information brokerage and delivery to mobile sinks hyungjune lee, branislav kusy, martin wicke
Post on 22-Dec-2015
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TRANSCRIPT
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
Sensornet Meeting 3
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
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
Sensornet Meeting 11
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
Sensornet Meeting 12