Monitoring Continuous Phenomena: Background, Methods and Solutions

Description

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. Cover Title Page Copyright Page Dedication Foreword Preface Acknowledgements Table of Contents List of Figures List of Tables 1. Introduction 1.1 Motivation and Challenges 1.2 Main Contributions 1.3 Observing and Interpolating Continuous Phenomena 1.4 Deterministic Approaches 1.5 Geostatistical Approaches 1.6 Mixed Approaches 1.7 Simulation 1.8 Summary 2. Monitoring Continuous Phenomena 2.1 Overview 2.2 Requirements 2.2.1 (Near) Real-Time Monitoring 2.2.2 Persistent Storage and Archiving 2.2.3 Retrieval 2.3 Resources and Limitations 2.3.1 Sensor Accuracy 2.3.2 Sampling 2.3.3 Computational Power 2.3.4 Time (Processing and Transmission) 2.3.5 Energy (Processing and Transmission) 2.4 Summary 3. Spatio-Temporal Interpolation: Kriging 3.1 Method Overview 3.2 The Experimental Variogram 3.3 The Theoretical Variogram and the Covariance Function 3.4 Variants and Parameters 3.5 Kriging Variance 3.6 Summary 4. Representation of Continuous Phenomena: Vector and Raster Data 4.1 Overview 4.2 Vector Data Properties 4.3 Raster Data Properties 4.4 Raster-Vector Interoperability 4.5 Summary 5. A Generic System Architecture for Monitoring Continuous Phenomena 5.1 Overview 5.2 Workflow Abstraction Concept 5.2.1 Datasets (Input/Source and Output/Sink) 5.2.2 Process/Transmission 5.3 Monitoring Process Chain 5.3.1 Random Field Generation by Variogram Filter 5.3.2 Sampling and Sampling Density 5.3.3 Experimental Variogram Generation 5.3.4 Experimental Variogram Aggregation 5.3.5 Variogram Fitting 5.3.6 Kriging 5.3.7 Error Assessment 5.4 Performance Improvements for Data Stream Management 5.4.1 Problem Context 5.4.2 Sequential Model Merging Approach 5.4.2.1 Overview 5.4.2.2 Related Work 5.4.2.3 Requirements 5.4.2.4 Principle 5.4.2.5 Partitioning Large Models: Performance Considerations 5.4.3 Compression and Progressive Retrieval 5.4.3.1 Overview 5.4.3.2 Related Work 5.4.3.3 Requirements 5.4.3.4 Principle 5.4.3.5 Binary Interval Subdivision 5.4.3.6 Supported Data Types 5.4.3.7 Compression Features 5.5 Generic Toolset for Variation and Evaluation of System Configurations 5.5.1 Context and Abstraction 5.5.2 Computational Workload 5.5.3 Systematic Variation of Methods, Parameters and Configurations 5.5.4 Overall Evaluation Concept 5.6 Summary 6. A General Concept for Higher Level Queries about Continuous Phenomena 6.1 Introduction 6.2 Interpolation 6.3 Intersection 6.4 Aggregation 6.5 Conclusions 7. Experimental Evaluation 7.1 Minimum Sampling Density Estimator 7.1.1 Experimental Setup 7.1.2 Results 7.1.3 Conclusions 7.2 Variogram Fitting 7.2.1 Experimental Setup 7.2.2 Results 7.2.3 Conclusions 7.3 Sequential Merging 7.3.1 Experimental Setup 7.3.2 Results 7.3.3 Conclusions 7.4 Compression 7.4.1 Experimental Setup 7.4.2 Results 7.4.3 Conclusions 7.5 Prediction of Computational Effort 7.5.1 Experimental Setup 7.5.2 Results 7.5.3 Conclusions 7.6 Higher Level Queries 7.6.1 Experimental Setup 7.6.2 Results 7.6.3 Conclusions 7.7 Case Study: Satellite Temperature Data 7.7.1 Experimental Setup 7.7.2 Results 7.7.3 Conclusions 8. Conclusions 8.1 Subsuming System Overview 8.2 Perspective References Index