1. More routing protocols Alec Woo June 18 th, 2002

2. Motivation Empirical data shows a probabilistic model of packet reception over distance –Long links –Asymmetric links A routing tree using poor link “disconnects” the tree Make sense to choose a routing parent using good neighbors with good link quality - high probability of packet reception –Feedback from how good your neighbors can hear you –Is that adequate? Packet Reception Probability 100% distance

3. Local Link Estimation Parent Selection 10% 90% 60% Base Station

4. Estimating Path Success Path success estimation can be built by multiplying each individual packet reception probability along the path Many to one routing –Pick the path with the highest probability of success in reaching the base station Simple proof can show that this is basically a shortest path problem –By turning products into sums

5. The Maximum Reliable Path Tree 20 nodes in a 30”x30“ space with communication radius of 20 feet. Assume Link Quality(distance), generate G and compute all link qualities

6. In Reality Don’t know the real underlying link qualities Estimations have errors Collisions can affect estimations Overloading the network should be avoided Approximate the minimum path loss tree –Online, distributed approach with each node maintaining neighbor link qualities and path estimations to base station –Low duty cycle periodic exchange of link qualities and path estimates among neighbors Rate of adaptation depends on this knob

7. Beacon Based Routing Simulation Using the same graph as before, running apps/router/router in TOSSIM

8. Distributed MRP Simulation With sample size = 80, estimation error +/- 10%, 20 nodes, with 5 hops in the network Data Generation Rate: 5 s/node/message Estimations exchange rate: 15 s/node/exchange In ~40 link exchanges, we get

9. Centralized Approach With +/- 10% error on the estimations, similar route deviations are also observed

10. Phase Approach –Connectivity discovery –Route computation –Sample, communicate, sleep Collision free by ensuring one transmitter in the entire network –Decouple tree building from link estimations –If link estimations are fixed, our distributed algorithm will converge to the tree fairly quickly Adaptation rate depends on frequency of connectivity discovery

11. Future Work Evaluation –Knobs: data rate link exchange rate => (tree propagation rate) number of neighbors to feedback on link statistics Link estimator MAC layer –Aggregate bandwidth –Stability vs. Agility over network changes Aging neighbors –Congestion control Automatic adaptation data rate –Hop Count Simulation in TOSSIM, Matlab, and Empirical