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Energy Efficient Computing

Design and Applications
Edited by Himanshu Sharma, Krishan Arora, Suman Lata Tripathi, and Gyanendra Prasad Joshi
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394404315 | Hardcover |
346 pages
Price: $225 USD

One Line Description
Transform your data infrastructure into a lean, eco-friendly powerhouse with cutting-edge strategies designed to drastically cut carbon emissions without compromising performance.

Description
As global demand for data processing and storage continues to rise, cloud computing and data centers have become the backbone of the digital economy. However, their rapid expansion has significantly increased energy consumption, leading to heightened carbon emissions and environmental concerns. This book explores innovative, energy-efficient, and eco-friendly approaches to minimize the environmental impact of these critical infrastructures. This comprehensive volume delves into the core principles of green computing, focusing on sustainable design, resource optimization, and renewable energy integration for cloud platforms and data centers. It examines emerging technologies such as AI-driven energy management, server virtualization, liquid cooling systems, edge computing, and carbon-aware load balancing. The book also addresses the role of regulatory frameworks, carbon footprint assessments, and eco-conscious data governance in fostering sustainable practices. Through case studies, industry insights, and cutting-edge research, this book provides academics, industry professionals, and policymakers with actionable strategies to design and operate environmentally responsible cloud infrastructures and data centers.

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Author / Editor Details
Himanshu Sharma, PhD is an Assistant Professor at Lovely Professional University, India, with more than four years of experience in academics. He has published more than ten research papers in refereed journals and conferences. His area of expertise includes power electronics, optimization techniques, load frequency control, automatic generation control, and modernization of smart grids.

Krishan Arora, PhD is the Head of the Department of Power Systems in the School of Electronics and Electrical Engineering at Lovely Professional University, India with more than seventeen years of experience in academics and research. He has published more than 85 research papers in refereed journals and conferences, five edited books, six Indian patents, and a copyright. He has edited 5 books in different areas of Electronics and Electrical engineering. His expertise lies in power electronics, non-conventional energy sources, electric drives, induction and synchronous machines, and digital electronics.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University, India, with more than 20 years of experience in academics. She has published more than 74 research papers in refereed journals and conferences, 17 books, 13 Indian patents, and two copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design of testing, and advanced FET design for IoT.

Gyanendra Prasad Joshi, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Kangwon National University, Samcheok, Republic of Korea. He has more than 150 research articles published in books, international journals, and international conferences as a first or corresponding author. His main research interests include UAV localization, routing protocols for next-generation wireless networks, wireless sensor networks, cognitive radio networks, and RFID systems.

Table of Contents
Preface
1. Machine Learning for Real-Time Energy Optimization
Irfan Ahmad Pindoo
1.1 Introduction
1.1.1 The Urge of Real-Time Energy Optimization
1.2 Foundations: Energy Optimization and Machine Learning Primitives
1.2.1 Core Optimization Targets
1.2.2 Key Machine Learning Concepts for Energy
1.2.3 The “Real-Time” Constraint
1.3 Machine Learning Approaches for Real-Time Energy Optimization
1.3.1 Predictive Modeling for Proactive Control
1.3.1.1 Predicting Workload Characteristics
1.3.1.2 Predicting Future System State
1.3.1.3 Using Predictions to Proactively Set Hardware States
1.3.1.4 Models: Regression and Time-Series Forecasting Techniques
1.3.2 Reinforcement Learning for Adaptive Control
1.3.2.1 Framing Energy Optimization as a Reinforcement Learning Problem
1.3.2.2 Algorithms: Q-Learning, State-Action-Reward-State-Action, Policy Gradients
1.3.2.3 Online vs. Offline Training and Simulator Use
1.3.2.4 Challenges of Reinforcement Learning in Energy-Optimization
1.3.3 Deep Learning for Complex Pattern Recognition
1.3.3.1 Using Convolutional Neural Networks/Recurrent Neural Networks to Learn Intricate Patterns in Sensor Data
1.3.3.2 End-to-End Learning of Control Policies
1.3.3.3 Quantization and Pruning for Deploying Efficient DL Models at the Edge
1.3.4 Hybrid and Lightweight Machine Learning Approaches
1.3.4.1 Combining Machine Learning Models (e.g., Predictor Informs Reinforcement Learning State)
1.3.4.2 Decision Trees, Random Forests, Gradient Boosting for Interpretable and Fast Inference
1.3.4.3 Bayesian Optimization for Hyperparameter Tuning of Controllers
1.3.4.4 Federated Learning for Collaborative, Privacy-Preserving Model Training across Devices
1.4 Key Application Domains and Case Studies
1.4.1 Mobile and Edge Devices
1.4.1.1 Display Backlight and Refresh Rate Adaptation
1.4.1.2 Network Interface Management
1.4.1.3 Case Study of Machine Learning-Driven Dynamic Voltage and Frequency Scaling on Smartphones/Embedded Systems
1.4.2 Data Centers
1.4.2.1 Real-Time Workload Scheduling and Placement for Energy Efficiency
1.4.2.2 Dynamic Server Power Capping (Rack/Row Level)
1.4.2.3 Cooling Optimization (Fan Speed, Chilled Water Flow) Based on Predicted Heat Load
1.4.2.4 Case Study: Google’s DeepMind for Data Center Cooling/Machine Learning-Driven Power Capping
1.4.3 High-Performance Computing
1.4.3.1 Adaptive Task/Process Scheduling across Heterogeneous Nodes
1.4.3.2 Dynamic Power Budgeting for Compute Nodes/Jobs
1.4.3.3 Optimizing Communication Patterns (Energy-Aware Message Passing Interface)
1.4.4 Autonomous Systems (Robotics/Drones)
1.4.4.1 Energy-Aware Path Planning and Navigation
1.4.4.2 Dynamic Sensor Activation/Fusion Strategies
1.4.4.3 Optimizing Computation vs. Communication Trade-Offs
1.5 Critical Challenges and Mitigation Strategies
1.5.1 Data Acquisition and Quality
1.5.2 Model Training Complexity and Cost
1.5.3 Inference Latency and Overhead
1.5.4 Safety, Stability, and Robustness
1.5.5 Generalization
1.5.6 Explainability and Trust
1.5.7 Security
1.6 Metrics for Evaluation
1.6.1 Energy Efficiency
1.6.2 Performance Impact
1.6.3 Optimization Overhead
1.6.4 Robustness and Stability
1.6.5 Fairness
1.7 Future Research Directions
1.7.1 Ultra-Low-Power Machine Learning Hardware (Neuromorphic Computing, In-Memory Computing)
1.7.2 Self-Improving/Continual Learning Systems
1.7.3 Explainable Artificial Intelligence for Trustworthy Control
1.7.4 Federated and Distributed Learning at Scale
1.7.5 Integration with Renewable Energy Sources/Smart Grids
1.7.6 Co-Design of Hardware, Systems Software, and Machine Learning Algorithms
1.8 Conclusion
1.8.1 Responsible and Effective Use of Deployment Call to Action
References
2. Low-Power Energy-Efficient Design of an Operational Amplifier
Kudakwashe Muzira, Sanjeet Kumar Sinha and Sweta Chander
2.1 Introduction
2.2 Operational Amplifier and Equivalent Circuit
2.2.1 Circuit Equivalent for an Operational Amplifier
2.3 Characteristics of an Operational Amplifier
2.3.1 Background and Conceptual Principle
2.4 Energy-Efficient, Low-Power Design Techniques
2.4.1 Technology Level Techniques
2.4.2 Circuit -Level Techniques
2.4.3 Complementary Metal Oxide Semiconductor Technology for Operational Amplifiers
2.5 Conclusion and Future Prospects
References
3. Energy Storage Systems and Smart Grids
Harpreet Kaur Channi
3.1 Introduction
3.1.1 Overview of Energy Storage Systems
3.2 Literature Survey
3.2.1 Problem Formulation
3.2.2 Objectives
3.3 Methodology
3.3.1 MATLAB Implementation
3.3.1.1 Data Input
3.3.1.2 Energy Storage Systems Modeling
3.3.1.3 Control Strategy
3.3.1.4 Simulation Procedure
3.3.1.5 Output Analysis
3.4 Case Study: Integration of Energy Storage System in a Residential Smart Grid
3.4.1 Simulating the Impact of Energy Storage Systems on Peak Load Reduction
3.4.2 Simulating Energy Storage Systems’ Role in Frequency Regulation
3.5 Results and Discussion
3.5.1 Load-Renewable Balance and Energy Storage System Operation
3.5.2 Frequency Regulation and ESS Response
3.5.3 Integrated Role of ESS in Hybrid Systems
3.6 Global Case Studies of Hybrid Renewable Energy Systems
with Energy Storage
3.7 Conclusion
3.7.1 Future Work
References
4. Enhancing Hydropower System Efficiency Using Artificial Intelligence-Driven Image Processing
Amanjot Singh
4.1 Introduction
4.2 Various Challenges of the Hydropower System
4.2.1 Dam Design and Construction
4.2.2 Turbine Technology and Efficiency
4.2.3 Sedimentation Management
4.2.4 Operational Challenges
4.2.5 Environmental Monitoring and Mitigation
4.2.6 Climate Change Resilience
4.2.7 Reservoir Management
4.3 Image Processing Role in Hydropower
4.3.1 Progress Monitoring
4.3.1.1 Terrain Mapping and Surveying
4.3.1.2 Environmental Impact Assessment
4.3.1.3 Detection of Geohazards
4.3.1.4 Monitoring Construction Progress
4.3.2 Post-Monitoring
4.3.2.1 Dam Safety and Monitoring
4.3.2.2 Water Flow Monitoring
4.3.2.3 Turbine Condition Monitoring
4.3.2.4 Environmental Impact Assessment
4.3.2.5 Security and Surveillance
4.3.2.6 Remote Sensing and Geographic Information System Integration
4.4 Conclusion
References
5. The Internet-of-Things-Enabled Design of Developed Hardware for the Graphene Derivatives-Based Moisture Sensor and Testing Under Laboratory Conditions
Shelej Khera
5.1 Introduction
5.2 Materials and Methods
5.2.1 Graphene Oxide and Reduced Graphene Oxide Sensor Soil Moisture Measurement
5.2.2 Developed Hardware
5.2.2.1 Power Management Unit
5.2.2.2 Signal Conditioning Unit
5.2.2.3 Signal Processing Unit
5.2.2.4 Sensor
5.3 Results and Discussion
5.3.1 Simulation
5.3.2 Lab Testing
5.3.3 Internet-of-Things Data Transmission to MATLAB
5.4 Conclusions
References
6. Cloud Resource Optimization Technique
Pandey Gaurav Kumar and Srivastava Sumit
6.1 Introduction
6.1.1 Overview of Cloud Computing Evolution and Architecture
6.1.2 Need for Resource Optimization in the Cloud Environment
6.1.3 Inefficient Resource Utilization
6.1.3.1 Cost Management for Providers and Users
6.1.3.2 Ensuring Quality of Service
6.1.3.3 Supporting Dynamic Workloads
6.1.3.4 Environmental Sustainability
6.1.3.5 Scalability and Elasticity
6.1.4 Importance of Energy Efficiency in Data Centers
6.1.4.1 Massive Energy Consumption
6.1.4.2 Environmental Impact
6.1.4.3 Economic Efficiency and Cost Reduction
6.1.4.4 Performance and Scalability
6.1.4.5 Compliance and Reputation
6.1.4.6 Enablement of Technology
6.2 Cloud Resource Management: Background
6.2.1 Definition of Cloud Resources (Central Processing Unit, Memory, Storage, Network)
6.2.1.1 Central Processing Unit
6.2.1.2 Memory
6.2.1.3 Storage
6.2.1.4 Network
6.2.2 Types of Cloud Services: Software as a Service, Platform as a Service, and Infrastructure as a Service
6.2.3 Resource Allocation Models
6.2.4 Metrics for Cloud Performance and Energy Usage
6.2.5 Challenges in Cloud Resource Management
6.2.5.1 Dynamic Workloads
6.2.5.2 Service-Level Agreement Compliance
6.2.5.3 Resource Heterogeneity
6.2.5.4 Energy-Performance Trade-Offs
6.3 Resource Optimization Techniques
6.3.1 Classification of Optimization Techniques
6.3.2 Key Techniques
6.4 Intelligent Algorithms for Optimization
6.5 Case Studies and Simulation Results
6.6 Tools and Simulation Frameworks
6.7 Challenges and Future Directions
6.8 Conclusion
Bibliography
7. Power Distribution and Renewable Energy Integration
Karthikeyan K., Umasankar P. and Parathraju P.
7.1 Introduction to Power Distribution
7.2 The History of the Electrical Distribution System
7.2.1 Initial Electric Power Distribution Began in 1889
7.2.2 Alternating Current Transmission Transformed Long-Distance Electricity Distribution
7.2.3 Transmission Voltages Increased Dramatically Over Time
7.2.4 Introduction of Polyvinyl Chloride-Insulated Wiring in the 1930s
7.2.5 Evolution of Switchgear Design
7.3 Operation of Electrical Distribution System
7.4 Procedure for Delivering Electricity to Consumers
7.5 Challenges in Power Distribution
7.6 Types of Distribution Systems Based on the Nature of Current
7.6.1 AC Distribution System
7.6.2 DC Distribution System
7.6.3 Based on Type of Construction
7.6.4 Based on the Scheme of Connection
7.7 Overview of Renewable Energy Integration
7.8 Evolution of Renewable Energy Integration
7.8.1 Need for Renewable Integration
7.9 Renewable Energy Sources Operation in a Distributed System
7.10 Developments in Renewable Integration
7.10.1 Technologies and Methods Enabling Renewable Energy Integration
7.10.2 Grid Management, Demand Response, and Energy Forecasting
7.10.3 Innovation and Research in Advancing Integration Technologies
7.10.4 Practical Deployment of Renewable Energy Integration
7.10.5 Tools, Resources, and Strategies for Effective Implementation
7.10.6 Role of Stakeholder Engagement and Community Involvement
7.10.7 Benefits of Renewable Integration
7.10.8 Environmental Benefits of Renewable Integration
7.11 Challenges and Opportunities of Renewable Energy Integration
7.11.1 Challenges
7.11.2 Opportunities
7.12 Conclusion
References
8. Advanced Soft Computing Techniques in Renewable Energy Technologies
Krishan Arora
8.1 Introduction
8.2 The Development History of Soft Computing
8.3 Fuzzy Logic
8.3.1 Fuzzy Logic in Artificial Intelligence
8.3.2 Fuzzy Set Applications in Power Systems
8.3.3 Other Fields Applications of Fuzzy Logic
8.4 Artificial Neural Network
8.4.1 Electrical Engineering and Machine Learning Applications for Neural Networks
8.5 Adaptive Neuro-Fuzzy Inference System
8.5.1 Blocks of Fuzzy Inference Systems
8.5.2 Arcs of Fuzzy Reasoning
8.6 Conclusion
References
9. Automating Operating System and Software Migration: Engineering a Software Framework for Effortless Operating System and Application Migration
Muhammad Younus, Halimah Abdul Manaf , Dyah Mutiarin, Achmad Nurmandi, Andi Adawiah, Tunjung Sulaksono, Jamaluddin Ahmad, Eliza Meiyani, Andi Luhur Prianto and Sunhyuk Kim
9.1 Introduction
9.2 Literature Review
9.3 Research Method
9.4 Results and Discussion
9.4.1 Overview of the Model
9.4.1.1 Important Elements of the Software
9.4.1.2 Data Packaging and Extraction Module
9.4.1.3 Data Transfer and Synchronization Module
9.4.1.4 Data Restoration and Configuration Module
9.4.2 Working Principles
9.4.2.1 User Experience and Interface Design
9.4.2.2 Technological Infrastructure and Requirements
9.4.2.3 Security and Data Privacy Features
9.4.2.4 Testing and Validation
9.4.3 Key Insights
9.4.4 Implications
9.4.5 Recommendations
9.5 Conclusion
Bibliography
10. Smart Cradle Baby Care Monitoring System Using the Internet of Things
A. Arthi, K. Keerthirajan, N. Mohammed Hussain, M. Vinuvarsith, S. Vishnuvaradhan and Qodirov Asliddin Asomiddin
10.1 Introduction
10.2 Related Work
10.3 Proposed System
10.4 Methodology and Technologies Used
10.4.1 Methodology
10.4.1.1 Integrating the Internet of Things for Remote Monitoring
10.4.1.2 Sensor Integration for Environmental and Health Monitoring
10.4.1.3 Automated Rocking and Temperature Regulation
10.4.1.4 Real-Time Alerts and Data Visualization
10.4.2 Technology Used
10.4.2.1 Internet of Things
10.4.2.2 Temperature Sensors
10.4.2.3 Sound Sensors and Cry Detection
10.4.2.4 Motorized Cradle Mechanism
10.4.2.5 Equations
10.5 Result and Discussion
10.6 Conclusion and Future Enhancement
Bibliography
11. Analysis and Identification of Soil Fertility Using Machine Learning
Y. Sreenivasa Reddy, Shaik Karimulla, S. Siva Yaswanth Reddy, L. Sharmila and Muhammad Rukunuddin Ghalib
11.1 Introduction
11.2 Related Work
11.3 Existing System
11.3.1 Requirement Analysis
11.3.1.1 Evaluation of the Rationale and Feasibility
11.4 Proposed System
11.4.1 System Methodologies
11.4.2 Decision Tree
11.4.3 Support Vector Machine
11.4.4 Random Forest
11.4.5 System Architecture
11.5 System Modules
11.5.1 Modules Description
11.5.1.1 Data Collection Module
11.5.2 Pre-Processing
11.5.3 Model Creation
11.5.4 Model Training
11.5.5 Integration of Real-Time Analysis and Correction
11.5.6 Assessment
11.6 Conclusion
11.7 Future Enhancements
Bibliography
12. Advanced Driver Drowsiness Detection and Yawning Alert
System Leveraging Machine Learning
Sachin Kumar, Gaurav Kumar, Vishal Kohli, Krishan Arora and Ajay Kumar
12.1 Introduction
12.2 Background and Related Work
12.2.1 Driving Patterns
12.2.2 Physiological Sensors
12.2.3 Computer Vision
12.3 Methodology
12.3.1 Process for Detecting Driver Drowsiness
12.3.1.1 Video Recording and Data Generation
12.3.1.2 Facial Recognition
12.3.1.3 Marking Facial Landmarks
12.3.1.4 Extracting Features
12.3.1.5 Categorization
12.3.2 Behavioral Strategies and Identifying Techniques
12.4 Results and Discussion
12.4.1 Examination of Comparable Classification Approaches
12.5 Conclusion
References
13. Internet-of-Things-Enabled Monitoring for Real-Time
Energy Optimization
Dabbula Sahithi, Guntumadugu Venkata Sravan, Vajrala Ruksana, Dhivya P., Amritha Krishnan A., Ranjit Singh Chauhan and Ashish Aggarwal
13.1 Introduction
13.1.1 Background and Motivation
13.1.2 Importance of Energy Optimization in Smart Systems
13.1.3 Role of the Internet of Things in Energy Efficiency
13.1.4 Relevance to Biomedical Engineering and Smart Healthcare Infrastructure
13.2 Fundamentals of the Internet of Things-Based Monitoring Systems
13.2.1 Architecture of Internet of Things Systems
13.2.2 Sensor Networks and Energy-Aware Embedded Devices
13.2.3 Real-Time Data Acquisition and Edge Processing
13.2.4 Communication Protocols
13.3 Energy Optimization Techniques in Internet of Things Systems
13.3.1 Adaptive Sampling and Duty Cycling
13.3.2 Energy-Aware Routing Algorithms
13.3.3 Dynamic Power Management and Dynamic Voltage Scaling
13.3.4 Artificial Intelligence/Machine Learning for Predictive Energy Control
13.4 Real-Time Monitoring and Control Frameworks
13.4.1 Fog and Edge Computing for Energy Optimization
13.4.2 Cloud vs Edge vs On-Device Processing Trade-Offs
13.4.3 Interoperability and Standardization for Efficient Energy Communication
13.4.4 Real-Time Operating Systems in Energy-Constrained Environments
13.5 Case Studies in General Domains
13.5.1 Smart Homes and Buildings
13.5.2 Industrial Internet of Things for Energy Optimization
13.5.3 Smart Grid Integration and Load Balancing
13.5.4 Agricultural Internet of Things for Sustainable Resource Use
13.6 Applications in Biomedical Engineering
13.6.1 The Internet of Things in Smart Hospitals: Energy-Aware Heating, Ventilation, Air Conditioning, Lighting, and Equipment Scheduling
13.6.2 Wearable Biomedical Devices with Energy-Efficient Sensing
13.6.3 Internet of Things-Enabled Medical Imaging and Diagnostics
13.6.4 Data Privacy and Power Management Trade-Offs in Biomedical Internet of Things
13.6.5 Energy-Aware Artificial Intelligence Algorithms in Biomedical Edge Devices
13.6.6 Energy Optimization in Remote Patient Monitoring Systems
13.7 Design Considerations and Challenges
13.7.1 Scalability and Resource Constraints
13.7.2 Battery Management and Energy Harvesting
13.7.3 Cybersecurity Implications in Energy-Optimized Internet of Things Networks
13.7.4 Interference and Latency in Biomedical Signal Transmission
13.8 Future Trends and Research Directions
13.8.1 Quantum Internet of Things and Ultra Low-Power Devices
13.8.2 Integration of 6G, Blockchain, and Edge-Artificial Intelligence
13.8.3 Bio-Compatible Energy Harvesting Systems
13.8.4 Smart Energy Management in Internet of Things-Based Bioinstrumentation
13.9 Conclusion
References
14. High-Performance Filter Design and Implementation Using
a Field-Programmable Gate Array
Suman Lata Tripathi
14.1 Introduction
14.2 Tool Description and Models
14.2.1 Models
14.2.2 Hamming Window
14.2.2.1 Pipeline
14.2.2.2 Parallel
14.3 Block Diagram on Methodology
14.4 Verilog Design Modules and Verilog Code
14.5 Result and Discussion
14.5.1 Register-Transfer-Level Schematic
14.5.2 Simulation Waveform
14.5.3 Power and Timing Analysis
14.6 Performance Comparison Table
14.7 Applications
14.8 Conclusion
References
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