Muutke küpsiste eelistusi

E-raamat: Building Blocks of IoT Analytics: Internet-of-Things Analytics

Edited by
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 93,60 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Building Blocks of IoT Analytics: Internet-of-Things AnalyticsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized.

The Building Blocks of IoT Analytics is devoted to the presentation of the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of Big Data analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and Big Data technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications.

Technical topics discussed in the book include:
* Cloud Computing and Big Data for IoT analytics
* Searching the Internet of Things
* Development Tools for IoT Analytics Applications
* IoT Analytics-as-a-Service
* Semantic Modelling and Reasoning for IoT Analytics
* IoT analytics for Smart Buildings
* IoT analytics for Smart Cities
* Operationalization of IoT analytics
* Ethical aspects of IoT analytics

This book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programs. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI).
Preface xiii
List of Contributors xix
List of Figures xxi
List of Tables xxv
List of Abbreviations xxvii
Part I: IoT Analytics Enablers
1 Introducing IoT Analytics
3(8)
John Soldatos
1.1 Introduction
3(1)
1.2 IoT Data and BigData
3(2)
1.3 Challenges of IoT Analytics Applications
5(2)
1.4 IoT Analytics Lifecycle and Techniques
7(3)
1.5 Conclusions
10(1)
References
10(1)
2 IoT, Cloud and BigData Integration for IoT Analytics
11(28)
Abdur Rahim Biswas
Corentin Dupont
Congduc Pham
2.1 Introduction
11(1)
2.2 Cloud-based IoT Platform
12(3)
2.2.1 IaaS, PaaS and SaaS Paradigms
12(1)
2.2.2 Requirements of IoT BigData Analytics Platform
13(2)
2.2.3 Functional Architecture
15(1)
2.3 Data Analytics for the IoT
15(5)
2.3.1 Characteristics of IoT Generated Data
15(2)
2.3.2 Data Analytic Techniques and Technologies
17(3)
2.4 Data Collection Using Low-power, Long-range Radios
20(3)
2.4.1 Architecture and Deployment
20(1)
2.4.2 Low-cost LoRa Implementation
21(2)
2.5 WAZIUP Software Platform
23(4)
2.5.1 Main Challenges
23(1)
2.5.2 PaaS for IoT
24(1)
2.5.3 Architecture
25(1)
2.5.4 Deployment
26(1)
2.6 iKaaS Software Platform
27(9)
2.6.1 Service Orchestration and Resources Provisioning
30(1)
2.6.2 Advanced Data Processing and Analytics
30(1)
2.6.3 Service Composition and Decomposition
31(2)
2.6.4 Migration and Portability in Multi-cloud Environment
33(2)
2.6.5 Cost Function of Service Migration
35(1)
2.6.6 Dynamic Selection of Devices in Multi-cloud Environment
35(1)
Acknowledgement
36(1)
References
37(2)
3 Searching the Internet of Things
39(42)
Richard McCreadie
Dyaa Albakour
Jarana Manotumruksa
Craig Macdonald
Iadh Ounis
3.1 Introduction
39(1)
3.2 A Search Architecture for Social and Physical Sensors
40(8)
3.2.1 Search engine for Multimedia Environment generated contenT (SMART)
41(5)
3.2.2 Challenges in Building an IoT Search Engine
46(2)
3.3 Local Event Retrieval
48(6)
3.3.1 Social Sensors for Local Event Retrieval
48(1)
3.3.2 Problem Formulation
49(2)
3.3.3 A Framework for Event Retrieval
51(2)
3.3.4 Summary
53(1)
3.4 Using Sensor Metadata Streams to Identify Topics of Local Events in the City
54(9)
3.4.1 Definition of Event Topic Identification Problem
55(1)
3.4.2 Sensor Data Collection
56(1)
3.4.3 Event Pooling and Annotation
57(2)
3.4.4 Learning Event Topics
59(2)
3.4.5 Experiments
61(2)
3.4.6 Summary
63(1)
3.5 Venue Recommendation
63(10)
3.5.1 Modelling User Preferences
65(2)
3.5.2 Venue-dependent Evidence
67(3)
3.5.3 Context-Aware Venue Recommendations
70(2)
3.5.4 Summary
72(1)
3.6 Conclusions
73(1)
Acknowledgements
74(1)
References
74(7)
4 Development Tools for IoT Analytics Applications
81(18)
John Soldatos
Katerina Roukounaki
4.1 Introduction
81(1)
4.2 Related Work
82(2)
4.3 The VITAL Architecture for IoT Analytics Applications
84(3)
4.4 VITAL Development Environment
87(4)
4.4.1 Overview
87(1)
4.4.2 VITAL Nodes
87(1)
4.4.2.1 PPI nodes
88(1)
4.4.2.2 System nodes
88(1)
4.4.2.3 Services nodes
89(1)
4.4.2.4 Sensors nodes
89(1)
4.4.2.5 Observations nodes
89(1)
4.4.2.6 DMS nodes
89(1)
4.4.2.7 Query systems
89(1)
4.4.2.8 Query services
89(1)
4.4.2.9 Query sensors
89(1)
4.4.2.10 Query observations
90(1)
4.4.2.11 Discovery nodes
90(1)
4.4.2.12 Discover systems nodes
90(1)
4.4.2.13 Discover services nodes
90(1)
4.4.2.14 Discover sensors nodes
90(1)
4.4.2.15 Filtering nodes
90(1)
4.4.2.16 Threshold nodes
90(1)
4.4.2.17 Resample nodes
91(1)
4.5 Development Examples
91(5)
4.5.1 Example #1: Predict the Footfall!
91(1)
4.5.2 Example #2: Find a Bike!
91(5)
4.6 Conclusions
96(1)
Acknowledgements
96(1)
References
96(3)
5 An Open Source Framework for IoT Analytics as a Service
99(40)
John Soldatos
Nikos Kefalakis
Martin Serrano
5.1 Introduction
99(2)
5.2 Architecture for IoT Analytics-as-a-Service
101(5)
5.2.1 Properties of Sensing-as-a-Service Infrastructure
101(1)
5.2.2 Service Delivery Architecture
102(3)
5.2.3 Service Delivery Concept
105(1)
5.3 Sensing-as-a-Service Infrastructure Anatomy
106(6)
5.3.1 Lifecycle of a Sensing-as-a-Service Instance
106(2)
5.3.2 Interactions between OpenIoT Modules
108(4)
5.4 Scheduling, Metering and Service Delivery
112(10)
5.4.1 Scheduler
112(6)
5.4.2 Service Delivery & Utility Manager
118(4)
5.5 Sensing-as-a-Service Example
122(12)
5.5.1 Data Capturing and Flow Description
122(1)
5.5.2 Semantic Annotation of Sensor Data
123(1)
5.5.3 Registering Sensors to LSM
124(1)
5.5.4 Pushing Data to LSM
125(1)
5.5.5 Service Definition and Deployment Using OpenIoT Tools
126(5)
5.5.6 Visualizing the Request
131(3)
5.6 From Sensing-as-a-Service to IoT-Analytics-as-a-Service
134(2)
5.7 Conclusions
136(1)
Acknowledgements
137(1)
References
137(2)
6 A Review of Tools for IoT Semantics and Data Streaming Analytics
139(28)
Martin Serrano
Amelie Gyrard
6.1 Introduction
139(2)
6.2 Related Work
141(6)
6.2.1 Linking Data
141(1)
6.2.2 Real-time & Linked Stream Processing
142(1)
6.2.3 Logic
142(1)
6.2.4 Machine Learning
143(2)
6.2.5 Semantic-based Distributed Reasoning
145(1)
6.2.6 Cross-Domain Recommender Systems
146(1)
6.2.7 Limitations of Existing Work
146(1)
6.3 Semantic Analytics
147(5)
6.3.1 Architecture towards the Linked Open Reasoning
148(1)
6.3.2 The Workflow to Process IoT Data
149(3)
6.3.3 Sensor-based Linked Open Rules (S-LOR)
152(1)
6.4 Tools & Platforms
152(4)
6.4.1 Semantic Modelling and Validation Tools
152(2)
6.4.2 Data Reasoning
154(2)
6.5 A Practical Use Case
156(1)
6.6 Conclusions
157(1)
Acknowledgement
157(1)
References
158(9)
Part II: IoT Analytics Applications and Case Studies
7 Data Analytics in Smart Buildings
167(40)
M. Victoria Moreno
Fernando Terroso-Saenz
Aurora Gonzalez-Vidal
Antonio F. Skarmeta
7.1 Introduction
167(2)
7.2 Addressing Energy Efficiency in Smart Buildings
169(5)
7.3 Related Work
174(5)
7.4 A Proposal of General Architecture for Management Systems of Smart Buildings
179(2)
7.4.1 Data Collection Layer
179(1)
7.4.2 Data Processing Layer
180(1)
7.4.3 Services Layer
181(1)
7.5 IoT-based Information Management System for Energy Efficiency in Smart Buildings
181(11)
7.5.1 Indoor Localization Problem
185(5)
7.5.2 Building Energy Consumption Prediction
190(1)
7.5.3 Optimization Problem
191(1)
7.5.4 User Involvement in the System Operation
191(1)
7.6 Evaluation and Results
192(7)
7.6.1 Scenario of Experimentation
192(2)
7.6.2 Evaluation and Indoor Localization Mechanism
194(1)
7.6.3 Evaluation. Energy Consumption Prediction and Optimization
195(2)
7.6.4 Evaluation. User Involvement
197(2)
7.7 Conclusions and Future Work
199(1)
Acknowledgments
200(1)
References
200(7)
8 Internet-of-Things Analytics for Smart Cities
207(24)
Martin Bauer
Bin Cheng
Flavio Cirillo
Salvatore Longo
Fang-Jing Wu
8.1 Introduction
207(1)
8.2 Cloud-based IoT Analytics
208(2)
8.2.1 State of the Art
209(1)
8.3 Cloud-based City Platform
210(5)
8.3.1 Use Case of Cloud-based Data Analytics
213(2)
8.4 New Challenges towards Edge-based Solutions
215(2)
8.5 Edge-based IoT Analytics
217(6)
8.5.1 State of the Art
217(1)
8.5.2 Edge-based City Platform
218(3)
8.5.3 Workflow
221(1)
8.5.4 Task and Topology
221(1)
8.5.5 IoT-friendly Interfaces
222(1)
8.6 Use Case of Edge-based Data Analytics
223(3)
8.6.1 Overview of Crowd Mobility Analytics
223(2)
8.6.2 Processing Tasks and Topology of Crowd Mobility Analytics
225(1)
8.7 Conclusion and Future Work
226(1)
References
227(4)
9 IoT Analytics: From Data Collection to Deployment and Operationalization
231(8)
John Soldatos
Ioannis T. Christou
9.1 Operationalizing Data Analytics Using the VITAL Platform
231(2)
9.1.1 IoT Data Analysis
232(1)
9.1.2 IoT Data Deployment and Reuse
232(1)
9.2 Knowledge Extraction and IoT Analytics Operationalization
233(1)
9.3 A Practical Example based on Footfall Data
234(3)
Acknowledgement
237(1)
References
237(2)
10 Ethical IoT: A Sustainable Way Forward
239(10)
Maarten Botterman
10.1 Introduction
239(1)
10.2 From IoT to a Data Driven Economy and Society
240(5)
10.3 Way Forward with IoT
245(1)
10.4 Conclusions
246(1)
References
247(2)
Epilogue 249(2)
Index 251(2)
About the Editor 253(2)
About the Authors 255
John Soldatos
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97887935190462e.html