Download - Design Automation Lab. / SNU Sensor Network 1 2002. 4. 23 Design Automation Lab. Jung, Jinyong
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Design Automation Lab. / SNU
Sensor Network 1
2002. 4. 23
Design Automation Lab.
Jung, Jinyong
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2Design Automation Lab. / SNU
Contents
m-Links Navigation model for very small internet devices
Exposure Formulation of coverage problem in sensor networks
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Design Automation Lab. / SNU
m-Links: An Infrastructure for Very Small Internet Devices
MOBICOM 2001
Bill N. Schilit, Jonathan Trevor,
David M. Dilbert, Tzu Khiau Koh
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4Design Automation Lab. / SNU
Introduction
Mobile Link (m-Links) infrastructure Utilizing existing WWW contents and services on very
small devices
Approaches to Device-independent Access Device-specific authoring Multiple-device authoring Client-side navigation Automatic re-authoring
• Digestor
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5Design Automation Lab. / SNU
Introduction
Navigation model “browsing” = navigation + use
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6Design Automation Lab. / SNU
Design Goals
Web navigation Culling the links from the content
Get a useful bits of information Data detector
Maximize program/data composibility Link’s MIME type
Open Extensibility Re-use existing web-based services
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7Design Automation Lab. / SNU
A Small-device Navigation Model
Navigation model “dig and go” model
Issues Determining sensible labels for Web links Context of a link Dealing with “link overload” Data detect Open system design
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8Design Automation Lab. / SNU
A Small-device Navigation Model
Context of a link
Link “overload”
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9Design Automation Lab. / SNU
Data Flow
m-Links is like Search engine Caching or transducing proxy
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10Design Automation Lab. / SNU
M-Links Architecture
Link Engine Service Manager UI Generator
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11Design Automation Lab. / SNU
Link Engine
Processing flow1) The document is loaded from internet.
2) HTML parser creates a parse tree.
3) Text elements are scanned by data detectors and new links are created.
4) The links are categorized
5) Each link is added to the page’s link collection.
6) Link collection data structure is stored in a cache.
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12Design Automation Lab. / SNU
Link Engine
Link extraction and naming Link extraction
• Explicit: <A>, <AREA>
• Data detected Link naming algorithm
• Concise and meaningful text label for the link
• Quality value• Title > anchor text, alt-text, .. > URL
• Check the uniqueness of the label
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13Design Automation Lab. / SNU
Link Engine
Link categorization Off-site Navigation Based on MIME type Based on layout characteristics
Link cache Caching Web pages processed Similar manner to those used by search engines
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14Design Automation Lab. / SNU
Service Manger
Returning the subset of services appropriate for a link and user
General service, Content provider service Check
• MIME type, characteristics of device, user’s indentity Submit HTTP request to the appropriate web server.
Defining and extending services Service specification document
• XML-based
• Rule section, execution section, presentation section
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15Design Automation Lab. / SNU
User Interface Generator
Supporting a variety of different UI HDML and WML for web-phones HTML for palm-size PDA
Template markup files Generates the variable values
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16Design Automation Lab. / SNU
Services
Reading Extracts content from a type of file and presents it in a
device-specific manner.
Sending Email, WAP-alert service
Printing Printing, fax service
Mapping Yahoo on-line mapping service
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17Design Automation Lab. / SNU
Implementation and Experience
Implementation of the m-Links Java servlet engine Microsoft’s IIS web server
Problem Web pages contain client-side scripts Not severe
• Authors provide “hidden” or extra links for non-script browsers
• Many sites provide alternative pages
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18Design Automation Lab. / SNU
Conclusions
Propose the navigation model for very small devices m-Links system addresses design goal:
Supporting web navigation Getting useful bits of information Maximizing program-data composibility through a
separation of service from link Providing open framework
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Design Automation Lab. / SNU
Exposure In Wireless Ad-Hoc Sensor Networks
MOBICOM 2001
Seapahn Meguerdichian, Frinaz Koushanfar,
Gang Qu, Miodrag Potkonjak
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20Design Automation Lab. / SNU
Introduction
Calculation of coverage is fundamental problems in sensor networks
Coverage problems Art Gallery Problem Sensor coverage for detecting ocean color Coverage studies to maintain connectivity formulation of coverage
• Maximal breach, maximal support path
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21Design Automation Lab. / SNU
Introduction
Exposure A formulation of coverage in sensor network Expected average ability of observing a target in the
sensor field. An integral of a sensing function that generally depends
on distance from sensors on a path from a starting point path pS to destination point pD.
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22Design Automation Lab. / SNU
Technical Preliminaries
Sensor models Sensing ability diminishes as distance increase. Sensing ability can improve as the exposure increase.
S : sensing model, s : sensor d(s,p) : Euclidean distance bet’n the sensor s and the
point p
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23Design Automation Lab. / SNU
Technical Preliminaries
Sensor field intensity and exposure All-sensor field intensity IA(F,p)
Closest-sensor field intensity IC(F,p)
Exposure during [t1,t2] along the path p(t)
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24Design Automation Lab. / SNU
Exposure
Simple case pS = p(1,0)
Lemma 1
q(0,1)
p(1,0)
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25Design Automation Lab. / SNU
Exposure
Theorem 3
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26Design Automation Lab. / SNU
Exposure
Corollary 4
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27Design Automation Lab. / SNU
Exposure
Corollary 5
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28Design Automation Lab. / SNU
Generic Approach for Calculating Minimal Exposure Path
Finding the exposure path under arbitrary sensor and intensity models is an extremely difficult.
Divide sensor network region n x n, m-th-order
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29Design Automation Lab. / SNU
Generic Approach for Calculating Minimal Exposure Path
Finding minimal exposure path
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30Design Automation Lab. / SNU
Experimental Results
Simulation platform Sensor field is defined as a square, 1000m wide. Assume constant speed
Uniformly distributed random sensor deployment n=32, m=8 1/d2 (K=2), 1/d4 (K=4) model IA, IC intensity models Data for 50 cases
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31Design Automation Lab. / SNU
Experimental Results
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32Design Automation Lab. / SNU
Experimental Results
Relative standard deviation
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33Design Automation Lab. / SNU
Experimental Results
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34Design Automation Lab. / SNU
Experimental Results
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35Design Automation Lab. / SNU
Conclusion
Calculation of exposure is one of fundamental problem in wireless ad-hoc sensor networks
Introduced the exposure-based coverage model Presented efficient algorithm for minimal exposure
paths Performance and worst-case coverage analysis tool
in sensor networks