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Submit a ProposalDistributed and Parallel Computing
One Line Description
Master the growing field of distributed and parallel computing with this essential guide, offering expert insights into the fundamentals and real-world applications for intelligent and collaborative systems.
Audience
Researchers, academics, IT experts, and computer scientists working in distributed and parallel computing and processing, distributed networks, and grid computing
Researchers, academics, IT experts, and computer scientists working in distributed and parallel computing and processing, distributed networks, and grid computing
Description
Distributed computing, or running programs across multiple computers over a network, is becoming a popular solution for addressing the demands for increased performance across industries, including scientific computing, oil exploration, biotechnology, and medicine. Distributed computing enables seamless communication and collaboration by allowing users from different locations to access and interact with their digital twin simultaneously. Distributed computing enhances the capabilities of digital twins by providing scalability, parallel processing, real-time data integration, collaboration support, resource optimization, fault tolerance, and security features. Distributed and Parallel Computing explores the fundamentals and innovations in intelligent and distributed computing systems and applications, including adaptivity and learning, agents and multi-agent systems, argumentation, case-based reasoning, and collaborative systems. Through expert insights, readers will discover promising real-world applications for this emerging technology.
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Distributed computing, or running programs across multiple computers over a network, is becoming a popular solution for addressing the demands for increased performance across industries, including scientific computing, oil exploration, biotechnology, and medicine. Distributed computing enables seamless communication and collaboration by allowing users from different locations to access and interact with their digital twin simultaneously. Distributed computing enhances the capabilities of digital twins by providing scalability, parallel processing, real-time data integration, collaboration support, resource optimization, fault tolerance, and security features. Distributed and Parallel Computing explores the fundamentals and innovations in intelligent and distributed computing systems and applications, including adaptivity and learning, agents and multi-agent systems, argumentation, case-based reasoning, and collaborative systems. Through expert insights, readers will discover promising real-world applications for this emerging technology.
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Author / Editor Details
Sandhya Avasthi, PhD is an assistant professor in the Computer Science and Engineering Department at ABES Engineering College at Dr. Abdul Kalam Technical University with over 18 years of teaching experience. She has published numerous research articles in refereed international journals, conference proceedings, and book chapters. Her research interests include natural language processing, information extraction, information retrieval, data science, and business intelligence.
Sandhya Avasthi, PhD is an assistant professor in the Computer Science and Engineering Department at ABES Engineering College at Dr. Abdul Kalam Technical University with over 18 years of teaching experience. She has published numerous research articles in refereed international journals, conference proceedings, and book chapters. Her research interests include natural language processing, information extraction, information retrieval, data science, and business intelligence.
Suman Lata Tripathi, PhD is a professor at Lovely Professional University with more than 22 years of experience in academics and research. She has published more than 19 books, 125 research papers in refereed science journals, conference proceedings, 13 Indian patents, and four copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT.
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Table of Contents
Preface
1. Introduction to Distributed Systems
K. Karthikeyan, S. Hemalatha and S. Vignesh
1.1 Introduction
1.1.1 Background and Context
1.1.2 Objectives of the Study
1.1.3 Scope and Limitations
1.1.4 Key Characteristics of Distributed Systems
1.1.4.1 Decentralization and Concurrency
1.1.4.2 Communication and Scalability
1.1.4.3 Fault Tolerance and Consistency
1.1.4.4 Security and Resource Sharing
1.1.5 Types of Distributed Systems
1.1.5.1 Systems for Clients and Servers
1.1.5.2 Peer-To-Peer Systems
1.1.5.3 Middleware-Based Systems
1.1.5.4 Three-Tier and N-Tier
1.1.5.5 Grid and Cloud Computing
1.1.6 Distributed Algorithms for Boolean Equations Over Networks
1.1.6.1 System of Boolean Equations Over a Network
1.1.6.2 Locally Private Distributed Algorithm
1.1.6.3 Consensus Projection for Linear Algebraic Equations
1.1.6.4 Affine Subspace Boolean Vector Search
1.1.6.5 Distributed SAT Verification
1.1.6.6 Distributed Equilibrium Computation of Boolean Networks
1.1.7 Effective Communication in Dispersed ML
1.1.7.1 Data Parallelism with Gradient Compression
1.1.7.2 Scheduling Issues
1.1.7.3 Decentralized Analytics
1.1.7.4 Federated Learning
1.1.7.5 Distributed Security and Privacy in Mobile Networks and IoT Systems
1.1.8 DS Applications Across a Wide Range of Domains
1.1.8.1 Web Services and Mobile Apps
1.1.8.2 Databases and Distributed File Storage Systems
1.1.8.3 Blockchain Platforms
1.1.8.4 Financial Services
1.1.8.5 Scientific Research and Simulation
1.1.9 Challenges and Considerations
1.1.9.1 Scalability of Distributed Systems in Various Applications
1.1.9.2 Integration with Existing Systems
1.1.9.3 Regulatory Compliance and Legal Framework
1.1.10 Future Prospects and Trends
1.1.10.1 Distributed Systems Emerging Technologies (Quantum Distributed Computing)
1.1.10.2 Research Advancements
1.1.10.3 Anticipated Benefits and Challenges
1.1.11 Conclusion
1.1.11.1 Recap of Key Findings
1.1.11.2 Implications for Healthcare Transformation
References
2. Topology in Network Technologies
Mohit Malik, Varun Dogra, Ramandeep Sandhu, Ratesh Kumar and Deepika Ghai
2.1 Introduction
2.2 Related Work
2.3 Network Topology Design
2.4 Advantages of Network Topologies
2.5 Case Studies of Network Topologies
2.6 Distributed and Parallel Computing in Network Topology
2.6.1 Challenges/Issues in Network Topology in Distributed Computing or Parallel Computing
2.7 Conclusion
References
3. Distributed Processing Technology and Advancements
Saranya. N., Shermy. R.P., Sindhu. V., Karthika Renuka. D. and Renugadevi. G.
3.1 Introduction
3.2 Distributed Processing in Modern Computing
3.3 Evolution of Distributed Processing
3.4 Key Concepts and Technologies Driving the Evolution of Distributed Processing
3.5 Recent Advancements in Distributed Processing
3.6 Changing Landscape of Computing
3.6.1 New Opportunities and Challenges for Developers and Businesses
3.6.2 Use of Distributed and Parallel Computing
3.7 Security and Privacy Considerations
3.7.1 Case Studies
3.7.2 Future Directions
3.8 Opportunities and Challenges in Distributed Computing
3.8.1 Future Directions and Opportunity
3.8.2 Challenges
3.9 Conclusion
References
4. Distributed System Architecture and Computing Models
Sandhya Avasthi and Suman Lata Tripathi
4.1 Introduction
4.2 Distributed System Architecture
4.2.1 Architecture Style
4.2.2 Middleware Organization
4.3 Middleware in Distributed Systems
4.3.1 Host Infrastructure Middleware
4.3.2 Distribution Middleware
4.3.3 Domain-Specific Middleware
4.3.4 Intelligent Middleware
4.4 Distributed Cloud Architecture
4.5 Distributed Machine Learning
4.6 Conclusion
References
5. Parallel Computing Models and Architecture
Aastha Sharma, Swati Sharma and Divya Mishra
5.1 Introduction
5.2 Evolution of Parallel Computing Models
5.2.1 Basic Approach Parallel Computing
5.2.2 Architecture
5.2.3 Flowchart Analysis
5.3 Parallel Computing Models
5.3.1 Complexity
5.3.2 Advantages
5.3.3 Disadvantages
5.3.4 Algorithmic Problem
5.3.4.1 Shared Memory Cache
5.3.4.2 Distributed Memory Model
5.3.4.3 Data Parallel Model
5.3.4.4 Task Parallel Model
5.3.5 Pipeline Model
5.3.6 Hybrid Models
References
6. Network Issues and High-Level Communication Tools in Distributed Computing
Sandhya Avasthi and Sachin Kumar Vishwakarma
6.1 Introduction
6.1.1 Common Network Issues in Distributed Computing
6.1.2 Group Communication in Distributed Systems
6.2 Latency in Distributed and Parallel Computing
6.3 Straggler Effect
6.4 Packet Loss in the Distributed Network
6.4.1 Implications of Packet Loss
6.4.2 Identifying Packet Loss
6.4.3 Tools to Measure Packet Loss
6.4.4 Techniques to Prevent Packet Loss
6.5 Network Congestion
6.5.1 Causes of Network Congestion and Implications
6.5.2 Network Congestion Implication
6.6 Communication Load
6.6.1 A Deterministic Strategy
6.6.2 Bidding Strategy
6.6.3 Drafting Strategy
6.6.4 Greedy Strategy
6.6.5 Threshold Strategy
6.7 Conclusion
References
7. Infinite Horizons: Empowering Business Education Through Metaverse
Shikha Mishra and Sujit S. Nair
7.1 Introduction
7.2 Challenges and Concerns
7.3 Rules of Law–Based Governance
7.4 Possible Implementations in Educational Framework of Metaverse Technology
7.5 Understanding Metaverse
7.6 Explore the Transformational Potential of Metaverse for Business Education
7.7 Innovation and Design Ideas Forum
7.8 Continuing Education and Career Development
7.9 Takeaways
7.10 Ethical and Legal Issues
7.11 Future Prospects and Innovations
7.12 Conclusion
References
8. Paradigm Shifts and Future Directions in Distributed Data Management for Decentralized Networks
Bishnu Kant Shukla, Bhupender Parashar, Vikas Kumar Singla and Shivam Verma
8.1 Introduction
8.2 Paradigm Shifts in Distributed Data Management
8.2.1 Evolution of Data Management Paradigms
8.2.2 Impact of Decentralized Architectures
8.2.3 Key Technological Drivers
8.3 Emergent Architectures and Frameworks
8.3.1 Overview of Emergent Architectures
8.3.2 Domain-Specific Languages in Data Analytics
8.4 Integration of IoT
8.4.1 IoT Ecosystem and Data Management Challenges
8.5 Edge Computing and Big Data Analytics
8.5.1 Benefits of Edge Computing
8.5.2 Big Data Management in Distributed Systems
8.6 Data Aggregation and Summarization Techniques
8.6.1 Importance of Data Aggregation
8.6.2 Case Studies and Practical Implementations
8.7 Advanced Applications and Case Studies
8.7.1 Domestic and Industrial Applications
8.8 Future Directions in Distributed Data Management
8.8.1 Blockchain for Enhanced Data Security
8.8.2 AI and ML in Data Analytics
8.8.3 Prospective Research Avenues
8.9 Conclusion
References
9. Autonomy and Adaptive Architectures in Distributed Systems
Bishnu Kant Shukla, Bhupender Parashar, Vikas Kumar Singla and Shivam Verma
9.1 Introduction
9.2 Conceptual Framework
9.2.1 Definition of Key Concepts
9.2.1.1 Autonomy
9.2.1.2 Adaptive Architectures
9.2.1.3 Multiagent Systems
9.2.1.4 The Evolutionary Trajectory of MAS
9.2.2 Importance of Decentralized Paradigms
9.3 Architectural Models
9.3.1 Overview of Emergent Architectures
9.3.2 Agent-Based Models
9.3.2.1 Agent Architecture and Control Flow
9.4 Adaptive Strategies in Distributed Systems
9.4.1 Mechanisms for System Resilience and Scalability
9.5 Case Studies
9.5.1 Smart Grids
9.5.2 Geographical and Functional Infrastructure Interdependence
9.5.3 Water Distribution Systems
9.5.3.1 Adaptive Water Management
9.5.3.2 Leak Detection and Management
9.5.3.3 Water Quality Monitoring
9.5.4 Transportation Systems
9.5.4.1 Intelligent Transportation Systems
9.5.4.2 Traffic Management
9.5.4.3 Public Transportation
9.6 Application of MASs
9.6.1 Autonomous Surface Ships
9.6.2 Unmanned Surface Vehicles
9.6.3 Swarm Robotics
9.6.4 Autonomous Underwater Vehicles
9.6.5 Hybrid MASs
9.6.6 Common Coordination Models in MAS
9.6.6.1 Centralized Coordination
9.6.6.2 Decentralized Coordination
9.6.6.3 Hierarchical Coordination
9.6.6.4 Market-Based Coordination
9.6.6.5 Contract Net Protocol
9.7 Autonomous Navigation Systems
9.7.1 Examples of Recently Developed USVs
9.8 Collision Avoidance Algorithms
9.8.1 Comparison of Collision Avoidance Algorithms
9.8.2 Implementation of TBA
9.9 Network Properties in Distributed Systems
9.9.1 Small-World Networks
9.9.2 Scale-Free Networks
9.9.3 Robustness and Fault Tolerance
9.9.4 Scalability
9.9.5 Application-Specific Design
9.10 Challenges and Future Directions
9.10.1 Security and Privacy Considerations
9.10.2 Integration with Emerging Technologies
9.10.2.1 Blockchain
9.10.2.2 Artificial Intelligence
9.10.2.3 Internet of Things
9.11 Conclusion
References
10. Distributed Consensus Frequency Control in Networked Microgrid
Jeevitha Kandasamy, J. Jenitha, M.R. Mohanraj, Jenifer Amla. L., G. Mahalakshmi and Ponrekha M.
10.1 Introduction
10.2 System Model
10.3 Distributed Control Technique
10.3.1 ANN Tuned FOPID Distributed Controller
10.4 Results and Discussion
10.5 Conclusion
References
Appendix
11. Navigating Trust in Distributed Systems
Pooja Dehankar and Susanta Das
11.1 Introduction
11.1.1 Trust in Distributed Computing Ecosystem
11.1.2 Trust in the Internet of Things
11.2 Transparency Basics
11.3 Heterogeneous and Homogeneous DSs
11.3.1 Trust Concepts
11.3.2 Inadequacies with Security Mechanisms
11.4 Trust Management
11.5 Components’ Roles
11.6 User Authentication and Access Control
11.7 Trust Framework Pillars
11.8 Trust Metrics
11.9 Trust Models and Mechanisms
11.10 Trust in Sensor Networks
11.11 Apps of TMSs
11.12 DSs Advantages and Challenges
11.13 Artificial Intelligence for Trust Management
11.14 Conclusion
References
12. Trust in Distributed Systems, Distributed Ledger, Blockchain, and Related Technologies
Shikha Verma, Priya Mishra and Sandhya Avasthi
12.1 Introduction
12.2 Trust in Distributed Ledger
12.2.1 Double-Spending Problem
12.3 Trust Management in Edge-Based IoT Systems
12.4 A Taxonomy of Trust Computation Frameworks for Edge-Based IoT Systems
12.5 Trust in Distributed Databases
12.6 Trust in Blockchain
12.7 Serverless Computing
12.7.1 Functions of Serverless Computing
12.7.2 Serverless Computing Performance and Benefits
12.7.3 Trust in Serverless Computing
12.7.4 Compliance
12.7.5 Monitoring and Support
12.8 Trust in Cloud Computing
12.8.1 Data Security and Privacy
12.8.2 Service Reliability and Availability
12.8.3 Compliance and Regulatory Requirements
12.8.4 Performance and Scalability
12.9 Conclusion
References
13. Strategy to Improve Psychological Elements and Customer Service in Intelligent Health Systems
Mani Dublish, Anita Pati Mishra and Shikha Mittal
13.1 Introduction
13.2 Design with User in Mind
13.3 Designing Networked Medication Equipment with a Human Center
13.4 Testing Usability with End Users
13.4.1 Suggested Activities
13.4.2 Outcomes
13.5 Framework Required in Healthcare Fiction for Human-Based Design
13.6 A Necessary Step to Enable Quick Learning Processes
13.7 Requirement for a Planned Approach Setup
13.7.1 Techniques
13.7.2 Methodology Application in a Networked Health System
13.8 Limitations
13.9 Conclusion
References
Abbreviations
14. New Age Tech’s Backbone: Role of Distributed Systems in Paving Path for a Durable Future
Kadambri Agarwal and Saijal Dahiya
14.1 Introduction
14.2 Distributed Systems in Smart Grids
14.2.1 Advantages of Distributed Systems in Collaboration with Smart Grids
14.2.2 Disadvantages of Distributed Systems in Collaboration with Smart Grids
14.2.3 Working of Distributed Systems in Collaboration with Smart Grids
14.3 Distributed Systems in IoT
14.3.1 Advantages of Distributed Systems in Collaboration with IoT
14.3.2 Disadvantages of Distributed Systems in Collaboration with IoT
14.3.3 Working of Distributed Systems in Collaboration with IoT
14.4 Distributed Systems in Blockchain
14.4.1 Working of Distributed Systems in Collaboration with Blockchain
14.4.2 Advantages of Distributed Systems in Collaboration with Blockchain
14.5 Distributed Systems in Machine Learning
14.5.1 Distributed Learning Paradigms
14.5.1.1 Data Parallelism
14.5.1.2 Model Parallelism
14.5.2 Distributed Machine Learning Algorithms
14.5.2.1 Alternating Least Squares
14.5.2.2 Stochastic Gradient Descent
14.5.2.3 MapReduce
14.5.3 Potential Future Research Areas
14.5.3.1 Edge Computing
14.5.3.2 Federated Learning
14.6 Distributed Systems in AR
14.6.1 Application in Medicine
14.6.2 Role of Distributed Systems in AR
14.6.3 Working of Distributed Systems in Collaboration with AR
14.7 Distributed Systems in Distributed Databases
14.7.1 Working of Distributed Database
14.8 Distributed Systems in Metaverse
14.8.1 Working of Metaverse in Collaboration with Distributed Systems
14.9 Conclusion
References
15. Power Optimization in 4Bit Comparator Architecture Through Precomputation Technique
Adarsh Sharma, Suman Lata Tripathi, Deepika Ghai and Khilda Afifah
15.1 Introduction
15.1.1 Low-Power Design Techniques
15.1.1.1 Reduced Supply Voltage (Vdd)
15.1.1.2 Reduced Switching Activity
15.1.1.3 Reduced Capacitance
15.1.1.4 Reduced Static Current
15.1.1.5 Parallel and Pipelined Architectures
15.2 Comparator
15.2.1 4Bit Comparator
15.3 Precomputation Technique
15.3.1 Working Principle
15.4 Reported Precomputation Circuit
15.5 Comparator Design with Cadence Virtuoso
15.5.1 4Bit Comparator Schematic
15.5.2 4-Bit Comparator Using Precomputation Technique
15.5.3 Simulation Results
15.6 Future Scopes
15.7 Conclusion
Acknowledgement
References
Index
Back to Top
Preface
1. Introduction to Distributed Systems
K. Karthikeyan, S. Hemalatha and S. Vignesh
1.1 Introduction
1.1.1 Background and Context
1.1.2 Objectives of the Study
1.1.3 Scope and Limitations
1.1.4 Key Characteristics of Distributed Systems
1.1.4.1 Decentralization and Concurrency
1.1.4.2 Communication and Scalability
1.1.4.3 Fault Tolerance and Consistency
1.1.4.4 Security and Resource Sharing
1.1.5 Types of Distributed Systems
1.1.5.1 Systems for Clients and Servers
1.1.5.2 Peer-To-Peer Systems
1.1.5.3 Middleware-Based Systems
1.1.5.4 Three-Tier and N-Tier
1.1.5.5 Grid and Cloud Computing
1.1.6 Distributed Algorithms for Boolean Equations Over Networks
1.1.6.1 System of Boolean Equations Over a Network
1.1.6.2 Locally Private Distributed Algorithm
1.1.6.3 Consensus Projection for Linear Algebraic Equations
1.1.6.4 Affine Subspace Boolean Vector Search
1.1.6.5 Distributed SAT Verification
1.1.6.6 Distributed Equilibrium Computation of Boolean Networks
1.1.7 Effective Communication in Dispersed ML
1.1.7.1 Data Parallelism with Gradient Compression
1.1.7.2 Scheduling Issues
1.1.7.3 Decentralized Analytics
1.1.7.4 Federated Learning
1.1.7.5 Distributed Security and Privacy in Mobile Networks and IoT Systems
1.1.8 DS Applications Across a Wide Range of Domains
1.1.8.1 Web Services and Mobile Apps
1.1.8.2 Databases and Distributed File Storage Systems
1.1.8.3 Blockchain Platforms
1.1.8.4 Financial Services
1.1.8.5 Scientific Research and Simulation
1.1.9 Challenges and Considerations
1.1.9.1 Scalability of Distributed Systems in Various Applications
1.1.9.2 Integration with Existing Systems
1.1.9.3 Regulatory Compliance and Legal Framework
1.1.10 Future Prospects and Trends
1.1.10.1 Distributed Systems Emerging Technologies (Quantum Distributed Computing)
1.1.10.2 Research Advancements
1.1.10.3 Anticipated Benefits and Challenges
1.1.11 Conclusion
1.1.11.1 Recap of Key Findings
1.1.11.2 Implications for Healthcare Transformation
References
2. Topology in Network Technologies
Mohit Malik, Varun Dogra, Ramandeep Sandhu, Ratesh Kumar and Deepika Ghai
2.1 Introduction
2.2 Related Work
2.3 Network Topology Design
2.4 Advantages of Network Topologies
2.5 Case Studies of Network Topologies
2.6 Distributed and Parallel Computing in Network Topology
2.6.1 Challenges/Issues in Network Topology in Distributed Computing or Parallel Computing
2.7 Conclusion
References
3. Distributed Processing Technology and Advancements
Saranya. N., Shermy. R.P., Sindhu. V., Karthika Renuka. D. and Renugadevi. G.
3.1 Introduction
3.2 Distributed Processing in Modern Computing
3.3 Evolution of Distributed Processing
3.4 Key Concepts and Technologies Driving the Evolution of Distributed Processing
3.5 Recent Advancements in Distributed Processing
3.6 Changing Landscape of Computing
3.6.1 New Opportunities and Challenges for Developers and Businesses
3.6.2 Use of Distributed and Parallel Computing
3.7 Security and Privacy Considerations
3.7.1 Case Studies
3.7.2 Future Directions
3.8 Opportunities and Challenges in Distributed Computing
3.8.1 Future Directions and Opportunity
3.8.2 Challenges
3.9 Conclusion
References
4. Distributed System Architecture and Computing Models
Sandhya Avasthi and Suman Lata Tripathi
4.1 Introduction
4.2 Distributed System Architecture
4.2.1 Architecture Style
4.2.2 Middleware Organization
4.3 Middleware in Distributed Systems
4.3.1 Host Infrastructure Middleware
4.3.2 Distribution Middleware
4.3.3 Domain-Specific Middleware
4.3.4 Intelligent Middleware
4.4 Distributed Cloud Architecture
4.5 Distributed Machine Learning
4.6 Conclusion
References
5. Parallel Computing Models and Architecture
Aastha Sharma, Swati Sharma and Divya Mishra
5.1 Introduction
5.2 Evolution of Parallel Computing Models
5.2.1 Basic Approach Parallel Computing
5.2.2 Architecture
5.2.3 Flowchart Analysis
5.3 Parallel Computing Models
5.3.1 Complexity
5.3.2 Advantages
5.3.3 Disadvantages
5.3.4 Algorithmic Problem
5.3.4.1 Shared Memory Cache
5.3.4.2 Distributed Memory Model
5.3.4.3 Data Parallel Model
5.3.4.4 Task Parallel Model
5.3.5 Pipeline Model
5.3.6 Hybrid Models
References
6. Network Issues and High-Level Communication Tools in Distributed Computing
Sandhya Avasthi and Sachin Kumar Vishwakarma
6.1 Introduction
6.1.1 Common Network Issues in Distributed Computing
6.1.2 Group Communication in Distributed Systems
6.2 Latency in Distributed and Parallel Computing
6.3 Straggler Effect
6.4 Packet Loss in the Distributed Network
6.4.1 Implications of Packet Loss
6.4.2 Identifying Packet Loss
6.4.3 Tools to Measure Packet Loss
6.4.4 Techniques to Prevent Packet Loss
6.5 Network Congestion
6.5.1 Causes of Network Congestion and Implications
6.5.2 Network Congestion Implication
6.6 Communication Load
6.6.1 A Deterministic Strategy
6.6.2 Bidding Strategy
6.6.3 Drafting Strategy
6.6.4 Greedy Strategy
6.6.5 Threshold Strategy
6.7 Conclusion
References
7. Infinite Horizons: Empowering Business Education Through Metaverse
Shikha Mishra and Sujit S. Nair
7.1 Introduction
7.2 Challenges and Concerns
7.3 Rules of Law–Based Governance
7.4 Possible Implementations in Educational Framework of Metaverse Technology
7.5 Understanding Metaverse
7.6 Explore the Transformational Potential of Metaverse for Business Education
7.7 Innovation and Design Ideas Forum
7.8 Continuing Education and Career Development
7.9 Takeaways
7.10 Ethical and Legal Issues
7.11 Future Prospects and Innovations
7.12 Conclusion
References
8. Paradigm Shifts and Future Directions in Distributed Data Management for Decentralized Networks
Bishnu Kant Shukla, Bhupender Parashar, Vikas Kumar Singla and Shivam Verma
8.1 Introduction
8.2 Paradigm Shifts in Distributed Data Management
8.2.1 Evolution of Data Management Paradigms
8.2.2 Impact of Decentralized Architectures
8.2.3 Key Technological Drivers
8.3 Emergent Architectures and Frameworks
8.3.1 Overview of Emergent Architectures
8.3.2 Domain-Specific Languages in Data Analytics
8.4 Integration of IoT
8.4.1 IoT Ecosystem and Data Management Challenges
8.5 Edge Computing and Big Data Analytics
8.5.1 Benefits of Edge Computing
8.5.2 Big Data Management in Distributed Systems
8.6 Data Aggregation and Summarization Techniques
8.6.1 Importance of Data Aggregation
8.6.2 Case Studies and Practical Implementations
8.7 Advanced Applications and Case Studies
8.7.1 Domestic and Industrial Applications
8.8 Future Directions in Distributed Data Management
8.8.1 Blockchain for Enhanced Data Security
8.8.2 AI and ML in Data Analytics
8.8.3 Prospective Research Avenues
8.9 Conclusion
References
9. Autonomy and Adaptive Architectures in Distributed Systems
Bishnu Kant Shukla, Bhupender Parashar, Vikas Kumar Singla and Shivam Verma
9.1 Introduction
9.2 Conceptual Framework
9.2.1 Definition of Key Concepts
9.2.1.1 Autonomy
9.2.1.2 Adaptive Architectures
9.2.1.3 Multiagent Systems
9.2.1.4 The Evolutionary Trajectory of MAS
9.2.2 Importance of Decentralized Paradigms
9.3 Architectural Models
9.3.1 Overview of Emergent Architectures
9.3.2 Agent-Based Models
9.3.2.1 Agent Architecture and Control Flow
9.4 Adaptive Strategies in Distributed Systems
9.4.1 Mechanisms for System Resilience and Scalability
9.5 Case Studies
9.5.1 Smart Grids
9.5.2 Geographical and Functional Infrastructure Interdependence
9.5.3 Water Distribution Systems
9.5.3.1 Adaptive Water Management
9.5.3.2 Leak Detection and Management
9.5.3.3 Water Quality Monitoring
9.5.4 Transportation Systems
9.5.4.1 Intelligent Transportation Systems
9.5.4.2 Traffic Management
9.5.4.3 Public Transportation
9.6 Application of MASs
9.6.1 Autonomous Surface Ships
9.6.2 Unmanned Surface Vehicles
9.6.3 Swarm Robotics
9.6.4 Autonomous Underwater Vehicles
9.6.5 Hybrid MASs
9.6.6 Common Coordination Models in MAS
9.6.6.1 Centralized Coordination
9.6.6.2 Decentralized Coordination
9.6.6.3 Hierarchical Coordination
9.6.6.4 Market-Based Coordination
9.6.6.5 Contract Net Protocol
9.7 Autonomous Navigation Systems
9.7.1 Examples of Recently Developed USVs
9.8 Collision Avoidance Algorithms
9.8.1 Comparison of Collision Avoidance Algorithms
9.8.2 Implementation of TBA
9.9 Network Properties in Distributed Systems
9.9.1 Small-World Networks
9.9.2 Scale-Free Networks
9.9.3 Robustness and Fault Tolerance
9.9.4 Scalability
9.9.5 Application-Specific Design
9.10 Challenges and Future Directions
9.10.1 Security and Privacy Considerations
9.10.2 Integration with Emerging Technologies
9.10.2.1 Blockchain
9.10.2.2 Artificial Intelligence
9.10.2.3 Internet of Things
9.11 Conclusion
References
10. Distributed Consensus Frequency Control in Networked Microgrid
Jeevitha Kandasamy, J. Jenitha, M.R. Mohanraj, Jenifer Amla. L., G. Mahalakshmi and Ponrekha M.
10.1 Introduction
10.2 System Model
10.3 Distributed Control Technique
10.3.1 ANN Tuned FOPID Distributed Controller
10.4 Results and Discussion
10.5 Conclusion
References
Appendix
11. Navigating Trust in Distributed Systems
Pooja Dehankar and Susanta Das
11.1 Introduction
11.1.1 Trust in Distributed Computing Ecosystem
11.1.2 Trust in the Internet of Things
11.2 Transparency Basics
11.3 Heterogeneous and Homogeneous DSs
11.3.1 Trust Concepts
11.3.2 Inadequacies with Security Mechanisms
11.4 Trust Management
11.5 Components’ Roles
11.6 User Authentication and Access Control
11.7 Trust Framework Pillars
11.8 Trust Metrics
11.9 Trust Models and Mechanisms
11.10 Trust in Sensor Networks
11.11 Apps of TMSs
11.12 DSs Advantages and Challenges
11.13 Artificial Intelligence for Trust Management
11.14 Conclusion
References
12. Trust in Distributed Systems, Distributed Ledger, Blockchain, and Related Technologies
Shikha Verma, Priya Mishra and Sandhya Avasthi
12.1 Introduction
12.2 Trust in Distributed Ledger
12.2.1 Double-Spending Problem
12.3 Trust Management in Edge-Based IoT Systems
12.4 A Taxonomy of Trust Computation Frameworks for Edge-Based IoT Systems
12.5 Trust in Distributed Databases
12.6 Trust in Blockchain
12.7 Serverless Computing
12.7.1 Functions of Serverless Computing
12.7.2 Serverless Computing Performance and Benefits
12.7.3 Trust in Serverless Computing
12.7.4 Compliance
12.7.5 Monitoring and Support
12.8 Trust in Cloud Computing
12.8.1 Data Security and Privacy
12.8.2 Service Reliability and Availability
12.8.3 Compliance and Regulatory Requirements
12.8.4 Performance and Scalability
12.9 Conclusion
References
13. Strategy to Improve Psychological Elements and Customer Service in Intelligent Health Systems
Mani Dublish, Anita Pati Mishra and Shikha Mittal
13.1 Introduction
13.2 Design with User in Mind
13.3 Designing Networked Medication Equipment with a Human Center
13.4 Testing Usability with End Users
13.4.1 Suggested Activities
13.4.2 Outcomes
13.5 Framework Required in Healthcare Fiction for Human-Based Design
13.6 A Necessary Step to Enable Quick Learning Processes
13.7 Requirement for a Planned Approach Setup
13.7.1 Techniques
13.7.2 Methodology Application in a Networked Health System
13.8 Limitations
13.9 Conclusion
References
Abbreviations
14. New Age Tech’s Backbone: Role of Distributed Systems in Paving Path for a Durable Future
Kadambri Agarwal and Saijal Dahiya
14.1 Introduction
14.2 Distributed Systems in Smart Grids
14.2.1 Advantages of Distributed Systems in Collaboration with Smart Grids
14.2.2 Disadvantages of Distributed Systems in Collaboration with Smart Grids
14.2.3 Working of Distributed Systems in Collaboration with Smart Grids
14.3 Distributed Systems in IoT
14.3.1 Advantages of Distributed Systems in Collaboration with IoT
14.3.2 Disadvantages of Distributed Systems in Collaboration with IoT
14.3.3 Working of Distributed Systems in Collaboration with IoT
14.4 Distributed Systems in Blockchain
14.4.1 Working of Distributed Systems in Collaboration with Blockchain
14.4.2 Advantages of Distributed Systems in Collaboration with Blockchain
14.5 Distributed Systems in Machine Learning
14.5.1 Distributed Learning Paradigms
14.5.1.1 Data Parallelism
14.5.1.2 Model Parallelism
14.5.2 Distributed Machine Learning Algorithms
14.5.2.1 Alternating Least Squares
14.5.2.2 Stochastic Gradient Descent
14.5.2.3 MapReduce
14.5.3 Potential Future Research Areas
14.5.3.1 Edge Computing
14.5.3.2 Federated Learning
14.6 Distributed Systems in AR
14.6.1 Application in Medicine
14.6.2 Role of Distributed Systems in AR
14.6.3 Working of Distributed Systems in Collaboration with AR
14.7 Distributed Systems in Distributed Databases
14.7.1 Working of Distributed Database
14.8 Distributed Systems in Metaverse
14.8.1 Working of Metaverse in Collaboration with Distributed Systems
14.9 Conclusion
References
15. Power Optimization in 4Bit Comparator Architecture Through Precomputation Technique
Adarsh Sharma, Suman Lata Tripathi, Deepika Ghai and Khilda Afifah
15.1 Introduction
15.1.1 Low-Power Design Techniques
15.1.1.1 Reduced Supply Voltage (Vdd)
15.1.1.2 Reduced Switching Activity
15.1.1.3 Reduced Capacitance
15.1.1.4 Reduced Static Current
15.1.1.5 Parallel and Pipelined Architectures
15.2 Comparator
15.2.1 4Bit Comparator
15.3 Precomputation Technique
15.3.1 Working Principle
15.4 Reported Precomputation Circuit
15.5 Comparator Design with Cadence Virtuoso
15.5.1 4Bit Comparator Schematic
15.5.2 4-Bit Comparator Using Precomputation Technique
15.5.3 Simulation Results
15.6 Future Scopes
15.7 Conclusion
Acknowledgement
References
Index
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