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ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks

ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks. Bugra Gedik Ling Liu Philip S. Yu IEEE Transactions on Parallel and Distributed Systems 2007. Introduction. Sensors are power constrained Event-based data collection Periodic data collection

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ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks

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  1. ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks BugraGedik Ling Liu Philip S. Yu IEEE Transactions on Parallel and Distributed Systems 2007

  2. Introduction • Sensors are power constrained • Event-based data collection • Periodic data collection • Model-based data collection • Intra-node modeling • Inter-node modeling

  3. System Model (1/2) • Network Architecture • A sensor node , where • Each node only communicates with its neighbors • Sensor nodes use a data collection tree for the purpose of propagating their sensed value to base node • Partition the network into disjoint clusters • Further partitioning each clusterinto subclusters

  4. System Model (2/2)

  5. Sensing-driven Cluster Construction (1/3) • Cluster head selection • Every node is initialized not to be a cluster head and does not have an associated cluster • Head selection probability • Cluster count factor : the average fraction of nodes that will be selected as cluster heads • Relative energy level : energy available at node at time

  6. Sensing-driven Cluster Construction (2/3) • Cluster formation • Message circulation • Cluster head will prepare a message m circulated within a bounded number of hops • stores the shortest hop from to • stores the mean of sensor reading from • Cluster Engagement • Attraction score Hop distance Data distance

  7. Sensing-driven Cluster Construction (3/3) • Effects of on Clustering • A color image representsthe change of readings A limited TTL Cluster-connection tree

  8. Correlation-based Sampler Selection (1/3) • Subclustering • Forced sampling • Collect sensor readings from all nodes in its cluster periodically. • denotes the column vector of consecutive forced samples. • Correlation Matrix and Distance Metric • Correlation matrix • Correlation distance matrix • Use agglomerative clustering to subcluster sensor nodes within into number of subclusters. ,

  9. Correlation-based Sampler Selection (2/3) • Sampler Selection • At least one node is selected as sampler • Nodes with more remaining energy are preferred • The number of samplers can be calculated by

  10. Correlation-based Sampler Selection (3/3) • Model and Schedule Reporting • Inform each node its new status • Send summary information to the base node • Data mean vector • Data covariance matrix • Effects of on Performance • Both large and small may decrease the prediction quality and increase the messaging cost

  11. Model-based Prediction • Prediction Model • The values of non-sampler nodes are predicted by probabilistic model • Forced samples do not propagate up to the based node Non-sampler Sampler : desired sampling period : forced sampling period

  12. Model-based Prediction • Calculating predicted sensor values • ,

  13. Evaluations

  14. A. Messaging Cost

  15. B. Energy Consumption

  16. C. Data Collection Quality(1/3) • 1000 sensor nodes in a square grid with side length of 1 unit • Transmission range 0.075 unit • TTL = 5

  17. C. Data Collection Quality(2/3) Fixed : system supplied Effective :

  18. C. Data Collection Quality(3/3) • Only messaging and sensing consume energy • Negative MAD implies the network exceeds its lifetime

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