,<1,2,3>) =
(|3-1|+|3-2|+|0-3|) = 6 B3

18. Simple example Route from 3,2,1 to 1,2,3 B1 B2 D=4 D=2 D=0 D=4 D=4 D=2
0,3,3
2,1,3
3,0,3
3,1,2
1,3,2
3,3,0
2,3,1
3,2,1
2,2,2
D=4
D=2
D=0
Fallback towards B1
D=4
1,2,3
D=4
D=2
D=4
D=6
The transition to this slide from the previous one is to say
But there are cases in which no neighbor will improve our current distance, and we have to have a way of getting out of this local minimum... B3

19. Beacon Vector Routing Routing Set-up Data Forwarding Select Beacons
Get Address
Get Neighbors
Data Forwarding
Greedy Routing
Fall back mode routing
Scoped Flooding

20. Enhanced Routing Metrics
Move towards the beacons that are near to destination
Move away from the beacons that are faraway from destination
W(p,q) returns 10 * fn (Etx) when p > q , returns fn(Etx)
when p < q.
K number of beacons used for addressing
p prospective next hop / q destination
Retransmission 5 times and exploiting all possible neighbors

21. Destination 1,2,3
[ 3 – 1 ] * 10
[ 1 – 2 ] * 10
[ 2 – 3 ] * 1
Cost = 22
Cost 22
Destination 1,2,3
[ 2 – 1 ] * 10
[ 3 – 2 ] * 10
[ 1 – 3 ] * 1
Cost = 22
Cost 22
Destination 1,2,3
[ 3 – 1 ] * 10
[ 3 – 2 ] * 10
[ 0 – 3 ] * 1
Cost = 33
Destination 1,2,3
[ 3 – 1 ] * 10
[ 2 – 2 ] * 10
[ 1 – 3 ] * 1
Cost = 22
Cost 22
Cost 33

22. What are the key practical issues?
Discussion
What are the key practical issues?
Link Quality 1. 2.
Beacon Maintenance 3.
Node Failure
Directory Service 4
Link Quality
Beacon Maintenance
Node Failure
Directory Service

23. Issue I: Link Quality Estimation
Since Radios are not ideal and sporadic reception from far away nodes may fool the system to think they are closer.
All outgoing packets have sequence number, receiver can estimate link quality through numbers missed.
Top N (18) nodes with Link quality above L% (20) are considered neighbors and other packets are dropped.

24. Parent Selection
Every node also broadcasts the forward Pf and backward transmission Pr probability from its parent.
Estimated number of transmissions (ETX)
= 1 / ( Pf * Pr ), summated over all links from the Beacon to the node.
Parent node for the beacon is one with lowest Etx.

25. Issue II: Beacon Maintenance
Each beacon has a sequence number which is incremented periodically. Broadcast Beacon Id , Address
Node maintains a list of beacons
Sequence Number , beacon Address pair
If Sequence number not updated => delete pair
Elect Beacons
At timer intervals function of Sequence ID check beacon table size.
if size < r elect self as beacon
if node is a beacon and more than r beacons exist with sequence id smaller than mine , stop being beacon.

26. Set of Beacons used for address, need not be consistent across nodes.
Select Beacons
Get Address
Get Neighbors
Route Using Distance
Fall back mode routing
Node Address consist of a vector of distances from k beacons and a unique ID.
Hop from beacon
Beacon Sequence Number
Hop from beacon
Beacon Sequence Number
Hop from beacon
Beacon Sequence Number
K entry vector
Unique ID
Set of Beacons used for address, need not be consistent across nodes.

27. Issue III: Node Failure
Soft-State
Beacons periodic refresh the network
A key problem exists!!!
A node could change address
because other node’s failure

28. Issue VI: Routing to Node ID
Beacon-Vector routes to node positions; need a lookup mechanism to map node identifiers to positions [GLS, GHT]
Our solution: Use beacons to store mapping
Given a node identifier, use consistent hashing to determine which beacon stores its position
Simple, but imposes additional load on beacons
BV+Lookup enables routing to any node identifier (IP-like)

29. Discussion: Metrics If you were the author, what’s metrics
you will choose and why?
Performance metrics
success rate without flooding
path stretch – Ratio of length using BVR to other greedy Algo.
Node load
Overhead
total #beacons needed (flooding overhead)
#beacons used for routing (per-packet overhead)
#neighbors (per-node routing table)
Scalability
network size
network density

30. Evaluation Realism Scale (nodes) High Level Simulator
Real Implementation
Mica2 Testbeds
Low level simulator
TOSSIM
High Level Simulator
Algorithmic issues
Scale
Perfect Radios
Realism
Scale (nodes)
Figure from Elson et al., 2003

31. Greedy performance % routes with no flooding
Algorithmically 100%.
Greedy/flood
And in the paper we have results on how this scales (#beacons)
Not many beacons.
High Level simulator, 3200 nodes, random node placement, random beacon placement
Density: 16 neighbors/node. Load: 32,000 random-pair routes

32. Path Efficiency
High Level simulator, 3200 nodes, random node placement, random beacon placement
10 routing beacons. Density: 16/10 neighbors/node. Load: 32,000 random-pair routes

33. Performance
Real implementation, 40 nodes, 20x50m office space, 5 beacons at edges
Avg density: 12, random-pair routes

34. Conclusion Beacon-Vector performance Other results
Overhead scales well with network size and density
Outperforms true geography at lower densities
Other results
On demand 2-hop neighbors is a big win, specially on lower densities
obstacles: up to 40% improvement relative to true positions

35. Discussion: The limitation of BVR
Node address is unstable
Directory Service is needed for ID-Based Routing
Cost is higher than Geo-based Routing, due to beacon flooding
Network Load does not figure in routing decision
Scoped Flooding would be high overhead when beacon number is small. (see alterative approach LCR)
Power hungry as nodes have to listen always. Not a stealthy routing system either.
Path is not optimal. (Greedy Geo?)

36. Discussion What are the key difference between ID-Based Routing
Geo-Graphic Based Routing
Data-Centric Routing
Network Embedded Routing

37. Geographic Based Routing Network Encoding Based
Which one to use?
Data Centric Routing
ID-Based Routing
Geographic Based Routing
Infrastructure cost
Control Cost
Data Forwarding Cost
Scalability
Robustness
Energy (#msg)
Network Encoding Based

38. Thanks