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Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks. Abdelmajid Khe l il , Hanbin Chang , Neeraj Suri IPCCC 2011. Y. 1000. Maps. 800. 600. 400. 200. 0. Maps are an intuitive data representation technique
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Assessing the Comparative Effectiveness of Map Construction Protocols in Wireless Sensor Networks Abdelmajid Khelil, Hanbin Chang, Neeraj Suri IPCCC 2011
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
Map Construction in WSN Sink Naive Approach Example of Available Approaches
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?
Outline • Motivation • Classification of Existing Map Construction Approaches • Performance Comparison in a Wide Range Scenarios • Conclusions
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]
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
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
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]
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.
Outline • Motivation • Classification of Existing Map Construction Approaches • Performance Comparison in a Wide Range Scenarios • Conclusions
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
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
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
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
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
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