Presented by Jing Sun
Computer Science and Engineering Department
University of Conneticut
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 +Highly scalable
 O(1) route discovery
 O(1) routing table
 Path lengths are close to the shortest path
 -Each node should node its geographic coordinates
 -Greedy forwarding can be suboptimal because it does
not use real connectivity info.
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◦ Simple – minimal complexity, with
minimal assumptions about radio quality,
presence of GPS, …
◦ Scalable – low control overhead, small
routing tables
◦ Robust – node failure, wireless vagaries
◦ Efficient – low routing stretch
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
4 pieces
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◦
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
Deriving positions
Forwarding rules
Beacon Maintenance
Lookup: mapping node IDs  positions
Used from other work:
◦ Reverse path trees construction (Directed Diffusion)
◦ Consistent hashing to map node identities to its
current coordinates
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


Randomly select nodes as beacons. The beacon
vectors serve as coordinates
r beacon nodes (B0,B1,…,Br) flood the network;
P(q), a node q’s position, is its distance in hops to
each beacon
P(q) =  B1(q), B2(q),…,Br(q) 

k, p, C(k,p), Node p advertises its coordinates using
the k closest beacons
(we call this set of beacons
C(k,p))

Nodes know their own and neighbors’ positions

Nodes also know how to get to each beacon
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1.
Define the distance between two nodes P and Q as
dist k ( p, q) 

iC ( k , q )
i
Bi ( p)  Bi (q)
2.
To reach destination Q, choose neighbor to reduce distk(*,Q)
3.
If no neighbor improves, enter Fallback mode: route towards
the beacon which is closer to the destination
4.
If Fallback fails, and you reach the beacon, do a scoped flood
The sum of the differences for the beacons that are closer to the
destination d than to the current routing node p
The sum of the distances to the farther beacons
We want to minimize:
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B1
B2
0,3,3
3,0,3
1,2,3
2,1,2
1,3,2
2,2,2
Fallback towards B1
2,3,1
3,2,1
3,3,0
B3
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
Route based on the beacons the source and
destination have in common
◦ Does not require perfect beacon info.

Each entry in the beacon vector has a
sequence number
◦ Periodically updated by the corresponding beacon
◦ Timeout

If the #beacons < r, non-beacon nodes
nominate themselves as beacons
◦ Set a timer that is a function of its unique ID

If there’re more than r beacons
◦ A beacon stop being a beacon if there’re more than
r beacons with smaller IDs
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




First look up the destination coordinates by
name
Hashing H: nodeid → beaconid [14]
◦ Use beacons as storage
Each node k that wants to be a destination
periodically publishes its coordinates to its
corresponding beacon bk = H(k)
When a node wants to route to node k, it
sends a lookup request to bk
Cache the coordinates
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

Assumptions for high level simulation
◦ Fixed circular radio range
◦ Ignore the network capacity and congestion
◦ Ignore packet losses
Place nodes uniformly at random in a
square planner region
◦ 3200 nodes uniformly distributed in a 200 * 200
unit area
◦ Radio range is 8 units

Vary #total beacons and #routing beacons
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
At lower densities, each node has fewer immediate
neighbors
◦ The performance of greedy routing drops
◦ Add a neighbor’s neighbors to the routing table,
if greedy forwarding is impossible
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
Place horizontal & vertical walls with lengths
of 10 or 20 units when the radio range is 8
units. BVR (True Positions)
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
Office-Net: 42 mica2dot motes in a 20m *
50m office
Univ-Net: 74 mica2dot motes deployed
across multiple student offices on a single
floor in a UC Berkeley building
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Thank you!
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Route from 3,2,1 to 1,2,3
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
Shortest Path
◦ Scalability O(n2) message and O(n) routing state

Hierachical
◦ Less message. O(nlogn) message and O(logn) message.
◦ Maintainence issue.

Geographic Routing
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O(1) and O(1)
Assumes fixed radio range
Require each node knows its geographic coordinates
Doesn’t consider real radio connectivity
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