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Presented by Jing Sun Computer Science and Engineering DepartmentUniversity of Conneticut

+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◦ 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

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

),(

)()(),(distqkCi

iiik qBpBqp

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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

B3

1,2,3

0,3,3

3,0,3

2,1,2

1,3,2

3,3,0

2,3,13,2,1

2,2,2

Fallback towards B1

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

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◦ 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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