UID:
almahu_9949328461402882
Format:
1 online resource (248 pages)
ISBN:
0-12-820275-0
Series Statement:
Intelligent Data-Centric Systems
Content:
"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."--
Note:
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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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& -- M -- 9.3. Context, research problems, and challenges -- 9.3.1. Research problems and challenges in city T& -- M -- 9.3.2. Business strategies and opportunities in city T& -- M -- 9.3.3. Research and development of dynamic delivery systems -- 9.3.4. Analysis of real-life case studies of the T& -- M and benefits -- 9.4. The role of transport analytics in smart city T& -- M -- 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 -- 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 -- References -- Chapter Eleven: Conclusions and future directions of research -- Acronyms and glossary -- Index.
Additional Edition:
Print version: WU, Chun Ho Collective Intelligence for Smart Cities San Diego : Elsevier Science & Technology,c2022 ISBN 9780128201398
Language:
English
Bookmarklink
