Information Brokerage and Deliveryto Mobile Sinks

HyungJune Lee, Branislav Kusy, Martin Wicke

Motivations

• How to forward relevant data to mobile sinks with hard latency constraint?– Use two-tier architecture1) Exploit stationary node

networks to forward packets with reliability

2) Track mobile nodes by using nearby stationary nodes

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Evaluation of two-tier architecture

• Evaluation setting– Field size: 220 x 220 m2

– 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

Average Latency, Packet Delivery Ratio, Packet Overhead

Sensornet Meeting 4

Cluster-wide flood

802.11

Easy to implement, but inefficient!!!!

CTP tree maintained in each cluster, cluster-head is CTP sink

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!

Problem: routing in the same cluster

802.11

Shorter route may exist

Cluster head becomes a bottleneck

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

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

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.

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

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RSSI

Neighbor 1

Neighbor 2

Neighbor 3

Estimation of the nearest neighbor

• Evaluation setting– Field size: 120 x 120 m2

– 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

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Path Prediction

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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