1. Information Brokerage and Delivery to Mobile Sinks HyungJune Lee, Branislav Kusy, Martin Wicke

2. Motivations How to forward relevant data to mobile sinks with hard latency constraint? –Use two-tier architecture 1) Exploit stationary node networks to forward packets with reliability 2) Track mobile nodes by using nearby stationary nodes Sensornet Meeting 2

3. 3 Evaluation of two-tier architecture Evaluation setting –Field size: 220 x 220 m 2 –Transmission range of 802.11b : 500 m –Transmission range of 802.15.4 : 30 m –# of static nodes: 100 –# of mobile nodes: 10 (This is set to stationary for now) –# of cluster-heads: 9 –Total # of nodes: 119 –Measure average latency, packet delivery ratio and packet overhead

4. Average Latency, Packet Delivery Ratio, Packet Overhead Sensornet Meeting 4

5. Cluster-wide flood 802.11 Easy to implement, but inefficient!!!! CTP tree maintained in each cluster, cluster-head is CTP sink

6. Maintain back route 802.11 CTP tree maintained in each cluster, cluster-head is CTP sink Route to the mobile node is maintained using beacon pckts beacon pckt collects path info 1 route is kept Beacon pckts are periodically broadcasted, thus back route remains reliable Much lower streaming overhead!

7. Problem: routing in the same cluster 802.11 Shorter route may exist Cluster head becomes a bottleneck

8. Solution 1: CTP redirection Most of the time, the route length increase is negligible (we have small clusters) The rest of the cases: cluster head can detect where the 2 routes join and set a marker to redirect the traffic

9. Solution 2: two CTP trees Set up a new CTP tree with the mobile node being the sink More overhead to maintain two CTP trees But the performance is as good as the point to point routing… 802.11

10. Best neighbor prediction Problem 1: how to fix the data structure (back route vs CTP) Problem 2: how to re-route packets until data structure gets fixed Hope to efficiently solve these, by predicting the next neighbor of the mobile node at cluster head.

11. RSSI-based vs. Location-based Location-based prediction –RADAR: IEEE Infocom’00 –Nibble: UbiComp’01 –Distance != Connectivity RSSI-based prediction –RSSI value can provide the link status information –Locally weighted linear regression –Gaussian process regression Bayesian learning technique Sensornet Meeting 11 RSSI Neighbor 1 Neighbor 2 Neighbor 3

12. Estimation of the nearest neighbor Evaluation setting –Field size: 120 x 120 m 2 –Wireless PHY/MAC: 802.15.4 –Transmission range: 30 m –Propagation model Shadowing model –Mobility model Random waypoint mobility model Max speed: 5 m/s, pause time: 10 sec –# of mobile nodes: 20 –Beacon period: 5 sec –Check whether the real closest node at a given time resides in the best k neighbors where k=1, 2, and 3 Sensornet Meeting 12

13. Path Prediction Sensornet Meeting 13

14. Path Prediction From the RSSI graph, can we tell which path will be taken? –Extract typical RSSI graphs –Using partial information, generate most likely complete RSSI graph Subspace projection Discrete selection –Use reconstructed RSSI graph to select best relay node Sensornet Meeting 14