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  • Formaat: 216 pages
  • Ilmumisaeg: 24-Jun-2021
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429804595

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"The monitoring of continuous phenomena like temperature, soil moisture or air pollutants is becoming more common due to the availability of cheaper and better sensor infrastructure. Better methods for processing, storing and provisioning the data produced by such observations are still evolving. The fundamental features among them are interpolation, real-time provisioning, critical states definitions (e.g. exceeded threshold of the daily mean) and field data type management. The book gives an overview of monitoring and proposes several solutions for problems that occur in this context"--

Monitoring continuous phenomena by stationary and mobile sensors has become a common due to the improvement in hardware and communication infrastructure and decrease in it’s cost. Sensor data is now available in near real time via web interfaces and in machine-readable form, facilitated by paradigms like the Internet of Things (IoT).

There are still some obstacles in the usability of the data since the positions (in space and time) of observation and the positions of interest usually do not coincide. Interpolation is the technique to fill such gaps and there are manifold methods to perform it. To actually operate a monitoring system, there are problems like unambiguous identification of interpolation method and associated parameters, appropriate interface to store observations and retrieve interpolated data, continuous update of the interpolation model for real time monitoring, compression and progressive retrieval of observational data and critical states definition and notification by using aggregation of values.

This book proposes a general system architecture that addresses these problems. It is not confined to details about particular interpolation methods but rather takes a holistic view on the problem of monitoring. State-of-the-art technologies like geostatistics, sensor web enablement and field data types are introduced and applied in order to provide a viable toolset for the problem domain. The focus is on the overall organization of the monitoring and the architectural design of the software system and the associated simulation framework that is used to systematically evaluate different monitoring approaches. The whole cycle of a monitoring entailing observation, interpolation, discretization, storage, retrieval and notification is covered. Concrete solutions for several common problems in this context are provided.

Foreword iv
Preface viii
Acknowledgements x
List of Figures xvi
List of Tables xviii
1 Introduction 1(20)
1.1 Motivation and Challenges
2(1)
1.2 Main Contributions
2(7)
1.3 Observing and Interpolating Continuous Phenomena
9(2)
1.4 Deterministic Approaches
11(2)
1.5 Geostatistical Approaches
13(2)
1.6 Mixed Approaches
15(1)
1.7 Simulation
16(3)
1.8 Summary
19(2)
2 Monitoring Continuous Phenomena 21(16)
2.1 Overview
22(2)
2.2 Requirements
24(3)
2.2.1 (Near) Real-Time Monitoring
24(1)
2.2.2 Persistent Storage and Archiving
25(1)
2.2.3 Retrieval
26(1)
2.3 Resources and Limitations
27(6)
2.3.1 Sensor Accuracy
29(1)
2.3.2 Sampling
29(2)
2.3.3 Computational Power
31(1)
2.3.4 Time (Processing and Transmission)
31(1)
2.3.5 Energy (Processing and Transmission)
32(1)
2.4 Summary
33(4)
3 Spatio-Temporal Interpolation: Kriging 37(14)
3.1 Method Overview
38(1)
3.2 The Experimental Variogram
39(1)
3.3 The Theoretical Variogram and the Covariance Function
40(5)
3.4 Variants and Parameters
45(3)
3.5 Kriging Variance
48(2)
3.6 Summary
50(1)
4 Representation of Continuous Phenomena: Vector and Raster Data 51(10)
4.1 Overview
52(3)
4.2 Vector Data Properties
55(1)
4.3 Raster Data Properties
56(1)
4.4 Raster-Vector Interoperability
57(3)
4.5 Summary
60(1)
5 A Generic System Architecture for Monitoring Continuous Phenomena 61(58)
5.1 Overview
63(1)
5.2 Workflow Abstraction Concept
64(4)
5.2.1 Datasets (Input/Source and Output/Sink)
66(1)
5.2.2 Process/Transmission
67(1)
5.3 Monitoring Process Chain
68(21)
5.3.1 Random Field Generation by Variogram Filter
70(3)
5.3.2 Sampling and Sampling Density
73(6)
5.3.3 Experimental Variogram Generation
79(1)
5.3.4 Experimental Variogram Aggregation
80(5)
5.3.5 Variogram Fitting
85(3)
5.3.6 Kriging
88(1)
5.3.7 Error Assessment
88(1)
5.4 Performance Improvements for Data Stream Management
89(16)
5.4.1 Problem Context
90(1)
5.4.2 Sequential Model Merging Approach
91(7)
5.4.2.1 Overview
91(1)
5.4.2.2 Related Work
92(1)
5.4.2.3 Requirements
92(1)
5.4.2.4 Principle
93(2)
5.4.2.5 Partitioning Large Models: Performance Considerations
95(3)
5.4.3 Compression and Progressive Retrieval
98(7)
5.4.3.1 Overview
98(1)
5.4.3.2 Related Work
99(1)
5.4.3.3 Requirements
99(1)
5.4.3.4 Principle
100(1)
5.4.3.5 Binary Interval Subdivision
100(1)
5.4.3.6 Supported Data Types
101(2)
5.4.3.7 Compression Features
103(2)
5.5 Generic Toolset for Variation and Evaluation of System Configurations
105(13)
5.5.1 Context and Abstraction
106(3)
5.5.2 Computational Workload
109(4)
5.5.3 Systematic Variation of Methods, Parameters and Configurations
113(2)
5.5.4 Overall Evaluation Concept
115(3)
5.6 Summary
118(1)
6 A General Concept for Higher Level Queries about Continuous Phenomena 119(10)
6.1 Introduction
120(1)
6.2 Interpolation
121(3)
6.3 Intersection
124(1)
6.4 Aggregation
125(2)
6.5 Conclusions
127(2)
7 Experimental Evaluation 129(40)
7.1 Minimum Sampling Density Estimator
131(4)
7.1.1 Experimental Setup
131(1)
7.1.2 Results
131(4)
7.1.3 Conclusions
135(1)
7.2 Variogram Fitting
135(6)
7.2.1 Experimental Setup
136(3)
7.2.2 Results
139(2)
7.2.3 Conclusions
141(1)
7.3 Sequential Merging
141(4)
7.3.1 Experimental Setup
142(1)
7.3.2 Results
142(2)
7.3.3 Conclusions
144(1)
7.4 Compression
145(6)
7.4.1 Experimental Setup
145(3)
7.4.2 Results
148(2)
7.4.3 Conclusions
150(1)
7.5 Prediction of Computational Effort
151(2)
7.5.1 Experimental Setup
151(1)
7.5.2 Results
152(1)
7.5.3 Conclusions
152(1)
7.6 Higher Level Queries
153(9)
7.6.1 Experimental Setup
153(4)
7.6.2 Results
157(2)
7.6.3 Conclusions
159(3)
7.7 Case Study: Satellite Temperature Data
162(7)
7.7.1 Experimental Setup
163(2)
7.7.2 Results
165(2)
7.7.3 Conclusions
167(2)
8 Conclusions 169(8)
8.1 Subsuming System Overview
170(5)
8.2 Perspective
175(2)
References 177(12)
Index 189
Peter Lorkowski's first choice of profession was becoming a surveying engineer. He also holds a master degree in geodesy and geoinformatics and acquired his doctoral degree with a work on a software framework for the monitoring of continuous phenomena. He has since worked in the private sector in software engineering and also founded a non-profit project dealing with the semantic organization of knowledge about sustainability. Its objective is to quantify the socio-ecologic impacts of consumption in the sectors nutrition, housing, mobility and information and communication technology.