IPCCC’111 Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks Abdelmajid Khelil, Hanbin Chang, Neeraj Suri IPCCC 2011

IPCCC’112 Maps  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 Sensor locationSensor value (112, 209)145 (218, 163)163 (617, 783)158 (530, 745)163 (477, 625)165 (936, 423)157 (745, 817)155 (653, 237) X Y

IPCCC’113 Sink Map Construction in WSN Naive approach for map construction Energy-efficient approaches for map construction Data collection and processing centrally at sinkin-network Energy efficiency (Comm. complexity on sensor nodes) high comm. overhead Lower comm. overhead Map accuracy node-level accuracy, may decrease because of comm. failures may lose detailed information of each individual node Naive Approach Example of Available Approaches

IPCCC’114 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?

IPCCC’115 Outline  Motivation  Classification of Existing Map Construction Approaches  Performance Comparison in a Wide Range Scenarios  Conclusions

IPCCC’116 Data Collection Scheme Classification of Map Construction Approaches Map construction approaches for WSN Region Aggregation Data Suppression Tree-based data collection eScan [9] Isobar [8] Iso-node based data collection Cluster- based data collection Isolines [14] Iso-map [10,11] Contour Map [18] CME [19] Cluster- based data collection CREM [7] Multi-path data collection INLR [16] In-network Processing Technique

IPCCC’117 Region Aggregation Class  Basic idea  Sensor nodes are ordered hierarchically (clusters, tree..)  Every sensor reports to a dedicated node (cluster head, parent..)  Dedicated node aggregates adjacent similar 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 m m+1 m+2 Tree-based Cluster-basedRing-based

IPCCC’118  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 Data Suppression Class Isoline Nodes report to the sink Nodes suppress reports to the sink

IPCCC’119 Data Collection Scheme Classification of Map Construction Approaches Map construction approaches for WSN Region Aggregation Data Suppression Tree-based data collection eScan [9] Isobar [8] Iso-node based data collection Cluster- based data collection Isolines [14] Iso-map [10,11] Contour Map [18] CME [19] Cluster- based data collection CREM [7] Multi-path data collection INLR [16] In-network Processing Technique

IPCCC’1110 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, [14] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005.

IPCCC’1111 Outline  Motivation  Classification of Existing Map Construction Approaches  Performance Comparison in a Wide Range Scenarios  Conclusions

IPCCC’1112 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

IPCCC’1113 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) (35x35)  Communication failuresBER=0…10 -2  Communication rangeCR=60m

IPCCC’1114  Granularity increases  #Isolines and #Iso-nodes decrease -> lower msg overhead  Region size increase -> lower msg overhead  Accuracy  Isoline always outperforms eScan  Efficiency  Isoline outperforms eScan for lower granularities (b) Step value = 25 unit Comparison: Impact of Granularity (a) Step value = 5 unit

IPCCC’1115 Comparison: Impact of BER  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

IPCCC’1116 Comparison: Impact of Node Density  Node density increases  #Iso-nodes increases -> 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

IPCCC’1117 Conclusions Region aggregation class 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 ＋ Small granularity value ＋ Low density network - Small granularity value ＋ low density network Accuracy Efficiency

IPCCC’1118 Thanks for Your Attention!