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- 1. Distributed Localization for Wireless Distributed Networks in Indoor Environments Hermie P. Mendoza Wireless @ VT Virginia Polytechnic and State University June 28, 2011 Masters Thesis Defense Presentation
- 2. Agenda 1 Preliminaries of PL and WDC 2 Fingerprint-based PL 3 WDC-based Fingerprinting System 4 Algorithm Performance and Results 5 PL Demo 6 Conclusion and Future WorkHermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 2 / 46
- 3. PreliminariesPreliminaries Overview Location-Awareness in Ubiquitous Computing Position Location Fundamentals Wireless Distributed Computing (WDC) Fundamentals Why Position Location and WDC? Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 3 / 46
- 4. Preliminaries Position LocationLocation Awareness in Ubiquitous Computing Figure: User accessing location-based service on a smartphone. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 4 / 46
- 5. Preliminaries Position LocationThe Principles of Positioning I Positioning Problem: Reasonably localize an object within a global or local frame of reference. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 5 / 46
- 6. Preliminaries Position LocationThe Principles of Positioning II Figure: Summary of Position Location Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 6 / 46
- 7. Preliminaries Position LocationThe Principles of Positioning III Figure: Summary of Position Location Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 7 / 46
- 8. Preliminaries Wireless Distributed ComputingWhat is WDC? New paradigm emphasing distributed information services!Figure: Information service shift from centralized to de-centralized computation.Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 8 / 46
- 9. Preliminaries BeneﬁtsBeneﬁts of WDC Potential Beneﬁts Results 1 Lower energy and power Extends total network consumption per node lifetime 2 Eﬃcient load balancing Better resource demand across collaborating nodes and supply matching 3 Harnesses available Meets computational network resources latency requirements of 4 Robust, secure, & fault complex processing tasks tolerant execution Attain stringent QoS 5 Simpliﬁes radio’s form requirements factor Economic cost savings Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 9 / 46
- 10. Preliminaries BeneﬁtsBeneﬁts of WDC Potential Beneﬁts Results 1 Lower energy and power Extends total network consumption per node lifetime 2 Eﬃcient load balancing Better resource demand across collaborating nodes and supply matching 3 Harnesses available Meets computational network resources latency requirements of 4 Robust, secure, & fault complex processing tasks tolerant execution Attain stringent QoS 5 Simpliﬁes radio’s form requirements factor Economic cost savings Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 9 / 46
- 11. Preliminaries BeneﬁtsBeneﬁts of WDC Potential Beneﬁts Results 1 Lower energy and power Extends total network consumption per node lifetime 2 Eﬃcient load balancing Better resource demand across collaborating nodes and supply matching 3 Harnesses available Meets computational network resources latency requirements of 4 Robust, secure, & fault complex processing tasks tolerant execution Attain stringent QoS 5 Simpliﬁes radio’s form requirements factor Economic cost savings Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 9 / 46
- 12. Preliminaries BeneﬁtsBeneﬁts of WDC Potential Beneﬁts Results 1 Lower energy and power Extends total network consumption per node lifetime 2 Eﬃcient load balancing Better resource demand across collaborating nodes and supply matching 3 Harnesses available Meets computational network resources latency requirements of 4 Robust, secure, & fault complex processing tasks tolerant execution Attain stringent QoS 5 Simpliﬁes radio’s form requirements factor Economic cost savings Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 9 / 46
- 13. Preliminaries BeneﬁtsBeneﬁts of WDC Potential Beneﬁts Results 1 Lower energy and power Extends total network consumption per node lifetime 2 Eﬃcient load balancing Better resource demand across collaborating nodes and supply matching 3 Harnesses available Meets computational network resources latency requirements of 4 Robust, secure, & fault complex processing tasks tolerant execution Attain stringent QoS 5 Simpliﬁes radio’s form requirements factor Economic cost savings Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 9 / 46
- 14. Preliminaries Location Awareness for WDC ParadigmsLocation Awareness for WDC Paradigm Improve overall wireless communication system Needed to achieve interoperability Figure: Cognitive radio sensing environment Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 10 / 46
- 15. Preliminaries PL and WDCMotivations ILocalization is generally accomplished in a centralized manner at theexpense of a single network node’s resources. Can the problem ofpositioning be solved in a distributed manner or parallelized? Figure: Resource constrained mobile phone. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 11 / 46
- 16. Preliminaries PL and WDCMotivations II (a) Point inside the mall (b) Point inside an airport Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 12 / 46
- 17. Preliminaries Min MakespanMin Makespan Problem IGoalMinimize the time taken to compute the individual localizationcalculations.Problem Formulation Given a set of J of m jobs and a set of N of n nodes, the processing time for a job j ∈ J on node i ∈ N is pij ∈ Z+ . Then we must ﬁnd an assignment of the jobs J to the nodes N such that the makespan, or the completion time, is minimized. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 13 / 46
- 18. Preliminaries Min MakespanMin Makespan Problem IIInteger programming formulation minimize t subject to xij = 1, j ∈J i ∈N (1) xij pij ≤ t, i ∈N j∈J xij ∈ {0, 1} , i ∈ N, j ∈ J Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 14 / 46
- 19. Fingerprinting High Level OverviewFingerprint Overview Problem Formulation The Fingerprint Fingerprinting Algorithms Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 15 / 46
- 20. Fingerprinting Problem FormulationFingerprint Problem StatementProblem StatementUsing only RSS observations of an arbitrary transmitter, locate andestimate its position in a distributed manner.Goal Distributed algorithms must be ﬂexible and applicable for various ﬁngerprint-based positioning systems. Computational nodes must form a WDCN. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 16 / 46
- 21. Fingerprinting The FingerprintThe Fingerprint I Figure: Fingerprinting Concept Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 17 / 46
- 22. Fingerprinting The FingerprintThe Fingerprint IIMathematical Interpretation (xi , yi ) = [FP1 , FP2 , . . . , FPn ] (2)for ﬁngerprint location i , using n sensor nodes.Alternative Interpretation f = (xi , yi ) = [FP1 , FP2 , . . . , FPn ] (3)for ﬁngerprint location i , using n sensor nodes. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 18 / 46
- 23. Fingerprinting Fingerprinting AlgorithmsFingerprinting Algorithms Deterministic positioning method Euclidean n distance 2 L2 = FPi − FPi (4) Bayesian i =1 modeling n Neural 2 (ˆ, y ) = min x ˆ FPi − FPi (5) Networks FPi i =1 Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 19 / 46
- 24. Fingerprinting Fingerprinting AlgorithmsFingerprinting Algorithms Probabilistic positioning method P ( f | l ) P(l ) P( l | f ) = , P(f ) = 0 (4) P(f ) Euclidean distance n P( f | l ) = P( fj | l ) (5) Bayesian j=1 modeling Neural Networks P ( lt | lt−1 ) = P lt | lt−1 P(lt−1 ) dlt−1 (6) (ˆ, y ) = max P ( f | l ) P(l ) x ˆ (7) l Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 19 / 46
- 25. Fingerprinting Fingerprinting AlgorithmsFingerprinting Algorithms Pattern Recognition Euclidean distance Bayesian modeling Neural Networks Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 19 / 46
- 26. Fingerprinting Fingerprinting AlgorithmsDistributed Target Localization IDistributed Localization Approaches Transfering computationally complex operations to a single node with greater capabilities. Parallelizing the position location calculations. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 20 / 46
- 27. Fingerprinting Fingerprinting AlgorithmsDistributed Target Localization II Figure: Partitioning a service area for a WDCN. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 21 / 46
- 28. Fingerprinting Fingerprinting AlgorithmsNotations f number of ﬁngerprint locations p number of partitions (ˆ, y ) x ˆ estimated position of user gi vector of probabilities calculated by node i FPi tuple of RSS at ﬁngerprint location i ˆ FP i vector of distances calculated by node i FPi RSS received at sensor i FPi RSS database entry of sensor i pi AOR or partition assigned to a node i Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 22 / 46
- 29. Fingerprinting Fingerprinting AlgorithmsDistributed Euclidean Distance Algorithm (DEDA) ICentralized Approach f 2 (ˆ, y ) = min x ˆ FPi − FPi (4) FPi i =1 Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 23 / 46
- 30. Fingerprinting Fingerprinting AlgorithmsDistributed Euclidean Distance Algorithm (DEDA) IIDistributed Approach ˆ Initialize FPi = 0. while pi is assigned and received, do for all FPj ∈ fj , do ˆ j 2 FP i ← k=1 FPk − FPk end for end while (xj , yj ) ← minFPi ∈ fj . ˆ ˆ return (xj , yj ) ˆ ˆ Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 24 / 46
- 31. Fingerprinting Fingerprinting AlgorithmsDistributed Bayesian Model Algorithm (DBMA) ICentralized ApproachSEE P ( FPi | l ) P(l ) P( l | FPi ) = , P(FPi ) = 0 (5) P(FPi )ACT P ( lt | lt−1 ) = P lt | lt−1 P(lt−1 ) dlt−1 (6)where lt is the current location and lt−1 is the previous location. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 25 / 46
- 32. Fingerprinting Fingerprinting AlgorithmsDistributed Bayesian Model Algorithm (DBMA) IIDistributed Approach Initialize gi = 0. while pi is assigned and received, do for all FPj ∈ fj , do gi (j) ← P( j| {FP1 , FP2 , . . . , FPn }) end for end while return gi Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 26 / 46
- 33. Fingerprinting Fingerprinting AlgorithmsDistributed Neural Networks (DNN) ITypes of Neural Networks Multilayer Perceptron Generalized RegressionBoth will require a supervised learning to train the network. Figure: Artiﬁcial Neural Network (ANN) Architecture Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 27 / 46
- 34. Fingerprinting Fingerprinting AlgorithmsDistributed Neural Networks (DNN) II Figure: WDCN with neural networks Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 28 / 46
- 35. WDC-based Fingerprinting System OverviewSystem Overview Experimental Setup Hardware and Software The Radio Map Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 29 / 46
- 36. WDC-based Fingerprinting System Experimental SetupExperimental Setup Figure: System block diagram Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 30 / 46
- 37. WDC-based Fingerprinting System Hardware and SoftwareHardware Figure: USRP2 with custom WBX daughterboard Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 31 / 46
- 38. WDC-based Fingerprinting System Hardware and SoftwareSoftware WDCN communications - GNU Radio Fingerprint position processing - Python Web-based user interface - PHP Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 32 / 46
- 39. ICTAS ORIGIN
- 40. WDC-based Fingerprinting System The Radio MapThe Radio Map Radio Map for 1st Floor ICTAS 0 -5 -10 10 N22 N21 -15 N20 N19 RSS (dB) N18 -20 N17 N16 -25 N15 N14 N13 -30 N12 N11 -35 -40 0 5 10 15 20 25 30 35 40 45 Position Number Figure: Radio Map with 45 Positions Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 34 / 46
- 41. Algorithm Performance and ResultsAlgorithm Performance and Results Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 35 / 46
- 42. Algorithm Performance and Results Algorithm EvaluationAlgorithm Evaluation I Comparison of Distributed Localization Algorithms Distributed Euclidean Distributed Markov Y−direction (ft.) 5 5 Y−direction (ft.) 4 4 Actual Path Actual Path Estimated Path Estimated Path 3 3 0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220 X−direction (ft.) X−direction (ft.) Distributed Neural Network − GR Distributed Neural Network − MLP 5 5 Y−direction (ft.) Y−direction (ft.) 4 4 Actual Path Actual Path Estimated Path Estimated Path 3 3 0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220 X−direction (ft.) X−direction (ft.) Figure: Comparison of solutions of distributed localization algorithms Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 36 / 46
- 43. Algorithm Performance and Results Algorithm EvaluationAlgorithm Evaluation II 100 90 80 70 Percentage (%) 60 50 40 30 DEDA DBMA 20 GRNN MLPNN 10 50 100 150 200 Error Radius (ft) Figure: Performance comparision of distributed localization algorithms Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 37 / 46
- 44. Algorithm Performance and Results Error StatisticsError Statistics Error statistics of distributed localization algorithms Algorithm Minimum Error Mean Error Max Error DEDA 0 ft. 10.81 ft. 55 ft. DBMA 0 ft. 35.33 ft. 220 ft. GRNN 0 ft. 14.95 ft. 95 ft. MLP 0 ft. 16.90 ft. 155 ft. Average 0 ft. 19.50 ft. 131.25 ft. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 38 / 46
- 45. Position Location Demo OverviewOverview Functional Workﬂow of WDC process Video of Demo Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 39 / 46
- 46. Position Location Demo Functional WorkﬂowTask dissemination and retrieval I Figure: Phase I: Task dissemination Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 40 / 46
- 47. Position Location Demo Functional WorkﬂowTask dissemination and retrieval II Figure: Phase II: Task retrieval Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 41 / 46
- 48. Position Location Demo DemoFingerprinting Position System Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 42 / 46
- 49. Position Location Demo Computational ComplexityComputational Complexity of Online PhaseSingle node Algorithm Computation Searching Sorting EDA O(n) N/A O(n log n) BMA O(n) O (n (log u + 1)) O(n log n)WDC slave node Algorithm Computation Searching Sorting DEDA O(n/4) N/A O(n/4 log n/4) DBMA O(n/4) O (n/4 (log u + 1)) O(n/4 log n/4) Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 43 / 46
- 50. Concluding Remarks ConclusionsConclusions Successful location estimates are highly dependent on quality and uniqueness of RF ﬁngerprints. Increasing spatial granularity of ﬁngerprint positions does not necessarily improve performance of position estimation. Distributed PL is beneﬁcial for large service areas with large databases. De-centralized computations removes single-point of failure and security intrusions. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 44 / 46
- 51. Concluding Remarks Future WorkFuture Work Examine optimization techinque of multisplitting for conventional PL techniques. Expand distributed sensor system to all CORNET nodes and create mobile WDCN. Implement demo with new UHD driver for USRP2. Implement neural network for WDCN. Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 45 / 46
- 52. Concluding Remarks Future WorkQuestions Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 46 / 46

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