FIND: Faulty Node Detection for Wireless Sensor Networks Shuo Guo, Ziguo Zhong and Tian He y of Minnesota University
Sensys 09 Presenter: Jing He
Background Two types yp of faults • Function fault Crash of nodes, packet loss, routing failure or network partition
• Data D t ffaultlt Behaves normally except for sensing results
2
Related Work Outlier detection • Identify readings numerically distant from the rest
After-deployment p y calibration • Find a mapping function that maps faulty readings into correct ones (Y=aX+b)
Limitations • Assumptions on data distribution • Mapping functions may not exist
3
Our Work Objective: j find a blacklist of p possible faulty y nodes, in order of their probability of being faulty • Node locations are available • Generally monotonic sensing readings over distance • No longer assume any mathematical model for reading-distance relationship • No longer assume any function to map faulty readings into correct ones • Detect both random and biased faulty readings 4
Preliminary Experiments EVENT 1
RSS vs. Distance 49 sensor nodes, 49 events
5
Preliminary Experiments EVENT 1
EVENT 2 RSS vs. Distance 49 sensor nodes, 49 events
6
Preliminary Experiments EVENT 1 Average of 49 Events
EVENT 2 RSS vs. Distance The monotonicity assumption is more accommodating to real world environments than the assumption based on a more specific model. 7
Node Sequences and Ranks Node sequence q
RSS: Received Signal g Strength g
• A complete node list of node IDs sorted by reading (e.g., RSS), or physical distance from targets
Rankings • The rank a node appears in a node sequence
1
physical distance-based sequence:1243
2
-50dBm -55dBm -62dBm
4
-60dBm
3 -65dBm
RSS-based RSS based node sequence: seq ence 1243 2413 Node 1’s ranking in 1243 is 1 Node 1’s 1 s ranking in 1243 is 1 Node 1’s ranking in 2413 is 3
Ranking g Difference 8
Main Idea Find mismatch between RSS-based and physical distance-based rankings 1
2
4
3
E ents Distance RSS Events
1243 2314 4123
Ranking g Difference
2431 2341 4213 Total:
1 2 3
4
3 1 1 5
1 1 0 2
1 0 1 2
1 0 0 1
1 Unknown 1. Unkno n target locations? Estimate distance sequences from RSS-based sequences? 2. Why ranking difference? 3. How many nodes are faulty, given ranking differences? 9
Sequence Estimation Estimate p physical y distance-based sequence q sˆ from RSS-based sequences s • Map p Division: find = consisting g of all possible distance-based sequences • Maximum A Posterior (MAP) estimation
N-node Network
N! Possible Given Topology Sequences small subset O(N4)
sˆ
s 10
Map Division Divide map p into subareas identified by y a unique q node sequence indicating distance information
2
distance-based sequence
2 1
1 distance-based sequence q
1 2 11
Map Division Divide map p into subareas identified by y a unique q node sequence indicating distance information
