1. - 1 - Location Sensing Techniques and Applications National Chiao Tung University Department of Computer Science Yu-Chee Tseng 2007/09/07

2. - 2 - k-Placement (IEEE TPDS*) My Research Roadmap on WSN WSN Localization Location Tracking & Deployment Comm. Protocol Applications & Systems Signal Scrambling (IEEE TKDE*) Data Clustering (MASS 2007) Beacon Movement (VTC 2007) Location Management (IEEE TMC, IJSN) Placement (IEEE TMC) Connectivity and Placement (ACM ToSN) ConvergeCast (MobiWAC 2006) Orphan Problem (MSWiM 2007) Emergency Guiding (IEEE Computer) 3D Emergency Guiding (IJSN) Surveillance: iMouse (IEEE Computer) Energy Saving: iPower (IJSNET*) GeoAds (MASS 2007) Location Tracking & Deployment Comm. Protocol Applications & Systems Location Management (IEEE TMC, IJSN) Placement (IEEE TMC) Connectivity and Placement (ACM ToSN) ConvergeCast (MobiWAC 2006) Orphan Problem (MSWiM 2007) Emergency Guiding (IEEE Computer) 3D Emergency Guiding (IJSN) Surveillance: iMouse (IEEE Computer) Energy Saving: iPower (IJSNET*) GeoAds (MASS 2007)

3. - 3 - Pattern-Matching Localization Overview   1   2.   n Location Database Pattern-Matching Localization Algorithm Training PhasePositioning Phase avg. signal strength: [  i,1,  i.2,…,  i.m ] training data signal strength vector: [s 1, s 2, …, s m ] s s real-time data training location access point (AP) ii 11 ii

4. - 4 - Challenges with Pattern-Matching Localization Unstable signal strengths and unpredictable multipath effect High computation cost: huge location database to match, especially in large-scale environments Environment changes and training cost Maintenance (movement/lost of beacons) Publications  S.-P. Kuo, B.-J. Wu, W.-C. Peng, and Y.-C. Tseng, "Cluster-Enhanced Techniques for Pattern- Matching Localization Systems", IEEE Int'l Conf. on Mobile Ad-hoc and Sensor Systems (MASS), 2007  S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "Increasing Search Space for Pattern-Matching Localization Algorithms by Signal Scrambling ", IEEE Asia-Pacific Wireless Communications Symposium, 2007.  S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "A Scrambling Method for Fingerprint Positioning Based on Temporal Diversity and Spatial Dependency", IEEE Trans. on Knowledge and Data Engineering, submitted.  S.-P. Kuo, H.-J. Kuo, Y.-C. Tseng, and Y.-F. Lee, "Detecting Movement of Beacons in Location- Tracking Wireless Sensor Networks", IEEE VTC, 2007-Fall.

5. - 5 - Localization: Signal Scrambling A Scrambling Method for Pattern-Matching Positioning Based on Temporal Diversity and Spatial Dependency

6. - 6 - Difficulties Multipath effect results in low accuracy for pattern-matching localization. Most of pattern-matching localization schemes adopt traditional classification, but ignore some unique features.  Ex. Continuous samples should have high similarity as well as diversity.

7. - 7 - Observations A positioning error could be generated by a small portion of interfered signal strengths.  Counting on one single observation is unreliable.  We can enlarge the search space by multiple continuous observations. Continuous observations may have some degrees of  Temporal diversity: For a sequence of observations on a beacon, diversified signal strengths may be seen.  Spatial dependency: For a serious of estimated locations, they should be close each other.

8. - 8 - Localization: Clustering of Location Database for pattern-matching localization in large-scale sensing field (such as a wireless city)

9. - 9 - Challenges Scalability problem when the field is large.  High computation cost in the positioning phase  Long system response time (critical to real-time applications) To reduce computation cost in the positioning phase:  apply clustering technique to fragment database into a number of sets.  examine only one cluster in the positioning phase

10. - 10 - Cluster Scheme Overview   1   2.   n Location Database Pattern-Matching Localization Algorithm C*C* Training PhasePositioning Phase signal strength vector: [s 1, s 2, …, s m ] avg. signal strength: [  i,1,  i.2,…,  i.m ] training data s s real-time data Clustering training location access point (AP) ii 11 ii

11. - 11 - Localization: Beacon Movement Detection

12. - 12 - Beacon Movement Detection Problem Maintenance issue: beacon movement/failure Ex: What happens if some beacons are moved by accident? Goal:  Automatically detect the beacon movement events  Remove the data of these unreliable beacons from the database to improve accuracy  Result: More serious localization error!!

13. - 13 - System Model ( t =0 denotes the initial time) Positioning Procedure BMD Procedure

14. - 14 - Emergency Guiding (IEEE Computer) 3D Emergency Guiding (IJSN) Surveillance: iMouse (IEEE Computer) Energy Saving: iPower (IJSNET*) GeoAds (MASS 2007) Localization Comm. Protocol Applications & Systems Signal Scrambling (IEEE TKDE*) Data Clustering (MASS 2007) Beacon Movement (VTC 2007) ConvergeCast (MobiWAC 2006) Orphan Problem (MSWiM 2007) My Research Roadmap on WSN WSN Location Tracking & Deployment Location Management (IEEE TMC, IJSN) Placement (IEEE TMC) k-Placement (IEEE TPDS) Connectivity and Placement (ACM ToSN)

15. - 15 - Research Issues Object Tracking  Event Detection  Target Classification  Location Estimation  Location Management  Tree-based update & query mechanisms  Single-sink WSNs & Multi-Sink WSNs Deployment of WSNs  Placement  Dispatch  Single-level coverage & Multi-level coverage Coverage and Connectivity  Coverage  Connectivity  Distributed protocols for ensuring both coverage and connectivity of a wireless sensor network  More general decentralized solutions  Do not rely on the assumption R C  2R S  Distributed protocols to determine and to control coverage and connectivity

16. - 16 - Location Tracking & Deployment: “In-Network” Location Management

17. - 17 - Location Management Update and Query:  How to update the location information?  How to disseminate the queries?

18. - 18 - Proposed Model

19. - 19 - Location Tracking & Deployment: Sensor Placement

20. - 20 - Deployment of a WSN for Single-Level Coverage cost detection capability Sensor deployment is critical since it affects the cost and detection capability of a WSN. coverage connectivitysensing distance communication distance  A deployment should consider both coverage and connectivity, which decide by sensing distance r s and communication distance r c. Our contributions obstacles  Allow the sensing field to contain obstacles. arbitrary  Allow the relationship of r c and r s to be arbitrary.  Complete solution  Complete solution: placement + dispatch

21. - 21 - Sensor Placement Solutions Partition Partition the sensing field into sub-regions and then place sensors in each region.  Single-row regions  A belt-like area between obstacles a sequence of sensors  We can deploy a sequence of sensors to satisfy both coverage and connectivity.  Multi-row regions multiple rows  We need multiple rows of sensors to cover such areas.

22. - 22 - Sensor Dispatch Solutions Centralized algorithm maximum-weightperfect matching  Find a maximum-weight perfect matching in a weight complete bipartite graph Distributed algorithm compete  Let sensors compete to move to their destinations Existence of obstacles A I

23. - 23 - Location Tracking & Deployment: Multi-level Placement of Sensors

24. - 24 - Deployment of a WSN for Multi-Level Coverage Multi-level coverage Multi-level coverage is essential for many protocols and applications in WSNs triangulation  Positioning protocols by triangulation coveragesensory data  Fault tolerance on coverage or sensory data  Wakeup-sleep mechanism to extend the network’s lifetime Our contributions arbitrary  Allow the relationship of r c and r s to be arbitrary  Complete solution  Placement solution: interpolating scheme  Dispatch solution: competition-based scheme 3 2 22 1 1 1

25. - 25 - Interpolating Placement Scheme: 3-coverage placement? 3-coverage placement: - duplicate scheme: 3 3 × 3 = 9 rows scheme: - interpolating scheme: 21 3 × 2 + 1 = 7 rows 1-coverage placement: duplicate scheme: 3 rows regions that are NOT 3-covered

26. - 26 - Publications Journal Papers  C.-F. Huang, L.-C. Lo, Y.-C. Tseng, and W.-T. Chen “Decentralized Energy-Conserving and Coverage-Preserving Protocols for Wireless Sensor Networks”, ACM Trans. on Sensor Networks, Vol. 2, No. 2, 2006, pp. 182-187.  Y.-C. Wang, C.-C. Hu, and Y.-C. Tseng, “Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network”, IEEE Trans. on Mobile Computing (to appear). (SCI)  C.-Y. Lin, W.-C. Peng, and Y.-C. Tseng, "Efficient In-Network Moving Object Tracking in Wireless Sensor Networks", IEEE Trans. on Mobile Computing, Vol. 5, No. 8, Aug. 2006, pp. 1044-56. (SCI)  C.-Y. Lin, Y.-C. Tseng, T.-H. Lai, and W.-C. Peng, ”Message-efficient In-network Location Management in a Multi-sink Wireless Sensor Network”, Int’l Journal of Sensor Networks (to appear). Conference Papers  Y.-C. Wang, W.-C. Peng, M.-H. Chang, and Y.-C. Tseng, "Exploring Load-Balance to Dispatch Mobile Sensors in Wireless Sensor Networks", Int'l Conf. on Computer Communication and Networks (ICCCN), 2007.  Y.-C. Wang, C.-C. Hu, and Y.-C. Tseng, “Efficient Deployment Algorithms for Ensuring Coverage and Connectivity of Wireless Sensor Networks”, Wireless Internet Conf. (WICON), 2005.  C.-F. Huang, L.-C. Lo, Y.-C. Tseng, and W.-T. Chen, “Decentralized Energy-Conserving and Coverage-Preserving Protocols for Wireless Sensor Networks”, Int’l Symp. on Circuits and Systems (ISCAS), 2005.  C.-Y. Lin, Y.-C. Tseng, and T.-H. Lai, “Message-Efficient In-Network Location Management in a Multi-sink Wireless Sensor Network”, IEEE Int’l Conf. on Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006.  C.-Y. Lin and Y.-C. Tseng, "Structures for In-Network Moving Object Tracking in Wireless Sensor Networks", Broadband Wireless Networking Symp. (BroadNet), 2004.

27. - 27 - My Research Roadmap on WSN WSN Localization Location Tracking & Deployment Comm. Protocol Applications & Systems Signal Scrambling (IEEE TKDE*) Data Clustering (MASS 2007) Beacon Movement (VTC 2007) Location Management (IEEE TMC, IJSN) Placement (IEEE TMC) k-Placement (IEEE TPDS) Connectivity and Placement (ACM ToSN) ConvergeCast (MobiWAC 2006) Orphan Problem (MSWiM 2007) Emergency Guiding (IEEE Computer) 3D Emergency Guiding (IJSN) Surveillance: iMouse (IEEE Computer) Energy Saving: iPower (IJSNET*) GeoAds (MASS 2007)

28. - 28 - Communication Protocol: Convergecast

29. - 29 - Network Scenario In a tree network, routers can send regular beacons to support low duty cycle operations A’s beacon sche: A wakes up to hear C’s beacon and report data To C Zzz.. Zzz …. Zzz.. C’s beacon sche: ZigBee coordinator

30. - 30 - Contributions Define a minimum delay beacon scheduling (MDBS) problem for ZigBee tree-based WSNs Prove MDBS problem is NP-complete Find special cases in MDBS Propose centralized and distributed algorithms, which are compliant to the ZigBee standard

31. - 31 - Communication Protocol: Orphan Problem

32. - 32 - Challenge In ZigBee, when forming a network, devices are said to join the network if it can receive a network address  Each device tries to associate to the ZigBee coordinator or a ZigBee router  A ZigBee coordinator or router will decide whether to accept devices according to its capacity  The capacity of a ZigBee device relates to the ZigBee address assignment

33. - 33 - ZigBee Address Assignment In ZigBee, network addresses are assigned to devices by a distributed address assignment scheme ZigBee coordinator determines three network parameters  the maximum number of children (Cm) of a ZigBee router  the maximum number of child routers (Rm) of a parent node  the depth of the network (Lm) A parent device utilizes Cm, Rm, and Lm to compute a parameter called C skip  which is used to compute the size of its children’s address pools

34. - 34 - An ZigBee Address Assignment Example 01713 C skip =6 Total:21 19 For coord. 7 node B 20 A becomes an orphan node !!

35. - 35 - ZigBee network formationThe proposed scheme Dotted nodes are orphan nodes !! A Simulation Result

36. - 36 - Contributions The first work that models the orphan problem in ZigBee networks  This orphan problem is divided by two subproblems  The Bounded-Degree-and-Depth-Tree Formation (BDDTF) problem  The End-Device Maximum-Matching (EDMM) problem Prove the BDDTF problem is NP-complete Propose a network formation algorithm, which can effectively reduce the number of orphan devices

37. - 37 - Publications Y.-C. Tseng and M.-S. Pan, “Quick Convergecast in ZigBee/IEEE 802.15.4 Tree-Based Wireless Sensor Networks”, ACM Int’l Workshop on Mobility Management and Wireless Access (ACM MobiWac), 2006. M.-S. Pan and Y.-C. Tseng, "The Orphan Problem in ZigBee-based Wireless Sensor Networks", ACM/IEEE Int'l Symp. on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2007.

38. - 38 - My Research Roadmap on WSN WSN Localization Location Tracking & Deployment Comm. Protocol Applications & Systems Signal Scrambling (IEEE TKDE*) Data Clustering (MASS 2007) Beacon Movement (VTC 2007) Location Management (IEEE TMC, IJSN) Placement (IEEE TMC) k-Placement (IEEE TPDS) Connectivity and Placement (ACM ToSN) ConvergeCast (MobiWAC 2006) Orphan Problem (MSWiM 2007) Emergency Guiding (IEEE Computer) 3D Emergency Guiding (IJSN) Surveillance: iMouse (IEEE Computer) Energy Saving: iPower (IJSNET*) GeoAds (MASS 2007)