| Description |
1 online resource (205 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- 1 Fog Computing Fundamentals in the Internet of Things: A Taxonomy, Survey, and Future Directions -- 1.1 Introduction -- 1.2 What Is Internet of Things -- 1.2.1 How IoT Works -- 1.2.2 IoT-Enabling Technology -- 1.3 Cloud Computing -- 1.3.1 Cloud Layers -- 1.3.2 Cloud Computing Architecture -- 1.3.3 Benefits of Cloud Computing -- 1.4 Fog Computing -- 1.4.1 History of Fog Computing -- 1.4.2 How Fog Computing Works -- 1.4.3 Application of Fog Computing |
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1.4.4 Fog Computing Benefits and Drawbacks -- 1.4.5 What Materializes Inside the Nodes of Fog and the Cloud? -- 1.4.6 IoT and Fog Computing -- 1.4.7 How IoT Actually Relates to Your Enterprise -- 1.4.8 How IoT Actually Relates to Your Infrastructure -- 1.5 How to Trace and Save You in COVID-19 Network Transmission With the Aid of Fog Computing -- 1.5.1 Contextual and Problems -- 1.5.2 Scope -- 1.5.2.1 Implementation Overview -- 1.6 Conclusion -- References -- 2 Role of IoT to Control the Movement of Infections During Pandemic -- 2.1 Introduction -- 2.2 The Internet of Things |
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2.3 Controlling Social Distances Using IoT -- 2.4 Early Diagnosis of Infection -- 2.5 Remote Patient Monitoring -- 2.6 Quarantine Control -- 2.7 IoT Applications -- 2.8 Future of the Internet of Medical Things -- 2.9 Conclusion -- References -- 3 Internet of Things (IoT)-Based Smart Farming System: A Broad Study of Emerging Technologies -- 3.1 Introduction -- 3.2 Significant Applications -- 3.2.1 Soil Sampling and Mapping -- 3.2.2 Water System -- 3.2.3 Manure -- 3.2.4 Yield Observation, Anticipating, and Accumulation -- 3.3 Advanced Agricultural Practices -- 3.3.1 Green House Farming |
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3.3.2 Vertical Farming -- 3.3.3 Hydroponic -- 3.3.4 Phenotyping -- 3.4 Significant Equipment and Technologies -- 3.4.1 Modern Tractor -- 3.4.1.1 Event Detection Algorithm -- 3.4.1.2 Design of the Descending Seed Course Monitoring (DSCM) Appliance -- 3.4.2 Collecting Robots -- 3.4.3 Correspondence in Agriculture -- 3.4.3.1 Cellular Communication -- 3.4.3.2 Zigbee -- 3.4.3.3 Bluetooth -- 3.4.3.4 LoRa -- 3.4.3.5 Sigfox -- 3.5 Cloud Computing -- 3.6 Conclusion -- References |
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4 IoT and FOG Space-Time Particulate Matter (PM2.5) Concentration Forecasting for IoT-Based Air Pollution Monitoring Systems -- 4.1 Introduction -- 4.2 IoT- Based Air Quality Monitoring -- 4.3 Related Works -- 4.4 Overview -- 4.4.1 Data Description -- 4.4.2 Problem Formulation -- 4.5 Proposed Methodology -- 4.5.1 Proposed CNN-Based Encoder -- 4.5.2 Proposed LSTM for Learning Patterns in the Series Represented in Latent Space -- 4.5.3 Modelling -- 4.6 Evaluation Metrics -- 4.7 Experiments and Results -- 4.7.1 Performance Comparison With Baseline Models |
| Notes |
Description based upon print version of record |
|
4.7.2 Effectiveness of 1D-CNN in the Proposed CNN-LSTM Architecture |
| Subject |
Internet of things
|
|
Cloud computing
|
| Form |
Electronic book
|
| Author |
Rana, Ajay
|
|
Kumar, Vivek
|
|
Sebastián Gutiérrez Calderón, José
|
|
Athithan, Senthil
|
| ISBN |
9781000805406 |
|
1000805409 |
|
