Title Page
Abstract
국문요약
Contents
Chapter 1. Introduction 23
1.1. Research Backgrounds 23
1.2. Contributions of Dissertation 26
1.3. Organization of Dissertation 28
Chapter 2. Overview of Range-based Localization in Sensor Networks 31
2.1. Concept and Applications of Node Localization 31
2.1.1. Localization in Sensor Networks 33
2.1.2. Advantage of Range-based Localization 45
2.1.3. Extension of Localization to Underwater Applications 47
2.2. Ranging/Angle Techniques for Sensor Networks 49
2.2.1. Received Signal Strength (RSS) Based Ranging 50
2.2.2. Time (Difference) of Arrival Based Ranging 51
2.2.3. Angle of Arrivals Based Measurement 53
2.3. Positioning Techniques for Range-based Localization 54
2.3.1. Tri-lateration with Least Square Estimation 54
2.3.2. Maximum Likelihood Estimation (MLE) 56
2.3.3. Multidimensional Scaling (MDS) 59
Chapter 3. Research Issues on Range-based Localization in Sensor Networks 64
3.1. Issues on Range-based Localization 64
3.1.1. Distance and Position Estimation Accuracy 65
3.1.2. Communication Cost Reduction 70
3.1.3. Three-dimensional Applicability 70
3.1.4. Open Problems 71
3.2. Accuracy Improvement with Mobile Beacons for Range-based 3-D Localization 71
3.2.1. Benefit of Mobile Beacons for Range-based 3-D Localization 71
3.2.2. Previous Works for Mobile beacon-based 3-D Localization 73
3.2.3. Limitations of Previous Works 74
3.3. Contributions for Mobile beacon-based 3-D Localization 76
3.4. Summary 78
Chapter 4. Distance Estimation for Linear Beacon Track in Mobile Beacon-based Localization 79
4.1. Introduction 79
4.2. Distance Measurement Model 81
4.3. Proposed Distance Estimation for Linear Beacon Track 83
4.3.1. Distance Estimation with Weighted Least Square Approach 84
4.3.2. Performance Analysis with Cramer-Rao Bound (이미지 참조) 86
4.4. Performance Evaluation 88
4.4.1. Evaluation of Distance Accuracy 88
4.4.2. Evaluation of Location Accuracy 91
4.5. Study on Distance Estimation for Asynchronous Nodes 95
4.5.1. Measurement Model for Asynchronous Nodes 96
4.5.2. Distance Estimation for Asynchronous Nodes 97
4.5.3. Performance Evaluation 98
4.6. Summary 102
4.7. Appendix: Cramer-Rao Bound (이미지 참조) 103
Chapter 5. Mobile Beacon-based 3-D Localization with Multidimensional Scaling 107
5.1. Introduction 107
5.2. System Model of Mobile Beacon-based 3-D Localization 110
5.3. Proposed Scheme for Mobile Beacon-based 3-D Localization 111
5.3.1. Decision Rule for Resolving Ambiguous Reflection in Node Mapping 111
5.3.2. Quadrant-based Selection Rule for Choosing Efficient Sets of Beacon Points 113
5.4. Performance Evaluation 115
5.4.1. Simulation Setup 115
5.4.2. Effects of Decision Rule and Selection Rule 115
5.4.3. Performance Comparison: Location Accuracy and Computation Complexity 116
5.5. Summary 117
5.6. Appendix I: Linear Transformation for Node Mapping 118
Chapter 6. Floating Beacon-based 3-D Localization to Underwater Applications 120
6.1. Introduction 120
6.2. Related Works on Underwater Localization 123
6.2.1. Classical Underwater Positioning Systems 123
6.2.2. Locating Algorithms for UWSNs 124
6.3. System Model for Underwater Localization 125
6.3.1. Acoustic Signals for Underwater Communications 125
6.3.2. System Model 128
6.4. Proposed Scheme to Locate Nodes on Seabed with Floating Beacons 129
6.4.1. Frame Structure to Measure Time of Flights (TOFs) 129
6.4.2. Locating Nodes on Seabed with Positions of Floating Beacons and Computed Distances 131
6.5. Simulation Results and Discussion 133
6.6. Summary 136
Chapter 7. Conclusions and Further Works 138
7.1. Conclusions 138
7.1.1. Distance Estimation for Linear Beacon Track 138
7.1.2. Location Estimation with Mobile Beacon 139
7.1.3. Extension to Underwater Localization 139
7.2. Further Works 140
References 141
Curriculum Vitae 151
Table 2.1: Comparison chart of distance measurement. 50
Table 6.1: Baseline distance in SBL, USBL, and LBL systems. 124
Figure 2.1: Illustration of wireless sensor networks: (a) a sensor node known as MICA2 composed of processing, communication, sensor, and power units, and (b) self-organized WSNs with a hundred of sensor nodes and four beacon nodes, assuming that the communication range of a sensor node is limited to 150 and a sensing field is given as... 32
Figure 2.2: Performance comparison with known localization with respect to evaluation metrics 61
Figure 2.3: Underwater sound speed profile (SSP) depending on depth and errors in computed distances: (a) SSP and deployment of a transmitter and N number of receivers, and (b) the error in a computed distance between a transmitter and each receiver, where decrease in mean error is caused by SSP and dispersion of error is... 62
Figure 2.4: Trilateration positioning: (a) theoretical model with no measurement errors, (b) practical model with measurement errors, and (c) practical model in a multi-lateration case. 63
Figure 3.1: Recursive approach for given distances to beacon nodes 66
Figure 3.2: Flip ambiguity results in high location error in node localization: (a) Ambiguity for linearly aligned beacon nodes and (b) robust quadrilaterals approach to overcome the flip ambiguity in determining node's position. 67
Figure 4.1: In mobile beacon-based localization, beacon points for every Δ beacon distance inside a communication range of sensor node χs. (이미지 참조) 82
Figure 4.2: Actual distance di to each beacon point χi (for i = 1; 2; … ;N) with respect to a sensor node at χs, where a set of beacon points is supposed to be placed in a linear track of a beacon. (이미지 참조) 83
Figure 4.3: Simulation setup: deployment of Node A and Node B, mobile beacon trajectory, and received beacon messages at Node A and Node B, respectively. 89
Figure 4.4: Root mean square errors (RMSE) of two sensor nodes obtained with measurements only, distance estimation with Moore's scheme, CRB, and our distance estimation (DE). 90
Figure 4.5: RMSE in distance estimation and the average number of beacon points for a varying beacon distance, in order to show a trade-off between estimation accuracy and computation complexity. 91
Figure 4.6: Performance comparison of distance estimation for two conventional localization schemes: Bound box and CWLS, where Both Bound box with DE and CWLS with DE use our distance estimation instead of measured distances to improve location accuracy. 92
Figure 4.7: Simulation environment to evaluate location accuracy of the proposed distance estimation: beacon points are regularly placed along three linear beacon tracks (left), a set of received beacon points for Node B (right up), and a set of received beacon points for Node A (right down), respectively 94
Figure 4.8: Simulation results of location accuracy using Least Square Estimation (LSE) with measured distances and estimated distances, respectively. 95
Figure 4.9: Measured distances (Measurements) and estimated distances (TDOA) for asynchronous node having time offset to a mobile beacon, where estimated distances (TDOA) is compared to ones (TOA) of synchronous node. 99
Figure 4.10: Mean error and root-mean squared error (RMSE) for measured distances and estimated distances with increasing measurement error. 100
Figure 4.11: Performance comparison of distance estimation for asynchronous node (TDOA) to distance estimation (TOA) and Moore's scheme for synchronous node. 101
Figure 5.1: Received beacon points χ(k)i and measured distances r(k)i for the k-th linear beacon track at a sensor node s (이미지 참조) 110
Figure 5.2: Simulation environment: 200 sensor nodes are deployed over complex 3-D terrain with size of 1000 × 1000 and altitude of -300 to 300. A beacon node is glying over the terrain. 114
Figure 5.3: Effect of selection and decision rules of MBL-MDS in applying MDS to 3-D localization 116
Figure 5.4: Performance comparison of MBL-MDS with conventional location schemes using mobile beacons 117
Figure 6.1: Acoustic speed profile in water varying with depth. 127
Figure 6.2: System model of FBL-VAS: M beacons and N nodes on seabed. 128
Figure 6.3: Simulation setup of FBL-VAS: four TxBuoys are floating on the sea surface and their corresponding RxBuoys are moored on the seabed, and 200 sensor nodes are randomly deployed over the surveillance area given as 1000m × 1000m and depth of 100m. 134
Figure 6.4: Errors in computed distances from TxBuoy 1 (b₁) to each grid point on the seabed. 135
Figure 6.5: RMSE of estimated position of FBL [47] and FBL-VAS with increasing measurement errors. 136