Green Computing and Its Applications

Contents
Preface
Chapter 1
Embedded Internet of Things (IoT)  a New Industrial Revolution
	Abstract
	1.1. Introduction
		1.1.1. Evolution of Industry
		1.1.2. Industry 1.0
		1.1.3. Industry 2.0
		1.1.4. Industry 3.0
		1.1.5. Industry 4.0
	1.2. What Do You Think Industry 5.0 Will Be?
		1.2.1. The Advantages and Disadvantages of the  Industrial Revolution
			1.2.1.1. Pros
			1.2.1.2. Cons
	1.3. Literature
		1.3.1. Premiere Development Technologies for Industrial 4.0
		1.3.2. Characteristics of the Internet of Things
	1.4. Applications of IoT
		1.4.1. Detection and Tracking of Assets in Smart Factories Using Bluetooth Low Energy
		1.4.2. Applications for Audio Speech Processing in the  Smart Home
		1.4.3. Smart Health: Post-Stroke Rehabilitation by  Wearable Prototype
		1.4.4. Domain of the Application
	1.5. Difficulties in IIOT FDSM
	References
Chapter 2
Evolution of Green  Communication System
	Abstract
	2.1. Introduction
		2.1.1. Section I: UDMT System Model
			2.1.1.1. Device to Device communication
			2.1.1.2. Co-Operative Communication
		2.1.2. Section II: Massive MIMO
			2.1.2.1. MIMO Communication
			2.1.2.2. Multi‐User MIMO
			2.1.2.3. Massive MIMO
			2.1.2.4. Challenges of Massive MIMO in 5G
	2.2. Results and Discussion
	Conclusion
	References
Chapter 3
Big Data Analytics Based Green Application in Text Mining  and Literary World
	Abstract
	3.1. Introduction
	3.2. State-of-Art:  Literary World in Big Data Text Mining
	3.3. Sentiment Classification of Literary Test  in Big Data Text Mining
		3.3.1. Literary Argument Extraction in Big Data Text Mining
		3.3.2. Blog Mining for Literary World
		3.3.3. Poetry Data-Based Literary Text Mining
		3.3.4. Pre-Processing of Poetry Text
		3.3.5. Literary Transcript Analysis in Big Data
		3.3.6. Machine Learning Algorithm in Literary Text Mining of Big Data
		3.3.7. Linear Regression for Literary Text
		3.3.8. Logistic Regression for Literary Text
		3.3.9. Decision Tree for Literary Text
		3.3.10. Support Vector Machine (SVM) for Literary Text
		3.3.11. Naïve Bayes for Literary Text
		3.3.12. K- Nearest Neighbour’s For Literary Text
		3.3.13. Clustering for Literary Text
		3.3.14. K-Means Clustering Algorithm in the Literary World  of Big Data
		3.3.15. Apriori Algorithm in Literary World of Big Data
		3.3.16. Hierarchal Algorithm in the Literary World of Big Data
	Conclusion
	References
Chapter 4
Deep Learning-Based Solution
for Sustainable Agriculture
	Abstract
	4.1. Introduction
	4.2. Deep Learning
		4.2.1. Convolutional Neural Network
		4.2.2. Recurrent Neural Network (RNN)
		4.2.3. Autoencoder
	4.3. Problems in Agriculture
		4.3.1. Plant Classification
		4.3.2. Plant Recognition
		4.3.3. Classification of Crops
	4.4. Weeds and Crops Classification
	4.5. Plant Disease Identification
	4.6. Fruits Counting
	4.7. Classification of Fruits
	4.8. Available Datasets
	References
Chapter 5
Analysing Factors Impacting  the Adoption of Green Computing  in Indian Universities
	Abstract
	5.1. Introduction
	5.2. Literature Review
	5.3. Theoretical Framework
	5.4. Research Methodology
		Sampling
		Demographics of the Respondents
		5.4.1. Data Analysis
			Reliability and Validity
				(i) Cronbach’s Alpha
				(ii) Composite Reliability
				Exploratory Factor Analysis
				Construct Validity (CV)
					(i) Convergent Validity
				(ii) Divergent or Discriminant Validity
				Structural Equation Modelling (SEM)
	Discussion
	Conclusion
		Limitations and Future Research
	References
Chapter 6
Latest Advancement in Automotive Embedded System Using  IoT Computerization
	Abstract
	6.1. Introduction
	6.2. Related Work
	6.3. Essential Embedded Systems
	6.4. Internet of Things
		6.4.1. IoT Based Smart Vehicles Solution
		6.4.2. IoT Traffic Agents
	6.5. Prologue to IoT and Automotive  Cloud Services
		6.5.1. IoT and Automotive Cloud Services
		6.5.2. IoT Automotive Cloud Services
		6.5.3. Network
		6.5.4. Equipment Control and Management
		6.5.5. Data Collection
		6.5.6. Data Analytics
		6.5.7. Data Visualization
		6.5.8. Management of Configurations
		6.5.9. Command Execution
	6.6. Interoperability in Time
	6.7. Stochastic Analysis
	6.8. Multicore ECU
	6.9. Utilization of IoT  in Automotive Transportation
		6.9.1. Intelligent Fleet Management
		6.9.2. Insurance of Operational Optimization, Service Competence of Real-Time Tracking Exactness
		6.9.3. Real-Time Video Surveillance on Freight Logistics
		6.9.4. Risk Reduction, Operational Costs Decrease  and Fleet Safety Improvement
		6.9.5. Advance Driver Assistance Solution (ADAS)
		6.9.6. Workers
		6.9.7. Security
		6.9.8. Transportation of Goods
	6.10. Present Day Applications  of Automotive Embedded Systems
	6.11. Setup of the Experiment
	6.12. GPS Tracking
		6.12.1. Arduino Uno Development Board
	6.13. Proposed Methodology
		6.13.1. Accident Detection
		6.13.2. Travelers Safety
		6.13.3. Drunk Driver Prevention
		6.13.4. Automatic Rain-Sensing Wipers
	6.14. Results and Discussion
	Conclusion and Future Directions
	References
Chapter 7
Integration of Smart-IoT Devices to Enhance Security and Performance of Smart Grids and Smart  Energy Systems
	Abstract
	7.1. Introduction
	7.2. Literature Review
	7.3. Proposed Smart-IoT Device Architecture Design for Smart Grid and Smart  Energy Distribution
		7.3.1. The Mode Selection Interface
		7.3.2. RS232 Interface with PLI for Scanning and Control
		7.3.3. Load Prediction Block for Analysis of Demand and Supply
		7.3.4. Stability Analysis Block
		7.3.5. Bi-Directional Communication Interface
		7.3.6. Blockchain for Improved Attack Detection
	7.4. Result Analysis and Comparison
	Conclusion
	References
Chapter 8
Design of an Adaptive and Flexible Green Computing Architecture for Multi-Domain Social Applications via Artificial Intelligence
	Abstract
	8.1. Introduction
	8.2. Literature Review
	8.3. Proposed Artificial Intelligence-Based Flexible Green Computing Model
	8.4. Result Analysis and Comparison
	Conclusion
	References
Chapter 9
Impact on Organizational Performance of Indian SMEs After the Adoption of Green Computing
	Abstract
	9.1. Introduction
	9.2. Literature Review
	9.3. Research Framework
	9.4. Research Methodology
		9.4.1. Sampling
		9.4.2. Demographics of the Respondents
	9.5. Data Analysis
		9.5.1. Reliability and Validity
			9.5.1.1. Cronbach’s Alpha
			9.5.1.2. Composite Reliability
		9.5.2. Exploratory Factor Analysis
		9.5.3. Construct Validity (CV)
			9.5.3.1. Validity Divergent or Discriminatory
		9.5.4. Structural Equation Modeling
	9.5. Discussion
	9.6. Managerial Implications
	Conclusion
	References
Chapter 10
High-Performance Computing and Fault Tolerance Technique Implementation in Cloud Computing
	Abstract
	10.1. Introduction
	10.2. Related Work
		10.2.1. Supercomputers
	10.3. Cloud Computing
		10.3.1. Cloud Characterization
		10.3.2. Cloud Services
		10.3.3. HPC in the Cloud
		10.3.4. All-Cloud
			10.3.4.1. Cloud Blasting
			10.3.4.2. All-Cloud: Minimize the Local Footprint
		10.3.5. Cloud Bursting: Expanding from Local
			10.3.5.1. All-Cloud or Bursting?
			10.3.5.2. HPC Performance Benchmarking
			10.3.5.3. Superior Computing Requirements in Cloud
		10.3.6. HPC versus HSC
		10.3.7. GPU-Accelerated Computing
			10.3.7.1. How GPUs Accelerate Software Applications
			10.3.7.2. Memory Modes for Increased Performance on  Intel Xeon Phi
			10.3.7.3. HPC Software
		10.3.8. Execution Penalties
		10.3.9. Difficulties for High-Performance Computing Applications in the cloud
		10.3.10. Cloud Benefits for High-Performance Computing
	10.4. Proposed Method
		10.4.1. Relocation Policy Based on Proposed Method
		10.4.2. Control Module in Proposed Method
	10.5. Result and Simulation
	Conclusion
	References
Chapter 11
An Analysis of Internet of Things (IoT)–Based Home Appliances
	Abstract
	11.1. Introduction
		11.1.1. Identification
		11.1.2. Sensing
		11.1.3. Communication
		11.1.4. Computation
		11.1.5. Services
		11.1.6. Semantics
			11.1.6.1. Saving Time
			11.1.6.2. Saving Energy
			11.1.6.3. Cost-Efficient
			11.1.6.4. Security Enhancement
			11.1.6.5. Convenience
			11.1.6.6. Adaptability
			11.1.6.7. Integration
			11.1.6.8. Task Management
	11.2. IoT Technology
		11.2.1. Radiofrequency Identification (RFID)
		11.2.2. Wireless Sensor Networks (WSNs)
			11.2.2.1. Barcodes
			11.2.2.2. Near Field Communication (NFC)
			11.2.2.3. Cloud Computing
	11.3. IoT Based Home Appliances
		11.3.1. Amazon Echo
		11.3.2. Google Nest Hub
		11.3.3. Nest Cam Indoor and Outdoor Camera
		11.3.4. Smart Mat Intelligent Yoga Mat
		11.3.5. Smart LED Bulb
		11.3.6. TrackR Bravo Tracking Device
		11.3.7. Honeywell Wi-Fi Smart Thermostat
		11.3.8. Logitech Pop Smart Button Controller
		11.3.9. June Intelligent Oven
		11.3.10. Ring Pro Smart Video Doorbell
		11.3.11. LG Web OS Smart OLED TV
	11.4 Composition of an Advanced Smart Home
	Conclusion
	References
Chapter 12
Internet of Things (IoT)  in Agriculture
	Abstract
	12.1. Introduction
	12.2. Iot Transformation in  THE FUTURE of Agriculture
		12.2.1. Use of Smart Agriculture Iot Technology
		12.2.2. Usage of Greenhouse Can Be Automated  Using Iot Applications in Farming
		12.2.3. Reduced Water Consumption in Agriculture
		12.2.4. Pest Monitoring
		12.2.5. Livestock Tracking
		12.2.6. Big Data in Farming
		12.2.7. Smart Agriculture Predictive Analytics
	12.3. Applications of Iot in Agriculture
		12.3.1. Weed Robots
		12.3.2. Harvesting Robotics
		12.3.3. Drones
		12.3.4. Machine Navigation
		12.3.5. Climatic Conditions
		12.3.6. Soil Quality
	12.4. Sensors Used for Agriculture
		12.4.1. Agricultural Temperature Sensors
		12.4.2. Smart Cameras Use in Agriculture
		12.4.3. pH Sensors in Agriculture
		12.4.4. GPS Sensors
		12.4.5. Sensors for Resource Monitoring
		12.4.6. Accelerometer Sensor
	12.5. IoT Challenges in Agriculture
		12.5.1. Connectivity
		12.5.2. Design and Durability
		12.5.3. Limited Resources and Time
		12.5.4. Adaptability of Farmers’ Technology
	Conclusion
	References
About the Editors
Index
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