A Beginner's Guide to Data Agglomeration and Intelligent Sensing

Description

A Beginners Guide to Data Agglomeration and Intelligent Sensing provides an overview of the Sensor Cloud Platform, Converge-casting, and Data Aggregation in support of intelligent sensing and relaying of information. The book begins with a brief introduction on sensors and transducers, giving readers insight into the various types of sensors and how one can work with them. In addition, it gives several real-life examples to help readers properly understand concepts. An overview of concepts such as wireless sensor networks, cloud platforms, and device-to-cloud and sensor cloud architecture are explained briefly, as is data gathering in wireless sensor networks and aggregation procedures. Final sections explore how to process gathered data and relay the data in an intelligent way, including concepts such as supervised and unsupervised learning, software defined networks, sensor data mining and smart systems. Cover A Beginner’s Guide to Data Agglomeration and Intelligent Sensing Copyright Contents About the authors Preface Acknowledgment one Introduction to sensors and systems 1.1 Fundamentals of sensors/transducers 1.2 Principles and properties 1.3 Classification of sensors 1.4 Networking methodology 1.4.1 Fifth-generation technology 1.4.2 Bluetooth low energy 1.5 Types of sensors 1.5.1 Sound energy-based sensing 1.5.2 Thermal energy-based sensing 1.5.3 Optical energy-based sensing 1.5.4 Chemical energy-based sensing 1.5.5 Radioactivity sensing 1.6 Smart sensors and transducers 1.6.1 Electronic interfacing for smart sensors 1.6.2 Universal transducer interface 1.6.3 IEEE 1451 smart transducers 1.7 Summary References two Real-life application of sensors and systems 2.1 Overview of Internet of things 2.2 Design perspective 2.3 Related platform 2.3.1 Amazon web service 2.3.2 Google Cloud Platform 2.4 Real-life examples and implementation 2.4.1 A device to a cloud system 2.4.2 Home automation system 2.4.3 Body sensor networks 2.4.3.1 Arduino microcontroller 2.4.3.2 Datalogger 2.4.4 Implantable wireless body area network 2.4.5 Application of WBAN in medical cyber-physical systems 2.5 WSN simulation environments 2.5.1 OMNeT++ simulator 2.5.2 Opportunistic network simulation environment 2.5.3 Simulation result and analysis 2.5.4 Localization of sensor nodes in simulation perspective 2.5.5 Performance analysis 2.6 Summary References three Wireless sensor network: principle and application 3.1 Wireless communication and sensor networks 3.1.1 Network architecture 3.1.2 Network topology 3.2 Sensor components and technology 3.2.1 Micro electro-mechanical devices 3.2.2 MEMS accelerometer 3.2.3 MEMS gyroscope 3.3 Sensor network protocols 3.3.1 Flat-based routing 3.3.2 Adaptive routing mechanism 3.3.3 Hierarchy-based routing 3.4 Sensor networks application scenario 3.4.1 UAS-based sensor network 3.4.2 MAVlink protocol background 3.4.3 A case study on Ardupilot and mission planner for sensing and communication 3.4.4 Renewable energy integration using WSN 3.4.5 Vehicular sensor network for agriculture and food management 3.5 Summary References four Overview of sensor cloud 4.1 Basics of cloud computing 4.1.1 Key features of cloud computing 4.1.2 Cloud computing architecture 4.2 Types of clouds 4.2.1 Public cloud 4.2.2 Private cloud 4.2.3 Hybrid cloud 4.3 Cloud computing models 4.3.1 Software as a service 4.3.2 Platform as a service 4.3.3 Infrastructure as a service 4.4 Sensor cloud platform 4.5 Sensor cloud architecture 4.6 Sensor cloud workflow 4.6.1 Workflow procedure 4.6.2 Difficulties in sensor cloud platform 4.7 Application scenario 4.8 Summary References five Sensor data accumulation methodologies 5.1 Sensor data classification 5.2 Data transmission methodology 5.2.1 Unicast 5.2.2 Multicasting 5.2.3 Broadcasting 5.3 Convergecast: inverse of broadcasting 5.3.1 Important issues 5.4 Data aggregation 5.4.1 Fundamentals of aggregation 5.4.2 Need for aggregation 5.5 Choice of MAC layer 5.6 Energy analysis 5.7 Data collection methodologies 5.8 Types of aggregation 5.8.1 Network aggregation 5.8.2 Tree-based approach 5.8.3 Cluster-based aggregation 5.9 Summary References Further reading six Intelligent sensor network 6.1 Introduction 6.2 Intelligence hierarchy 6.3 Preliminary concepts of AI and Machine Learning 6.3.1 Unsupervised learning method 6.3.2 Supervised learning method 6.3.3 Real-life example of prediction 6.4 Intelligent approaches in WSN node deployment 6.4.1 Node deployment using PSO-Voronoi 6.4.2 Node deployment using ACO 6.5 Intelligent routing overview 6.5.1 Routing basics 6.5.2 SDN overview 6.6 Sensor data mining 6.6.1 Accelerometer sensor data classification: a case study 6.6.2 Random forest classification 6.7 Intelligent sensor network applications 6.8 Summary References Further reading seven Conclusion 7.1 Chapters 1 and 2 7.2 Chapters 3 and 4 7.3 Chapters 5 and 6 7.4 Scope for future enhancement Index Back Cover