1. Design Automation Lab. / SNU Sensor Network 1 2002. 4. 23 Design Automation Lab. Jung, Jinyong

2. 2 Design Automation Lab. / SNU Contents  m-Links Navigation model for very small internet devices  Exposure Formulation of coverage problem in sensor networks

3. 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

4. 4 Design 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

5. 5 Design Automation Lab. / SNU Introduction  Navigation model “browsing” = navigation + use

6. 6 Design 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

7. 7 Design 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

8. 8 Design Automation Lab. / SNU A Small-device Navigation Model Context of a link Link “overload”

9. 9 Design Automation Lab. / SNU Data Flow  m-Links is like Search engine Caching or transducing proxy

10. 10 Design Automation Lab. / SNU M-Links Architecture  Link Engine  Service Manager  UI Generator

11. 11 Design Automation Lab. / SNU Link Engine  Processing flow 1)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.

12. 12 Design Automation Lab. / SNU Link Engine  Link extraction and naming Link extraction Explicit:, 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

13. 13 Design 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

14. 14 Design 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

15. 15 Design 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

16. 16 Design 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

17. 17 Design 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

18. 18 Design 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

19. Design Automation Lab. / SNU Exposure In Wireless Ad-Hoc Sensor Networks MOBICOM 2001 Seapahn Meguerdichian, Frinaz Koushanfar, Gang Qu, Miodrag Potkonjak

20. 20 Design 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

21. 21 Design 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 p S to destination point p D.

22. 22 Design 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

23. 23 Design Automation Lab. / SNU Technical Preliminaries  Sensor field intensity and exposure All-sensor field intensity I A (F,p) Closest-sensor field intensity I C (F,p) Exposure during [t 1,t 2 ] along the path p(t)

24. 24 Design Automation Lab. / SNU Exposure  Simple case p S = p(1,0)  Lemma 1 q(0,1) p(1,0)

25. 25 Design Automation Lab. / SNU Exposure  Theorem 3

26. 26 Design Automation Lab. / SNU Exposure  Corollary 4

27. 27 Design Automation Lab. / SNU Exposure  Corollary 5

28. 28 Design 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

29. 29 Design Automation Lab. / SNU Generic Approach for Calculating Minimal Exposure Path  Finding minimal exposure path

30. 30 Design 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/d 2 (K=2), 1/d 4 (K=4) model I A, I C intensity models Data for 50 cases

31. 31 Design Automation Lab. / SNU Experimental Results

32. 32 Design Automation Lab. / SNU Experimental Results  Relative standard deviation

33. 33 Design Automation Lab. / SNU Experimental Results

34. 34 Design Automation Lab. / SNU Experimental Results

35. 35 Design 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