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Biblioteca

MARC Lista (tabellare)
2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
Pubbl/distr/stampa Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Descrizione fisica 1 online resource (114 pages)
Disciplina 005.117
Soggetto topico Distributed databases
Object-oriented methods (Computer science) - Distributed processing
Electronic data processing
Soggetto genere / forma Electronic books.
ISBN 1-5386-4141-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996280537403316
Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
2018 IEEE 22nd International Enterprise Distributed Object Computing Workshop : 16-19 October 2018, Stockholm, Sweden / / IEEE Computer Society
Pubbl/distr/stampa Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Descrizione fisica 1 online resource (114 pages)
Disciplina 005.117
Soggetto topico Distributed databases
Object-oriented methods (Computer science) - Distributed processing
Electronic data processing
Soggetto genere / forma Electronic books.
ISBN 1-5386-4141-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910294556403321
Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Los Alamitos, California : , : IEEE, , 2019
Descrizione fisica 1 online resource : illustrations
Disciplina 005.758
Soggetto topico Distributed databases
Electronic data processing - Distributed processing
Object-oriented methods (Computer science)
ISBN 1-7281-4598-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop
Record Nr. UNINA-9910389510203321
Los Alamitos, California : , : IEEE, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop : EDOCW 2019 : proceedings : Paris, France, 28-31 October 2019 / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Los Alamitos, California : , : IEEE, , 2019
Descrizione fisica 1 online resource : illustrations
Disciplina 005.758
Soggetto topico Distributed databases
Electronic data processing - Distributed processing
Object-oriented methods (Computer science)
ISBN 1-7281-4598-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop
Record Nr. UNISA-996574766603316
Los Alamitos, California : , : IEEE, , 2019
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
23rd International Conference on Distributed Computing and Networking / / Association for Computing Machinery
23rd International Conference on Distributed Computing and Networking / / Association for Computing Machinery
Pubbl/distr/stampa New York, NY : , : Association for Computing Machinery, , 2022
Descrizione fisica 1 online resource (298 pages)
Disciplina 005.7
Soggetto topico Distributed databases
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910548282103321
New York, NY : , : Association for Computing Machinery, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu
Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / / Hien Luu
Autore Hien Luu
Edizione [Second edition.]
Pubbl/distr/stampa New York, New York : , : Apress, , [2021]
Descrizione fisica 1 online resource (445 pages)
Disciplina 005.7
Soggetto topico Big data
Distributed databases
Open source software
Machine learning
ISBN 1-4842-7383-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Apache Spark -- Overview -- History -- Spark Core Concepts and Architecture -- Spark Cluster and Resource Management System -- Spark Applications -- Spark Drivers and Executors -- Spark Unified Stack -- Spark Core -- Spark SQL -- Spark Structured Streaming -- Spark MLlib -- Spark GraphX -- SparkR -- Apache Spark 3.0 -- Adaptive Query Execution Framework -- Dynamic Partition Pruning (DPP) -- Accelerator-aware Scheduler -- Apache Spark Applications -- Spark Example Applications -- Apache Spark Ecosystem -- Delta Lake -- Koalas -- MLflow -- Summary -- Chapter 2: Working with Apache Spark -- Downloading and Installation -- Downloading Spark -- Installing Spark -- Spark Scala Shell -- Spark Python Shell -- Having Fun with the Spark Scala Shell -- Useful Spark Scala Shell Command and Tips -- Basic Interactions with Scala and Spark -- Basic Interactions with Scala -- Spark UI and Basic Interactions with Spark -- Spark UI -- Basic Interactions with Spark -- Introduction to Collaborative Notebooks -- Create a Cluster -- Create a Folder -- Create a Notebook -- Setting up Spark Source Code -- Summary -- Chapter 3: Spark SQL: Foundation -- Understanding RDD -- Introduction to the DataFrame API -- Creating a DataFrame -- Creating a DataFrame from RDD -- Creating a DataFrame from a Range of Numbers -- Creating a DataFrame from Data Sources -- Creating a DataFrame by Reading Text Files -- Creating a DataFrame by Reading CSV Files -- Creating a DataFrame by Reading JSON Files -- Creating a DataFrame by Reading Parquet Files -- Creating a DataFrame by Reading ORC Files -- Creating a DataFrame from JDBC -- Working with Structured Operations -- Working with Columns -- Working with Structured Transformations.
select(columns) -- selectExpr(expressions) -- filler(condition), where(condition) -- distinct, dropDuplicates -- sort(columns), orderBy(columns) -- limit(n) -- union(otherDataFrame) -- withColumn(colName, column) -- withColumnRenamed(existingColName, newColName) -- drop(columnName1, columnName2) -- sample(fraction), sample(fraction, seed), sample(fraction, seed, withReplacement) -- randomSplit(weights) -- Working with Missing or Bad Data -- Working with Structured Actions -- describe(columnNames) -- Introduction to Datasets -- Creating Datasets -- Working with Datasets -- Using SQL in Spark SQL -- Running SQL in Spark -- Writing Data Out to Storage Systems -- The Trio: DataFrame, Dataset, and SQL -- DataFrame Persistence -- Summary -- Chapter 4: Spark SQL: Advanced -- Aggregations -- Aggregation Functions -- Common Aggregation Functions -- count(col) -- countDistinct(col) -- min(col), max(col) -- sum(col) -- sumDistinct(col) -- avg(col) -- skewness(col), kurtosis(col) -- variance(col), stddev(col) -- Aggregation with Grouping -- Multiple Aggregations per Group -- Collection Group Values -- Aggregation with Pivoting -- Joins -- Join Expression and Join Types -- Working with Joins -- Inner Joins -- Left Outer Joins -- Right Outer Joins -- Outer Joins (a.k.a. Full Outer Joins) -- Left Anti-Joins -- Left Semi-Joins -- Cross (a.k.a. Cartesian) -- Dealing with Duplicate Column Names -- Use Original DataFrame -- Renaming Column Before Joining -- Using Joined Column Name -- Overview of Join Implementation -- Shuffle Hash Join -- Broadcast Hash Join -- Functions -- Working with Built-in Functions -- Working with Date Time Functions -- Working with String Functions -- Working with Math Functions -- Working with Collection Functions -- Working with Miscellaneous Functions -- Working with User-Defined Functions (UDFs) -- Advanced Analytics Functions.
Aggregation with Rollups and Cubes -- Rollups -- Cubes -- Aggregation with Time Windows -- Window Functions -- Exploring Catalyst Optimizer -- Logical Plan -- Physical Plan -- Catalyst in Action -- Project Tungsten -- Summary -- Chapter 5: Optimizing Spark Applications -- Common Performance Issues -- Spark Configurations -- Different Ways of Setting Properties -- Different Kinds of Properties -- Viewing Spark Properties -- Spark Memory Management -- Spark Driver -- Spark Executor -- Leverage In-Memory Computation -- When to Persist and Cache Data -- Persistence and Caching APIs -- Persistence and Caching Example -- Understanding Spark Joins -- Broadcast Hash Join -- Shuffle Sort Merge Join -- Adaptive Query Execution -- Dynamically Coalescing Shuffle Partitions -- Dynamically Switching Join Strategies -- Dynamically Optimizing Skew Joins -- Summary -- Chapter 6: Spark Streaming -- Stream Processing -- Concepts -- Data Delivery Semantics -- Notion of Time -- Windowing -- Stream Processing Engine Landscape -- Spark Streaming Overview -- Spark DStream -- Spark Structured Streaming -- Overview -- Core Concepts -- Data Sources -- Output Modes -- Trigger Types -- Data Sinks -- Watermarking -- Structured Streaming Applications -- Streaming DataFrame Operations -- Selection, Project, Aggregation Operations -- Join Operations -- Working with Data Sources -- Working with a Socket Data Source -- Working with a Rate Data Source -- Working with a File Data Source -- Working with a Kafka Data Source -- Working with a Custom Data Source -- Working with Data Sinks -- Working with a File Data Sink -- Working with a Kafka Data Sink -- Working with a foreach Data Sink -- Working with a Console Data Sink -- Working with a Memory Data Sink -- Output Modes -- Triggers -- Summary -- Chapter 7: Advanced Spark Streaming -- Event Time.
Fixed Window Aggregation over an Event Time -- Sliding Window Aggregation over Event Time -- Aggregation State -- Watermarking: Limit State and Handle Late Data -- Arbitrary Stateful Processing -- Arbitrary Stateful Processing with Structured Streaming -- Handling State Timeouts -- Arbitrary State Processing in Action -- Extracting Patterns with mapGroupsWithState -- User Sessionization with flatMapGroupsWithState -- Handling Duplicate Data -- Fault Tolerance -- Streaming Application Code Change -- Spark Runtime Change -- Streaming Query Metrics and Monitoring -- Streaming Query Metrics -- Monitoring Streaming Queries via Callback -- Monitoring Streaming Queries via Visualization UI -- Streaming Query Summary Information -- Streaming Query Detailed Statistics Information -- Troubleshooting Streaming Query -- Summary -- Chapter 8: Machine Learning with Spark -- Machine Learning Overview -- Machine Learning Terminologies -- Machine Learning Types -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Machine Learning Development Process -- Spark Machine Learning Library -- Machine Learning Pipelines -- Transformers -- Estimators -- Pipeline -- Pipeline Persistence: Saving and Loading -- Model Tuning -- Speeding Up Model Tuning -- Model Evaluators -- Machine Learning Tasks in Action -- Classification -- Model Hyperparameters -- Example -- Regression -- Model Hyperparameters -- Example -- Recommendation -- Model Hyperparameters -- Example -- Deep Learning Pipeline -- Summary -- Chapter 9: Managing the Machine Learning Life Cycle -- The Rise of MLOps -- MLOps Overview -- MLflow Overview -- MLflow Components -- MLflow in Action -- MLflow Tracking -- MLflow Projects -- MLflow Models -- MLflow Model Registry -- Model Deployment and Prediction -- Summary -- Index.
Record Nr. UNINA-9910506385403321
Hien Luu  
New York, New York : , : Apress, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain & distributed ledger technologies
Blockchain & distributed ledger technologies
Pubbl/distr/stampa [Washington, D.C.] : , : GAO - Science, Technology Assessment, and Analytics, , 2019
Descrizione fisica 1 online resource (2 pages) : color illustrations
Collana Science & tech spotlight
Soggetto topico Blockchains (Databases)
Distributed databases
Electronic funds transfers - Security measures
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Blockchain and distributed ledger technologies
Record Nr. UNINA-9910713674203321
[Washington, D.C.] : , : GAO - Science, Technology Assessment, and Analytics, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain, smart contracts and distributed ledger technologies in the built environment : key concepts, technologies, and applications / / edited by Mohamad Kassem, Abel Maciel, Daniel M. Hall
Blockchain, smart contracts and distributed ledger technologies in the built environment : key concepts, technologies, and applications / / edited by Mohamad Kassem, Abel Maciel, Daniel M. Hall
Edizione [First published 2025]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering and Technology, , 2025
Descrizione fisica 1 online resource (xxiv, 442 pages) : illustrations (chiefly color), maps
Disciplina 338.927
Collana Built Environment Series
Soggetto topico Appropriate technology
Blockchains (Databases)
Built environment - Technological innovations
Construction industry - Technological innovations
Infrastructure (Economics) - Technological innovations
Smart contracts
Distributed databases
ISBN 9781839538353
183953835X
9781837244065
1837244065
9781839538346
1839538341
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; 1. Introduction / Mohamad Kassem, Abel Maciel and Daniel Hall -- ; 2. Distributed ledger technologies, blockchain and smart contracts: technical foundations and key concepts / Mahir Msawil, Mohamad Kassem and David Greenwood -- ; 3. Socio-technical approach to blockchain adoption in construction: conceptual models and roadmaps for macro- and meso-scale implementation / Jennifer Li, David Greenwood and Mohamad Kassem -- ; 4. Enhancing information security and digital trust in construction: decentralisation and encryption approaches / Moumita Das, Jack C.P. Cheng and Xingyu Tao -- ; 5. Blockchain and tokenization of built assets / Lavinia Chiara Tagliabue, Paolo Mistrangelo and Algan Tezel -- ; 6. Digital building logbooks on the blockchain: first conceptualisation and future research directions / Theodoros Dounas, Daniel M. Hall, Dimosthenis Kifokeris, David Christie, Jens Hunhevicz, Firehiwot Kedir, Joseph Mante, Goran Sibenik, Marijana Sreckovic, Lavinia Chiara Tagliabue and Catherine De Wolf -- ; 7. Blockchain for energy management in seaport infrastructure: a microgrid case study / Alexander James Howe, Ioan Petri and Yacine Rezgui -- ; 8. Decentralising architectural design through data governance / Theodoros Dounas, Davide Lombardi and Hico McDonald -- ; 9. Blockchain in construction supply chain management / Liupengfei Wu, Jinying Xu and Weisheng Lu -- ; 10. Blockchain for data observability: integrating BIM, digital twins and enterprise CDEs / Abel Maciel and Klaudia Jaskula -- ; 11. Blockchain and contract administration in construction / Mahir Msawil, David Greenwood and Mohamad Kassem -- ; 12. Legal risks for blockchain applications in the built environment: a legal perspective / Gavin P. Johnson -- ; 13. Conclusions: cross-cutting themes, challenges and recommendations / Mohamad Kassem, Abel Maciel and Daniel Hall.
Record Nr. UNINA-9911034480203321
Stevenage : , : Institution of Engineering and Technology, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchains : empowering technologies and industrial applications
Blockchains : empowering technologies and industrial applications
Autore Al-Dulaimi Anwer
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (419 pages)
Disciplina 005.74
Altri autori (Persone) DobreOctavia A
YiZhilin
Collana IEEE Series on Digital and Mobile Communication Series
Soggetto topico Blockchains (Databases)
Distributed databases
ISBN 9781119781042
1119781043
9781119781028
1119781027
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- About the Editors -- About the Contributors -- Foreword -- Preface -- Chapter 1 Introduction -- 1.1 Exploring Blockchain Technology -- 1.2 Developing and Testing Blockchains: Software Development Approach -- 1.3 Blockchains and Cloud Integration -- 1.4 Blockchain and Mobile Networking -- 1.5 Open Architecture and Blockchains -- 1.6 Open API and Monetization of Mobile Network Infrastructure -- 1.6.1 Using Blockchain Technology to Tokenize API Access -- 1.6.2 Monetize Mobile Network Infrastructure -- 1.7 Resiliency of Current Blockchain Models -- 1.8 Next Evolution in Blockchain Functions -- 1.9 Book Objectives and Organization -- References -- Chapter 2 Enabling Technologies and Distributed Storage -- 2.1 Introduction -- 2.2 Data Storage -- 2.2.1 Distributed File Systems -- 2.2.2 Cloud Storage Systems -- 2.3 Blockchains -- 2.3.1 Building Elements of Blockchains -- 2.3.2 Mining in Blockchains -- 2.3.3 Blockchain‐Based Data Storage -- 2.3.4 Blockchain Types -- 2.4 Distributed Storage Systems -- 2.4.1 DSS Layers -- 2.4.2 Distributed Storage Challenges -- 2.4.2.1 Security -- 2.4.2.2 Reliability -- 2.4.2.3 Economic Incentives -- 2.4.2.4 Coordination -- 2.4.2.5 Monetization -- 2.4.3 DSS Implementations -- 2.4.4 DSS Use Cases -- 2.4.4.1 SCT dApps -- 2.4.4.2 SCT dApp Food Chain Example -- 2.4.5 Performance Evaluation of DSSs -- 2.5 The Future of DSS -- 2.6 Concluding Considerations -- Acronyms -- References -- Chapter 3 Managing Consensus in Distributed Transaction Systems -- 3.1 Ledgers and Consensus -- 3.1.1 Distributed Ledgers -- 3.1.2 Consensus -- 3.1.2.1 Consensus for Consistent Data Storage -- 3.1.2.2 Consensus for Transaction Ordering -- 3.1.2.3 Consensus as a Defense Against Bad Actors -- 3.1.3 Industrial Case Study -- 3.2 Consensus Protocols, Then and Now -- 3.2.1 State Machine Replication.
3.2.2 Byzantine Fault Tolerance -- 3.2.3 Nakamoto Consensus -- 3.2.4 Hybrid Consensus -- 3.3 Cryptographic Nakamoto Proofs -- 3.3.1 Proof of Work -- 3.3.2 Proof of Stake -- 3.3.2.1 Chain‐Based Proof of Stake -- 3.3.3 Proof of Capacity -- 3.3.4 Proof of Time -- 3.4 Challenges to Scalability -- 3.4.1 Communication Complexity -- 3.4.2 Asynchronous Context -- 3.4.3 Participant Churn -- 3.4.4 The Blockchain Scalability Problem -- 3.5 Block Size and Propagation -- 3.5.1 Larger Blocks -- 3.5.2 Shorter Rounds -- 3.6 Committees, Groups, and Sharding -- 3.6.1 Committees -- 3.6.2 Groups -- 3.6.3 Sharding -- 3.7 Transaction Channels -- 3.7.1 Trust‐Weighted Agreement -- 3.7.2 Off‐Chain Transactions -- 3.7.3 Lightning Network -- 3.8 Checkpointing and Finality Gadgets -- 3.8.1 Probabilistic Finality -- 3.8.2 Checkpointing -- 3.8.3 Finality Gadgets -- 3.9 Bootstrapping -- 3.9.1 Networking -- 3.9.2 Data -- 3.10 Future Trends -- 3.10.1 Private Consensus -- 3.10.2 Improved Oracles -- 3.10.3 Streaming Consensus -- 3.11 Conclusion -- References -- Chapter 4 Security, Privacy, and Trust of Distributed Ledgers Technology -- 4.1 CAP Theorem and DLT -- 4.1.1 Distributed Database System (DDBS) -- 4.1.2 Evolution of DDBS to the Blockchain -- 4.1.3 Public vs Permissioned Blockchains -- 4.1.4 Evolution of Blockchain to the DLTs -- 4.2 CAP Theorem -- 4.2.1 CAP Theorem and Consensus Algorithms -- 4.2.2 Availability and Partition Tolerance (AP) Through PoW -- 4.2.3 Consistency and Partition Tolerance (CP) Through PBFT -- 4.2.4 Consistency and Availability (CA) -- 4.3 Security and Privacy of DLT -- 4.3.1 Security Differs by DLT -- 4.3.2 Security and Requirements for Transactions -- 4.3.3 Security Properties of DLT -- 4.3.4 Challenges and Trends in DLT Security -- 4.4 Security in DLT -- 4.4.1 Governance Scenario Security -- 4.4.2 DLT Application Security -- 4.4.3 DLT Data Security.
4.4.4 Transactions Security -- 4.4.5 DLT Infrastructure Security -- 4.5 Privacy Issues in DLT -- 4.6 Cyberattacks and Fraud -- 4.6.1 Challenges -- 4.6.2 Key Privacy and Security Techniques in DLT -- 4.7 DLT Implementation and Blockchain -- 4.7.1 Cryptocurrencies and Bitcoin -- 4.7.1.1 Origin of Blockchain -- 4.7.1.2 Bitcoin -- 4.7.1.3 Monero -- 4.7.2 Blockchain and Smart Contracts -- 4.7.3 Typical Blockchain Systems -- 4.7.3.1 Ethereum Classic (ETC) -- 4.7.3.2 Ethereum (ETH) -- 4.7.3.3 Extensibility of Blockchain and DLT -- 4.7.4 Origin of Blockchain 3.0 -- 4.7.5 Overview of Hyperledger Fabric -- 4.8 DLT of IOTA Tangle -- 4.9 Trilemma of Security, Scalability, and Decentralization -- 4.9.1 First‐Generation Solutions: BTC/BCH -- 4.9.2 Second‐Generation Solutions: ETH/BSC -- 4.9.3 Threats in DLT and Blockchain Networks -- 4.10 Security Architecture in DLT and Blockchain -- 4.10.1 Threat Model in LDT -- 4.11 Research Trends and Challenges -- References -- Chapter 5 Blockchains for Business - Permissioned Blockchains# -- 5.1 Introduction -- 5.2 Major Architectures of Permissioned Blockchains -- 5.2.1 Order-Execute -- 5.2.2 Simulate-Order-Validate -- 5.2.2.1 Simulation Phase -- 5.2.2.2 Ordering Phase -- 5.2.2.3 Validation Phase -- 5.2.3 Comparison and Analysis -- 5.3 Improving Order-Execute Using Deterministic Concurrency Control -- 5.3.1 Calvin -- 5.3.2 BOHM -- 5.3.3 BCDB -- 5.3.3.1 Simulation Phase -- 5.3.3.2 Commit Phase -- 5.3.4 Aria -- 5.3.4.1 Simulation Phase -- 5.3.4.2 Analysis Phase -- 5.3.4.3 Commit Phase -- 5.3.5 Comparison and Analysis -- 5.4 Improving Execute-Order-Validate -- 5.4.1 Transaction Reordering -- 5.4.2 Early Abort -- 5.4.3 FastFabric -- 5.5 Scale‐Out by Sharding -- 5.6 Trends of Development -- 5.6.1 Trusted Hardware -- 5.6.2 Chainify DBMSs -- Acronyms -- References.
Chapter 6 Attestation Infrastructures for Automotive Cybersecurity and Vehicular Applications of Blockchains -- 6.1 Introduction -- 6.2 Cybersecurity of Automotive and IoT Systems -- 6.2.1 Protecting Assets in Smart Cars -- 6.2.2 Reported Cases -- 6.2.3 Trusted Computing Base for Automotive Cybersecurity -- 6.2.4 Special Hardware for Security -- 6.2.5 Truthful Reporting: The Challenge of Attestations -- 6.3 The TCB and Development of Trusted Hardware -- 6.3.1 The Trusted Computing Base -- 6.3.2 The Trusted Platform Module (TPM) -- 6.3.3 Resource‐Constrained Automotive Systems: Thin TPMs -- 6.3.4 Virtualized TPMs for ECUs -- 6.3.5 The DICE Model and Cyber‐Resilient Systems -- 6.4 Attestations in Automotive Systems -- 6.4.1 A Reference Framework for Attestations -- 6.4.2 Entities, Roles, and Actors -- 6.4.3 Variations in Evidence Collations and Deliveries -- 6.4.4 Composite Attestations for Automotive Systems -- 6.4.5 Appraisal Policies -- 6.5 Vehicle Wallets for Blockchain Applications -- 6.5.1 Vehicular Application Scenarios -- 6.5.2 Protection of Keys in Automotive Wallets -- 6.5.3 Types of Evidence from Wallets -- 6.6 Blockchain Technology for Future Attestation Infrastructures -- 6.6.1 Challenges in the Supply‐Chain of Endorsements -- 6.6.2 Decentralized Infrastructures -- 6.6.3 Example of Verifier Tasks -- 6.6.4 Notarization Records and Location Records -- 6.6.5 Desirable Properties of Blockchain‐Based Approaches -- 6.6.6 Information within the Notarization Record -- 6.6.7 Information in the Location Record -- 6.6.8 The Compliance Certifications Record -- 6.7 Areas for Innovation and Future Research -- 6.8 Conclusion -- Acknowledgments -- References -- Chapter 7 Blockchain for Mobile Networks -- 7.1 Introduction -- 7.2 Next‐Generation Mobile Networks: Technology Enablers and Challenges -- 7.2.1 Mobile Networks: Technology Enablers.
7.2.1.1 Software‐Defined Networking (SDN) -- 7.2.1.2 Network Function Virtualization (NFV) -- 7.2.1.3 Cloud Computing (CC) -- 7.2.1.4 Multi‐access Edge Computing (MEC) -- 7.2.1.5 5G‐New Radio (5G‐NR) and Millimeter Wave (mmWave) -- 7.2.2 Mobile Networks: Technology Challenges -- 7.2.2.1 Scalability in Massive Communication Scenarios -- 7.2.2.2 Efficient Resource Sharing -- 7.2.2.3 Network Slicing and Multi‐tenancy -- 7.2.2.4 Security -- 7.3 Blockchain Applicability to Mobile Networks and Services -- 7.3.1 Background and Definitions -- 7.3.2 Blockchain for Radio Access Networks -- 7.3.3 Blockchain for Core, Cloud, and Edge Computing -- 7.3.3.1 Data Provenance -- 7.3.3.2 Encrypted Data Indexing -- 7.3.3.3 Mobile Network Orchestration -- 7.3.3.4 Mobile Task Offloading -- 7.3.3.5 Service Automation -- 7.4 Blockchain for Network Slicing -- 7.4.1 The Network Slice Broker (NSB) -- 7.4.2 NSB Blockchain Architecture (NSBchain) -- 7.4.2.1 Technical Challenges -- 7.4.3 NSBchain Modeling -- 7.4.3.1 System Setup -- 7.4.3.2 Message Exchange -- 7.4.3.3 Billing Management -- 7.4.4 NSBchain Evaluation -- 7.4.4.1 Experimental Setup -- 7.4.4.2 Full‐Scale Evaluation -- 7.4.4.3 Brokering Scenario Evaluation -- 7.5 Concluding Remarks and Future Work -- Acronyms -- References -- Chapter 8 Blockchains for Cybersecurity and AI Systems -- 8.1 Introduction -- 8.2 Securing Blockchains and Traditional IT Architectures -- 8.2.1 On Securing a Blockchain Platform -- 8.3 Public Blockchains Cybersecurity -- 8.3.1 Vulnerabilities Categorization -- 8.3.1.1 Technical Limitations, Legal Liabilities, and Connected 3rd‐Party Applications -- 8.3.1.2 Cybersecurity Issues -- 8.3.1.3 Public Blockchain 1.0: PoW and PoS -- 8.3.1.4 Public Blockchain 1.0: DPoS -- 8.3.1.5 Public Blockchain 2.0: Ethereum Smart Contracts -- 8.3.1.6 Public Blockchain 2.0 - Privacy Issues.
8.4 Private Blockchains Cybersecurity.
Record Nr. UNINA-9911018826003321
Al-Dulaimi Anwer  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma
Cooperative control of distributed multi-agent systems [[electronic resource] /] / edited by Jeff S. Shamma
Pubbl/distr/stampa Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007
Descrizione fisica 1 online resource (453 p.)
Disciplina 003.5
003/.5
Altri autori (Persone) ShammaJeff S
Soggetto topico Distributed artificial intelligence
Control theory
Cooperation - Mathematics
Distributed databases
Electronic data processing - Distributed processing
Soggetto genere / forma Electronic books.
ISBN 1-281-31911-2
9786611319113
0-470-72420-X
0-470-72419-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cooperative Control of Distributed Multi-Agent Systems; Contents; List of Contributors; Preface; Part I Introduction; 1 Dimensions of cooperative control; 1.1 Why cooperative control?; 1.1.1 Motivation; 1.1.2 Illustrative example: command and control of networked vehicles; 1.2 Dimensions of cooperative control; 1.2.1 Distributed control and computation; 1.2.2 Adversarial interactions; 1.2.3 Uncertain evolution; 1.2.4 Complexity management; 1.3 Future directions; Acknowledgements; References; Part II Distributed Control and Computation
2 Design of behavior of swarms: From flocking to data fusion using microfilter networks2.1 Introduction; 2.2 Consensus problems; 2.3 Flocking behavior for distributed coverage; 2.3.1 Collective potential of flocks; 2.3.2 Distributed flocking algorithms; 2.3.3 Stability analysis for flocking motion; 2.3.4 Simulations of flocking; 2.4 Microfilter networks for cooperative data fusion; Acknowledgements; References; 3 Connectivity and convergence of formations; 3.1 Introduction; 3.2 Problem formulation; 3.3 Algebraic graph theory
3.4 Stability of vehicle formations in the case of time-invariant communication3.4.1 Formation hierarchy; 3.5 Stability of vehicle formations in the case of time-variant communication; 3.6 Stabilizing feedback for the time-variant communication case; 3.7 Graph connectivity and stability of vehicle formations; 3.8 Conclusion; Acknowledgements; References; 4 Distributed receding horizon control: stability via move suppression; 4.1 Introduction; 4.2 System description and objective; 4.3 Distributed receding horizon control; 4.4 Feasibility and stability analysis; 4.5 Conclusion; Acknowledgement
References5 Distributed predictive control: synthesis, stability and feasibility; 5.1 Introduction; 5.2 Problem formulation; 5.3 Distributed MPC scheme; 5.4 DMPC stability analysis; 5.4.1 Individual value functions as Lyapunov functions; 5.4.2 Generalization to arbitrary number of nodes and graph; 5.4.3 Exchange of information; 5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems; 5.5 Distributed design for identical unconstrained LTI subsystems; 5.5.1 LQR properties for dynamically decoupled systems; 5.5.2 Distributed LQR design; 5.6 Ensuring feasibility
5.6.1 Robust constraint fulfillment5.6.2 Review of methodologies; 5.7 Conclusion; References; 6 Task assignment for mobile agents; 6.1 Introduction; 6.2 Background; 6.2.1 Primal and dual problems; 6.2.2 Auction algorithm; 6.3 Problem statement; 6.3.1 Feasible and optimal vehicle trajectories; 6.3.2 Benefit functions; 6.4 Assignment algorithm and results; 6.4.1 Assumptions; 6.4.2 Motion control for a distributed auction; 6.4.3 Assignment algorithm termination; 6.4.4 Optimality bounds; 6.4.5 Early task completion; 6.5 Simulations; 6.5.1 Effects of delays; 6.5.2 Effects of bidding increment
6.5.3 Early task completions
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Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley & Sons, c2007
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