Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks

1. Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks Abdelmajid Khelil, Hanbin Chang, Neeraj Suri IPCCC 2011

2. Y 1000 Maps 800 600 400 200 0 • Maps are an intuitive data representation technique • provide a visual representation of an attribute in a certain area; • street map, typographic map, world map, etc. • Maps for Wireless Sensor Networks (WSN) applications • help users to understand sensed physical phenomena • help users to make a decision 0 200 400 600 800 1000 X

3. Map Construction in WSN Sink Naive Approach Example of Available Approaches

4. Problem statement and Objectives Several approaches have been proposed. However, • Evaluation in carefully selected application scenarios • No assessment of the comparative effectiveness of existing approaches: Which is outperforming in Which application/scenario for Which network configuration?

5. Outline • Motivation • Classification of Existing Map Construction Approaches • Performance Comparison in a Wide Range Scenarios • Conclusions

6. Data Collection Scheme In-network Processing Technique Classification of Map Construction Approaches Map construction approaches for WSN Region Aggregation Data Suppression Tree-based data collection Cluster-based data collection Multi-path data collection Iso-node based data collection Cluster-based data collection eScan [9] CREM [7] INLR [16] Isolines [14] CME [19] Isobar [8] Iso-map [10,11] Contour Map [18]

7. m m+1m+2 Region Aggregation Class 37 Tree-based Cluster-based Ring-based • Basic idea • Sensor nodes are ordered hierarchically (clusters, tree ..) • Every sensor reports to a dedicated node (cluster head, parent ..) • Dedicated node aggregates adjacentsimilar data to regions • 3 Phases: Region Segmentation • At each sensor • Non-overlapping polygons • Vertex representation Data Collection • Aggregator determination Region Aggregation • At aggregator • Regions formation • Aggregation function, e.g. average

8. Data Suppression Class Isoline 36 41 42 38 42 43 37 41 45 Nodes suppress reports to the sink Nodes report to the sink • Basic idea • A subset of sensor nodes (iso-nodes) report their value to the sink • suppress similar data to be reported • 2 Phases Iso-node Identification • what is an iso-node? • has a neighbor with different value • how to identify? • broadcast • snoop Isoline Report Generation • iso-node based • generated at Iso-node • routed directly to the sink • cluster based • generated at cluster-head • Iso-node reports to cluster-head • a local map

9. Data Collection Scheme In-network Processing Technique Classification of Map Construction Approaches Map construction approaches for WSN Region Aggregation Data Suppression Tree-based data collection Cluster-based data collection Multi-path data collection Iso-node based data collection Cluster-based data collection eScan [9] CREM [7] INLR [16] Isolines [14] CME [19] Isobar [8] Iso-map [10,11] Contour Map [18]

10. Selected Map Construction Algorithms • The eScan approach [9] • Nodes ordered as an aggregation-tree • Polygon regions • Aggregation function: Average • The Isoline approach [14] • Local flood to label border nodes • Each iso-node reports to the sink • Map constructed at the sink [9] Y. Zhao et al. Residual Energy Scan for Monitoring Sensor Networks. In IEEE WCNC, 2002. [14] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005.

11. Outline • Motivation • Classification of Existing Map Construction Approaches • Performance Comparison in a Wide Range Scenarios • Conclusions

12. Evaluation Framework: Methodology • Selected map construction protocols • Region aggregation class: eScan • Data suppression class: Isoline • Simulations using OMNet++ • Network • Area : 300 x 300 m² • Topology: Grid or random • Tree-based routing protocol • Performance metrics • Map accuracy: The ratio of false classified sensors to all sensor nodes. • Energy efficiency: Network traffic

13. Evaluation Framework: Comparative Studies Compare for a wide range of parameters: • Impact of physical phenomena properties • Hotspot effect range : limited vs. diffusive • Hotspot number : 1 vs. n • Impact of protocol parameters • Sensor value range [0, 60], classes: [0, GV[, [GV, 2GV[ ... • Signal discretization (Granularity value: GV) GV=5…25 • Impact of network properties • Node densityN=256(16x16)...1225 (35x35) • Communication failures BER=0…10-2 • Communication range CR=60m

14. Comparison:Impact ofGranularity • Granularity increases • #Isolines and #Iso-nodesdecrease->lower msg overhead • Region size increase ->lower msg overhead • Accuracy • Isoline always outperforms eScan • Efficiency • Isoline outperforms eScan for lower granularities 5040302010 (a) Step value = 5 unit 50 25 (b) Step value = 25 unit

15. Comparison: Impact ofBER • BER increases • Loss of messages -> lower msg overhead • Overhead reduction is higher for eScan • Higher BER decreases map accuracy • Loss of messages -> gaps in the map • Higher accuracy drop for eScan

16. Comparison:Impact of Node Density • Node density increases • #Iso-nodesincreases-> higher msg overhead • #Region and “region border information” increase -> higher msg overhead • Node density has low impact on map accuracy • Region border precision increases -> provide a more detailed map

17. Conclusions Data suppression class • High accuracy with reliable comm. - Less suitable for less reliable comm. • high accuracy for reliable comm. • performs also well for less reliable comm. • accuracy increases with increasing granularity value Accuracy Region aggregation class • Small granularity value • Low density network - Small granularity value • low density network Efficiency

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