1. Diagnosing Wireless Sensor Networks through Wireless Mobile Nodes
-Junjie Xiong 1 1

2. Outline Motivation Mobile Node Aided Network Diagnosis
Preliminary Experiments
Emphasis on my own work 2 2

3. Outline Motivation Mobile Node Aided Network Diagnosis
Preliminary Experiments
Emphasis on my own work 3 3

4. Motivation  
The diagnosis of wireless sensor networks (WSNs) is performed at the base station (BS).
The log should be transmitted back to BS.
How to transmit as much log to the BS as possible and take up as few bandwidth as possible?
Emphasis on my own work 4 4

5. Motivation (Continue)
In Sympathy, nodes periodically send metrics back to the BS.
BS detects failures and determines their causes.
Sympathy limit its metrics to information about connectivity and data flow, such as nodes’ next hops and neighbors.
Emphasis on my own work 5 5

6. Motivation (Continue)
PassiveDiagnosis adds a few fields to the original packets that will be sent to the BS.
Using the information contained in the fields, the BS can infer the network problems.
Emphasis on my own work 6 6

7. Outline Motivation Mobile Node Aided Network Diagnosis
Preliminary Experiments
Emphasis on my own work 7 7

8. The Role of Wireless Mobile Node
It travels the WSNs to passively collect the packets sent or received by the sensor nodes.
It does not require the sensor nodes to send network diagnosis log back to the BS.
It does not send packets and hence does not interfere the WSN applications.
Emphasis on my own work 8 8

9. The Packets Collected by the Wireless Mobile Node
It is not all the packets generated by the whole network.
Can it represent all the packets?
Can it help detect anomalies in the network behavior?
Emphasis on my own work 9 9

10. Analyze the Packets Application …. Chameleon Rime XMAC 802.15.4
We do not know the abnormal behavior, so we use 1-class SVM to identify anomalies.
The features to be selected: the number of different types of packet sent or received.
DATA packet #, routing packet #, forwarded packet #, and so on.
The packets are collected from the radio frequency chip. Hence the packet contains all the upper protocol headers.
Application ….
Chameleon
Rime
XMAC
Emphasis on my own work 10 10

11. Outline Motivation Mobile Node Aided Network Diagnosis
Preliminary Experiments
Emphasis on my own work 11 11

12. Prepare Train and Test File for LibSVM
Run 50-node-network emulation in Contiki.
Emphasis on my own work 12 12

13. Prepare Train and Test File for LibSVM (Continue)
Collect raw packets from the radio frequency chip.
Parse the packets.
Calculate the ratios of different types of packets sent by each node.
Emphasis on my own work 13 13

14. Train and Test File for LibSVM
The train file is generated from the packets collected by the mobile nodes.
The test file is generated from all the packets sent and received in the WSNs.
Emphasis on my own work 14 14

15. Learn with One-class SVM
After running for 7 hours, we use the train file to build a model, and the test file to test the model. The accuracy is 76%.
How to select the features and tune the parameters?
Emphasis on my own work 15 15

16. Learn with One-class SVM (Continue)
Ongoing experiments: inserting a real bug in CTP and checking the results.
More experiments: Inserting more real bugs to verify the diagnosing method.
Emphasis on my own work 16 16

17. Thank you!
Emphasis on my own work 17 17