1. Frequent Itemsets Mining in Distributed Wireless Sensor Networks Manjunath Rajashekhar

2. Motivation Sensor network: –Battery powered, wireless communication –Limited RAM (10K – 32M), large flash (512MB – 1GB) –Communication over wireless –Speed (4MHz – 40MHz) Centralized Distributed I/O Communication Different Data Rates –Can think of data as baskets? –Data is not uniform distributed across all nodes! Trivial solution

3. Algorithm (1) Preprocessing –Each node sends {node-id, #baskets-count} to the base station Sampling –Query the network to collect the random sample Generation of Frequent Itemsets –Apriori algorithm –Scaled threshold

4. Algorithm (2) Verification of Frequent Itemsets –Eliminate False Negatives Negative Border Aggregate counts of negative border over the network Fails: Repeat the whole algorithm –Eliminate False Positives Aggregate counts of frequent itemsets over the network

5. Experiments Setup: # Nodes = 100 # Baskets = 10400, baskets are distributed non-uniformly across nodes. Threshold scaling factor = 0.9 Support threshold = 25% Synthetic dataset Values averaged over 100 trials. ~73 % saving in communication Insights?

6. Backup slides

7. Analysis Preprocessing: (C1) –size-of {node-id, count} * # nodes * cumulative-communication-distance Sampling (C2) –average-size-of-baskets * size-of-random-sample * cumulative- communication-distance False Negatives (C3) –size-of-negative-border * # nodes * aggregation-distance False Positives (C4) –size-of-frequent-itemsets * # nodes * aggregation-distance Total Cost = C1 + C2 + C3 + C4