Kooperativer Bibliotheksverbund

UID:

Format: XXIV, 129 p. 63 illus., 52 illus. in color. , online resource.

Edition: 1st ed. 2022.

ISBN: 9783030933876

Series Statement: Lecture Notes on Data Engineering and Communications Technologies, 108

Content: This book reflects the recent developments while providing a comprehensive introduction to the Internet of things (IoT) and cloud technologies in transforming aging. IoT has its origins in device connectivity, whereas the cloud grew out of computer science. They can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. This book is aimed at advanced undergraduates or first-year research students, as well as researchers and practitioners, and assumes no previous knowledge of IoT and cloud concepts. Basics of computer applications and concepts are required. Some familiarity with gerontechnology would be helpful, though not essential, as this book includes a self-contained introduction to how technology is transforming elderly care and eHealth management. This book aims to give references that offer more detail than is possible here and hopefully provide an entry point into a series of technologies that can improve the quality of life for the elderly. The book includes several case studies explaining how each piece of technology works and its benefits to the elderly. This book is also considered as a simple guide to the technologies for the elderly to use in the community.

Note: Introduction -- The Vision of the Healthcare Industry for Supporting the Aging Population -- Building Long-Term Care Services Around the World -- Iot and Cloud Computing for Development of Systems for Elderly and E-Health -- New Generation of Healthcare Services Based on Internet of Medical Things, Edge and Cloud Computing Infrastructures -- Artificial Intelligence and Data Mining Techniques for the Wellbeing of Elderly -- Domesticating Homecare Services -- Case Study in Fall Prevention in Indoor Environments -- Case Study in Elderly Consultancy Services -- Case Study in Remote Diagnosis.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030933869

Additional Edition: Printed edition: ISBN 9783030933883

Language: English

DOI: 10.1007/978-3-030-93387-6

URL: https://doi.org/10.1007/978-3-030-93387-6

URL: Volltext  (URL des Erstveröffentlichers)

URL: lizenzpflichtig

UID:

Format: 1 Online-Ressource (lxvii, 1172 Seiten) , Illustrationen, Diagramme

ISBN: 9783030150358

Series Statement: Advances in intelligent systems and computing 927

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-15034-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-15036-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-3-030-15035-8

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Takizawa, Makoto 1950-

UID:

Format: XV, 308 p. 153 illus., 139 illus. in color. , online resource.

Edition: 1st ed. 2021.

ISBN: 9783030711726

Series Statement: Lecture Notes on Data Engineering and Communications Technologies, 67

Content: This book brings to readers thirteen chapters with contributions to the benefits of using IoT and Cloud Computing to agro-ecosystems from a multi-disciplinary perspective. IoT and Cloud systems have prompted the development of a Cloud digital ecosystem referred to as Cloud-to-thing continuum computing. The key success of IoT computing and the Cloud digital ecosystem is that IoT can be integrated seamlessly with the physical environment and therefore has the potential to leverage innovative services in agro-ecosystems. Areas such as ecological monitoring, agriculture, and biodiversity constitute a large area of potential application of IoT and Cloud technologies. In contrast to traditional agriculture systems that have employed aggressive policies to increase productivity, new agro-ecosystems aim to increase productivity but also achieve efficiency and competitiveness in modern sustainable agriculture and contribute, more broadly, to the green economy and sustainable food-chain industry. Fundamental research as well as concrete applications from various real-life scenarios, such as smart farming, precision agriculture, green agriculture, sustainable livestock and sow farming, climate threat, and societal and environmental impacts, is presented. Research issues and challenges are also discussed towards envisioning efficient and scalable solutions to agro-ecosystems based on IoT and Cloud technologies. Our fundamental belief is that we can collectively trigger a new revolution that will transition agriculture into an equable system that not only feeds the world, but also contributes to mitigating the climate change and biodiversity crises that our historical actions have triggered. .

Note: IoT-based Computational Modeling for Next Generation Agro-ecosystems: Research Issues, Emerging Trends and Challenges -- An IoT-Based Time Constrained Spectrum Trading in Wireless Communication for Tertiary Market -- 5G NB-IoT Enabled Smart Green Agriculture 4.0: A Survey -- Drones for Intelligent Agricultural Management -- Multi-Modal Sensor Nodes in Experimental Scalable Agricultural IoT Application Scenarios -- Design Architecture of Intelligent Agri-Infrastructure Incorporating IoT and Cloud: Link Budget and Socio-Economic Impact -- Remote Sensing and Soil Quality -- Enabling IoT Wireless Technologies in Sustainable Livestock Farming toward Agriculture 4.0.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030711719

Additional Edition: Printed edition: ISBN 9783030711733

Language: English

DOI: 10.1007/978-3-030-71172-6

URL: https://doi.org/10.1007/978-3-030-71172-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XVII, 248 p. 109 illus., 97 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9783031293016

Series Statement: Engineering Cyber-Physical Systems and Critical Infrastructures, 5

Content: This book presents advances on the state of the art in smart cities systems and applications based on the proof of concept and prototyping for smart cities in an interdisciplinary context of engineering and information sciences. Smart cities have emerged as highly complex technological endeavors that combine knowledge and technology from many disciplines ranging from information sciences to engineering. Due to their complex nature, the modeling, development, and prototyping of applications in smart cities present a myriad of challenges, including technical, economic, and social ones, across application subdomains such as smart transportation, social welfare, tourism, and smart industry. It becomes difficult or sometimes impossible to provide a solution for such potential research issues and challenges from a traditional disciplinary-approach only; to tackle such research issues and to make the paradigm of smart cities a reality, interdisciplinary approaches are deemed necessary. Readers, developers, practitioners, and policy-makers in the field find in the book insights, experiences, findings, and perspectives on smart cities applications with an emphasis on real-life prototyping, beyond the confines of laboratory experiments.

Note: Critical Infrastructures Resilience in the Context of a Physical Protection System -- Smart City Security based on Meta-Security Framework for Digital Twins -- On-Premise Artificial Intelligence as a Service for Small and Medium Size Setups -- Incentives in Surplus Food Distribution for Smart Cities and Beyond: An Activity Aware Solution -- Evaluation of Smart Charging Integrated with Smart Energy Management and Advance Booking in an eMobility Urban Living Lab -- Vehicle Allocation Algorithm Improving User Satisfaction in Ride -- A City Airspace Testbed for Drone Networks in Future Smart Cities -- A Feasibility Study of Tethered Autonomous Moving Cells for Smart City -- Relationship among Different Types of Input and Model Accuracies in LSTM Driver Models. Watch-from-inside: 3D sensing system to monitor the outside from inside -- Federated Learning with Client Selection in Resource-uncertain Wireless Networks: Simulation and Proof of Concept Experiments.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031293009

Additional Edition: Printed edition: ISBN 9783031293023

Additional Edition: Printed edition: ISBN 9783031293030

Language: English

DOI: 10.1007/978-3-031-29301-6

URL: https://doi.org/10.1007/978-3-031-29301-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xix, 845 Seiten) , Illustrationen, Diagramme

ISBN: 9783030343873

Series Statement: Advances in intelligent systems and computing 1084

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-34386-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-34388-0

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-3-030-34387-3

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Tavana, Madjid 1957-

UID:

Format: 1 Online-Ressource (xxi, 1167 Seiten) , Illustrationen, Diagramme

ISBN: 9783030028046

Series Statement: Advances in intelligent systems and computing 885

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-02803-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-02805-3

Language: English

DOI: 10.1007/978-3-030-02804-6

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Tavana, Madjid 1957-

UID:

Format: 1 Online-Ressource (xxv, 579 Seiten) , Illustrationen, Diagramme

ISBN: 9783030128395

Series Statement: Lecture notes on data engineering and communications technologies 29

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-12838-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-12840-1

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-3-030-12839-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xli, 1357 Seiten) , Illustrationen, Diagramme, Karten

ISBN: 9783030150327

Series Statement: Advances in intelligent systems and computing 926

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-15031-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-15033-4

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-3-030-15032-7

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Takizawa, Makoto 1950-

UID:

Format: 1 Online-Ressource

ISBN: 9781441916365

Series Statement: Springer optimization and its applications 41

Additional Edition: Erscheint auch als Druckausgabe ISBN 978-1-4419-1635-8

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Aufsatzsammlung

DOI: 10.1007/978-1-4419-1636-5

URL: Volltext

URL: Volltext  (lizenzpflichtig)

URL: Inhaltstext

UID:

Format: 1 online resource (328 pages) : , illustrations, tables.

ISBN: 0-12-809625-X

Series Statement: Intelligent Data Centric Systems

Note: Front Cover -- Big Data Analytics for Sensor-Network Collected Intelligence -- Copyright -- Contents -- List of Contributors -- Preface -- Acknowledgments -- Part I: Big Data Architecture and Platforms -- Chapter 1: Big Data: A Classification of Acquisition and Generation Methods -- 1. Big Data: A Classification -- 1.1. Characteristics of Big Data -- 2. Big Data Generation Methods -- 2.1. Data Sources -- 2.1.1. Born-digital data -- 2.1.2. Sensor data -- 2.2. Data Types -- 2.2.1. Structured data -- 2.2.2. Unstructured data -- 3. Big Data: Data Acquisition Methods -- 3.1. Interface Methods -- 3.1.1. Command-line interfaces -- 3.1.2. Graphical user interfaces -- 3.1.3. Context-sensitive user interfaces -- 3.1.4. Web-based user interfaces -- 3.1.5. Adaptive user interfaces or intelligent user interfaces -- 3.1.6. Natural user interfaces -- 3.1.7. Voice interfaces -- 3.1.8. Gesture-based interfaces -- 3.1.9. Multitouch gesture interface -- 3.1.10. Touchless gesture interfaces -- 3.2. Interface Devices -- 3.2.1. Keyboard -- 3.2.2. Mice -- 3.2.3. Joystick -- 3.2.4. Stylus -- 3.2.5. Touchpad -- 3.2.6. Touchscreens -- 3.2.7. Kinect -- 3.2.8. Leap motion -- 3.2.9. Myo -- 3.2.10. Wearable devices -- 4. Big Data: Data Management -- 4.1. Data Representation and Organization -- 4.1.1. File formats -- Javascript object notation records (JSON) -- Binary Javascript object notation records (BSON) -- Comma-separated values (CSV) -- Sequence file -- Record columnar files -- Optimized row columnar files (ORC files) -- Parquet files -- Avro files -- 4.1.2. Data compression -- 4.1.3. Hadoop codecs -- 4.2. Databases -- 4.2.1. Dynamic schema -- 4.2.2. Sharding, replication and auto-caching -- 4.2.3. NoSQL types -- Key-value stores -- Document stores -- Column-oriented stores -- Graph stores -- 4.3. Data Fusion and Data Integration -- 5. Summary -- References -- Glossary. , Chapter 2: Cloud Computing Infrastructure for Data Intensive Applications -- 1. Introduction -- 2. Big Data Nature and Definition -- 2.1. Big Data in Science and Industry -- 2.2. Big Data and Social Network/Data -- 2.3. Big Data Technology Definition: From 6V to 5 Parts -- 3. Big Data and Paradigm Change -- 3.1. Big Data Ecosystem -- 3.2. New Features of the BDI -- 3.3. Moving to Data-Centric Models and Technologies -- 4. Big Data Architecture Framework and Components -- 4.1. Defining the Big Data Architecture Framework -- 4.2. Data Management and Big Data Lifecycle -- 4.3. Data Structures and Data Models for Big Data -- 4.4. NIST Big Data Reference Architecture -- 4.5. General Big Data System Requirements -- 4.5.1. Data sources requirements (DSR) -- 4.5.2. Transformation (applications) provider requirements (TPR) -- 4.5.3. Capability (framework) provider requirements (CPR) -- 4.5.4. Data consumer requirements (DCR) -- 4.5.5. Security and privacy requirements (SPR) -- 4.5.6. Lifecycle management requirements (LMR) -- 4.5.7. Other requirements (OR) -- 5. Big Data Infrastructure -- 5.1. BDI Components -- 5.2. Big Data Stack Components and Technologies -- 5.3. Example of Cloud-Based Infrastructure for Distributed Data Processing -- 5.4. Benefits of Cloud Platforms for Big Data Applications -- 6. Case Study: Bioinformatics Applications Deployment on Cloud -- 6.1. Overall Description -- 6.2. UC1-Securing Human Biomedical Data -- 6.2.1. Description -- 6.2.2. Workflow -- 6.3. UC2-Cloud Virtual Pipeline for Microbial Genomes Analysis -- 6.3.1. Description -- 6.3.2. Workflow -- 6.4. Implementation of Use Cases and CYCLONE Infrastructure Components -- 6.4.1. Deployment UC1 Securing human biomedical data -- 6.4.2. Deployment UC2: Cloud virtual pipeline for microbial genomes analysis -- 7. CYCLONE Platform for Cloud Applications Deployment and Management. , 7.1. General Architecture for Intercloud and Multicloud Applications Deployment -- 7.2. Ensuring Consistent Security Services in Cloud-Based Applications -- 7.3. Dynamic Access Control Infrastructure -- 7.3.1. Dynamic trust bootstrapping -- 8. Cloud Powered Big Data Applications Development and Deployment Automation -- 8.1. Demand for Automated Big Data Applications Provisioning -- 8.2. Cloud Automation Tools for Intercloud Application and Network Infrastructure Provisioning -- 8.3. SlipStream: Cloud Application Management Platform -- 8.3.1. Functionality used for applications deployment -- 8.3.2. Example recipes -- 9. Big Data Service and Platform Providers -- 9.1. Amazon Web Services and Elastic MapReduce -- 9.2. Microsoft Azure Analytics Platform System and HDInsight -- 9.3. IBM Big Data Analytics and Information Management -- 9.4. Cloudera -- 9.5. Pentaho -- 9.6. LexisNexis HPCC Systems as an Integrated Open Source Platform for Big Data Analytics -- 10. Conclusion -- References -- Glossary -- Chapter 3: Open Source Private Cloud Platforms for Big Data -- 1. Cloud Computing and Big Data as a Service -- 1.1. Public Cloud Infrastructure -- 1.2. Advantages of the Cloud for Big Data -- 2. On-Premise Private Clouds for Big Data -- 2.1. Security of Cloud Computing Systems -- 2.2. Advantages of On-Premise Private Clouds -- 3. Introduction to Selected Open Source Cloud Environments -- 3.1. OpenNebula -- 3.2. Eucalyptus -- 3.3. Apache CloudStack -- 3.4. OpenStack -- 3.4.1. Using Docker with OpenStack -- 3.4.2. Sahara -- 3.4.3. Ironic -- 4. Heterogeneous Computing in the Cloud -- 4.1. Exclusive Mode -- 4.2. Sharing Mode -- 5. Case Study: The EMS, an On-Premise Private Cloud -- 6. Conclusion -- Disclaimer -- References -- Part II: Big Data Processing and Management -- Chapter 4: Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud. , 1. Introduction -- 1.1. Motivation -- 1.2. Organization of the Chapter -- 2. Related Work and Problem Analysis -- 2.1. Related Work -- 2.2. Problem Analysis: Real-World Requirements for Nonlinear Regression -- 3. Temporal Compression Model Based on Nonlinear Regression -- 3.1. Nonlinear Regression Prediction Model -- 4. Algorithms -- 4.1. Algorithm for Nonlinear Regression -- 4.2. Nonlinear Regression Compression Algorithm Based on MapReduce -- 5. Experiments -- 5.1. Experiment Environment and Process -- 5.2. Experiment for the Compression With Nonlinear Regression -- 5.3. Experiment for Data Loss and Accuracy -- 6. Conclusions and Future Work -- References -- Chapter 5: Big Data Management on Wireless Sensor Networks -- 1. Introduction -- 2. Data Management on WSNs -- 2.1. Storage -- 2.2. Query Processing -- 2.3. Data Collection -- 3. Big Data Tools -- 3.1. File System -- 3.2. Batch Processing -- 3.2.1. MapReduce in Hadoop -- 3.2.2. RDD in Spark -- 3.3. Streaming Data Processing -- 3.3.1. Continuous operator model -- 3.3.2. Discretized stream model -- 4. Put It Together: Big Data Management Architecture -- 4.1. Batch Layer -- 4.2. Serving Layer -- 4.3. Speed Layer -- 5. Big Data Management on WSNs -- 5.1. In-Network Aggregation Techniques and Data Integration Components -- 5.2. Exploiting Big Data Systems as Data Centers -- 5.2.1. Case 1: Fire security system -- 5.2.2. Case 2: Environment monitoring system -- 6. Conclusion -- References -- Glossary -- Chapter 6: Extreme Learning Machine and Its Applications in Big Data Processing -- 1. Introduction -- 1.1. Background -- 1.2. Artificial Neural Networks -- 1.2.1. What are artificial neural networks? -- 1.2.2. Architecture of a neuron -- 1.2.3. Architectures of ANNs -- 1.3. Era of Big Data -- 1.4. Organization -- 2. Extreme Learning Machine -- 2.1. Traditional Approaches to Train ANNs. , 2.2. Theories of the Extreme Learning Machine -- 2.2.1. Interpolation theory -- 2.2.2. Universal approximation capability -- 2.3. Classical ELM -- 2.3.1. Basic ELM -- 2.3.2. Essences of the ELM -- 2.4. ELM for Classification and Regression -- 2.4.1. Improving stability and compactness of the ELM -- 2.4.2. ELM for imbalanced data -- 2.4.3. ELM for semisupervised learning -- 2.4.4. Other variants of the ELM -- 2.5. ELM for Unsupervised Learning -- 2.5.1. ELM for embedding and clustering -- 2.5.2. ELM for representational learning -- 3. Improved Extreme Learning Machine With Big Data -- 3.1. Shortcomings of the Extreme Learning Machine for Processing Big Data -- 3.2. Optimization Strategies for the Traditional Extreme Learning Machine -- 3.3. Efficiency Improvement for Big Data -- 3.3.1. Orthogonal projection for ELM -- 3.3.2. ELM for online sequential data -- 3.3.3. Sparse ELM for classification -- 3.4. Parallel Extreme Learning Machine Based on MapReduce -- 3.4.1. MapReduce and Hadoop -- 3.4.2. PELM -- 3.4.3. ELM* -- 3.4.4. PASS-ELM -- 3.5. Parallel Extreme Learning Machine Based on Apache Spark -- 3.5.1. Advantage of Apache Spark -- 3.5.2. Parallel ELM on Spark -- 3.5.3. Performance of the SELM under different dimensionality -- 4. Applications -- 4.1. ELM in Predicting Protein Structure -- 4.2. ELM in Image Processing -- 4.3. ELM in Cancer Diagnosis -- 4.4. ELM in Big Data Security and Privacy -- 5. Conclusion -- References -- Glossary -- Part III: Big Data Analytics and Services -- Chapter 7: Spatial Big Data Analytics for Cellular Communication Systems -- 1. Introduction -- 2. Cellular Communications and Generated Data -- 3. Spatial Big Data Analytics -- 3.1. Statistical Foundation for Spatial Big Data Analytics -- 3.2. Spatial Pattern Mining From Spatial Big Data Analytics -- 3.2.1. Spatial prediction models -- 3.2.2. Spatial outlier detection. , 3.2.3. Spatial co-location discovery.

Additional Edition: ISBN 0-12-809393-5

Language: English