Collective intelligence for smart cities / Chun Ho Wu [and four others].

Format:

Book

Author/Creator:

Wu, Chen Ho, author.

Series:

Intelligent Data-Centric Systems

Language:

English

Subjects (All):

Swarm intelligence.

Smart cities.

Cloud computing.

Big data.

Physical Description:

1 online resource (248 pages)

Place of Publication:

London, England ; San Diego, California ; Cambridge, Massachusetts : Academic press, [2022]

Summary:

"Collective Intelligence for Smart Cities begins with an overview of the fundamental issues and concepts of smart cities. Surveying the current state-of-the-art research in the field, the book delves deeply into key smart city developments such as health and well-being, transportation, safety, energy, environment and sustainability. In addition, the book focuses on the role of IoT cloud computing and big data, specifically in smart city development. Users will find a unique, overarching perspective that ties together these concepts based on collective intelligence, a concept for quantifying mass activity familiar to many social science and life science researchers. Sections explore how group decision-making emerges from the consensus of the collective, collaborative and competitive activities of many individuals, along with future perspectives."-- Title details screen.

Contents:

Intro

Collective Intelligence for Smart Cities

Copyright

Dedication

Contents

List of figures and tables

List of figures

List of tables

About the authors

Acknowledgments

Introduction

References

Chapter One: Data streams-Concepts, definitions, models and applications in smart cities

1.1. Introduction

1.2. Types of stream processing and levels of event processing

1.3. Stream processing models

1.4. Anomaly detection methods from datastreams

1.4.1. Matching techniques

1.4.2. Statistical approaches

1.4.3. Regressive approaches

1.5. Challenges of real-time stream processing: Big data vs big data streams

1.5.1. Characteristics of big data-The standard 5Vs

1.5.2. Characteristics of IoT big data streams-The 5Vs revisited

1.5.3. A new challenge: Anonymity and privacy

1.6. Various levels of stream processing and their goals

1.6.1. Processing goals

1.6.1.1. Filtering

1.6.1.2. Event detection

1.6.1.3. Data enrichment

1.6.1.4. Data analysis

1.6.1.5. Application processing

1.6.2. Processing levels

1.6.2.1. Data sensing

1.6.2.2. Data preprocessing

1.6.2.3. Edge/fog processing

1.6.2.4. The cloud computing

Every project starts small

Open source platforms

Kafka

Spark

Flink

Commercial platforms

1.6.2.5. The client

1.7. Architecture decisions-The modern IoT client

1.8. Data enrichment from datastreams for enhanced reasoning

1.9. IoT streaming in Smart City applications: Road quality and safe driving

Chapter Two: Stream processing in the semantic web

2.1. Semantic data streams

2.1.1. Basic concept

2.1.2. Semantic web

2.1.3. RDF-stream

2.1.4. TripleWave

2.2. Semantic stream processing

2.2.1. DSMS approaches

2.2.1.1. C-SPARQL

2.2.1.2. SPARQLstream

2.2.1.3. CQELS

2.2.2. CEP approaches.

2.2.2.1. EP-SPARQL and ETALIS

2.2.2.2. CQELS-EP

2.2.3. Recent developments

2.2.4. Summative evaluation on semantic data streams

2.3. Reasoning over data streams

2.3.1. Inference

2.3.2. Production rules

2.3.2.1. Drools

2.3.2.2. Jena

2.3.2.3. Easy-rules

2.3.3. Event-condition-action (ECA) rules

2.3.4. Recent developments

2.3.5. Summative evaluation on inference and reasoning

Chapter Three: State-of-the-art research and development on Smart City

3.1. Definitions of Smart City

3.2. Characteristics of Smart City

3.3. Measurement for Smart City

3.4. Smart City implementation: From the view of IoT and Big Data Analytics

3.5. Practices of smart cities

Chapter Four: Internet of things (IoT), cloud computing, and big data collective intelligence for smart cities

4.1. Introduction of smart cities

4.2. Integration of IoT and cloud computing in smart cities

4.3. Big data in smart city

4.4. Application areas

4.5. Challenges for adopting IoT, cloud computing, and big data collective intelligence in a smart city

Chapter Five: Smart energy network

5.1. Smart home

5.2. Status of China and foreign countries

5.3. Market analysis and social value

5.4. Overall architecture of system design

5.5. Smart energy network solutions

5.6. IoT network layer and service management application layer of smart energy network

5.7. Case example

Chapter Six: Smart firefighting and fire protection

6.1. Status of China

6.2. Market analysis and social value

6.2.1. Peoples lives and property protection

6.2.2. Water saving

6.2.3. Employment promotion

6.2.4. Intellectual property products innovation and promotion

6.3. Overall architecture of system design

6.4. Smart city firefighting IoT solution.

6.5. Smart city firefighting IoT network layer

6.6. Firefighting IoT service management application layer

6.6.1. Functional requirements

6.6.2. Performance requirements

6.7. Conclusion

Chapter Seven: Smart parking using mobile and IoT

7.1. Introduction

7.2. Architecture of the IAPNP

7.2.1. IoT infrastructure and functions

7.2.2. Core components and their functions

7.2.3. Main features and functions

7.2.3.1. WSAN for smart parking management system

7.2.3.2. WSAN-based middleware

7.2.3.3. Advanced automobile parking navigation system

7.2.3.4. NFC-enabled customer relationship management mobile app

7.3. Case study

7.3.1. Implementation of the IAPNP

7.3.2. A new experience of quality parking services

7.3.3. Electric parking/enquiry and reservation services for parking spaces for people with disabilities

7.3.4. Quality environmental monitoring and management

7.3.5. Automated green energy-conserving and security management

7.3.6. Real-time system analysis report

7.4. Conclusion

Chapter Eight: Crane selection for project cargo

8.1. Introduction

8.2. Research methodology

8.2.1. Module 1: Data collection and preparation

8.2.2. Module 2: Analytic hierarchy process

8.2.2.1. Hierarchy construction

8.2.2.2. Pairwise comparison

8.2.3. Module 3: Evaluation

8.3. Case study

8.3.1. Build-up database

8.3.2. Set up criterion

8.3.3. Interview

8.3.4. System application

8.3.5. Results and plan

8.4. Discussion

8.4.1. Improvement in the efficiency and effectiveness of the crane selection

8.4.2. Improvement in the service quality in ABC Limited

8.5. Conclusion

Chapter Nine: Transport, mobility, and delivery in smart cities: The vision of the TransAnalytics research project

9.1. Introduction

9.2. Related work.

9.2.1. Optimization, metaheuristics, and simulation methods

9.2.2. IoT analytics and collective intelligence

9.2.3. New transportation means

9.2.4. Uncertainty in T&amp

M

9.3. Context, research problems, and challenges

9.3.1. Research problems and challenges in city T&amp

9.3.2. Business strategies and opportunities in city T&amp

9.3.3. Research and development of dynamic delivery systems

9.3.4. Analysis of real-life case studies of the T&amp

M and benefits

9.4. The role of transport analytics in smart city T&amp

9.4.1. Descriptive transport analytics

9.4.2. Predictive transport analytics

9.4.3. Prescriptive transport analytics

9.4.4. The role of IoT analytics in transport analytics

9.4.4.1. IoT network in Smart City

9.4.4.2. Data lake and data warehouse of semantically enriched data

9.4.4.3. Deep cognitive analytics

9.5. Conclusions

Chapter Nine. References

Further reading

Chapter Ten: Blockchain in a Smart City: Its applications and a selection framework*

10.1. Introduction

10.2. Definition and overview of blockchain

10.3. Features of blockchain

10.4. Applications of blockchain in daily life

10.5. Different industrial criteria

10.5.1. Electronic health record

10.5.1.1. Concern 1: Security-Information confidentiality

10.5.1.2. Concern 2: Security-Data integrity

10.5.1.3. Concern 3: Usability-Accessibility

10.5.1.4. Concern 4: Usability-Operability

10.5.1.5. Concern 5: Performance efficiency-Lead time for blockchain synchronization

10.5.1.6. Concern 6: Reliability-Availability

10.5.2. Retail in luxury goods

10.5.2.1. Concern 1: Products-Guarantees and integrity

10.5.2.2. Concern 2: Products-Stability

10.5.2.3. Concern 3: Usability-Accessibility

10.5.3. Automotive supply chain management.

10.5.3.1. Concern 1: Security-Authenticity

10.5.3.2. Concern 2: Security-Integrity

10.6. A case study in blockchain selection in the context of healthcare

10.6.1. The first level on the healthcare industry

10.6.2. The second level of the healthcare industry

10.6.3. The third level of best-worst method concern to the consensus algorithms

10.6.4. Subcriterion 1: Authenticity

10.6.5. Subcriterion 2: Confidentiality

10.6.6. Subcriterion 3: Integrity

10.6.7. Subcriterion 4: Guarantees

10.6.8. Subcriterion 5: Stability

10.6.9. Subcriterion 6: Time behavior

10.6.10. Subcriterion 7: Reputation

10.6.11. Subcriterion 8: Organizational structure

10.6.12. Subcriterion 9: Availability

10.6.13. Subcriterion 10: Accessibility

10.6.14. Subcriterion 11: Operability.

10.7. Results and limitations

Chapter Eleven: Conclusions and future directions of research

Acronyms and glossary

Index.

Notes:

Description based on print version record and other sources.

Includes bibliographical references and index.

Other Format:

Print version: WU, Chun Ho Collective Intelligence for Smart Cities

ISBN:

9780128202753

0128202750