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Components and Services for IoT Platforms : Paving the Way for IoT Standards.

By: Contributor(s): Material type: TextPublisher: Cham : Springer International Publishing AG, 2016Copyright date: ©2017Edition: 1st edDescription: 1 online resource (382 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319423043
Subject(s): Genre/Form: DDC classification:
  • 004.678
Online resources:
Contents:
Intro -- Preface -- Contents -- Part I Platforms and Design Methodologies for IoT Hardware -- 1 Power-Shaping Configurable Microprocessors for IoT Devices -- 1.1 Introduction -- 1.2 Microprocessor Design Constraints for the Internet of Things -- 1.2.1 Microprocessors for the Internet of Things -- 1.3 The Role of Power Consumption in IoT Devices -- 1.3.1 Localized Effects of Power Consumption -- 1.3.2 Globalized Effects of Power Consumption -- 1.3.3 Reliability of Integrated Circuits -- 1.4 Low Power Design Opportunities for IoT Processors -- 1.4.1 Dynamic Voltage/Frequency Scaling -- 1.4.2 GALS Design Style and Its Advantages for IoT Processors -- 1.4.3 Body (Substrate) Biasing -- 1.4.3.1 Impact of Substrate Biasing on Latch-Up -- 1.4.3.2 Case Study 1: DVFS and Body Biasing on Standard "Bulk" Processes -- 1.5 CMOS FD-SOI Technology and Related Opportunities for IoT Processor Design -- 1.5.1 Application of Forward Body Biasing in FD-SOI -- 1.5.2 Case Study 2: FBB on FD-SOI Technology -- 1.6 Conclusions -- References -- 2 Formal Design Flows for Embedded IoT Hardware -- 2.1 Introduction -- 2.2 Background and Existing Work -- 2.2.1 High-Level and Logic Synthesis -- 2.2.2 HLS Scheduling -- 2.2.3 Allocation and Binding Tasks -- 2.2.4 History of High-Level Synthesis Tools -- 2.2.5 Next Generation High-Level Synthesis Tools -- 2.3 Synthesis for Low Power -- 2.4 The C-Cubed Hardware Synthesis Flow -- 2.5 Back-End Compiler Inference Logic Rules -- 2.6 Inference Logic and Back-End Transformations -- 2.7 The PARCS Optimizer -- 2.8 Generated Hardware Architectures -- 2.9 Generated Hardware Execution Platform -- 2.10 Experimental Results and Conclusions -- 2.11 Conclusions and Future Work -- References -- 3 AXIOM: A Flexible Platform for the Smart Home -- 3.1 Introduction -- 3.2 Smart Home Scenarios -- 3.3 The AXIOM Platform -- 3.4 Thread Management.
3.5 The OmpSs Programming Model -- 3.5.1 OmpSs@FPGA -- 3.5.2 OmpSs@cluster -- 3.6 Some Initial Experiments -- 3.6.1 Methodology -- 3.6.2 Matrix Multiplication Benchmark -- 3.6.3 Experiments -- 3.7 Conclusions -- References -- Part II Simulation, Modeling and Programming Frameworks for IoT -- 4 Internet of Things Simulation Using OMNeT++ and Hardware in the Loop -- 4.1 Introduction -- 4.2 OMNeT++ -- 4.3 Related Work -- 4.4 Robots in Assisted Living Environments -- 4.5 Concept -- 4.5.1 Modified Scheduler -- 4.5.2 HiL Interfaces -- 4.5.3 Messages -- 4.6 Case Study -- 4.7 Conclusion -- References -- 5 Towards Self-Adaptive IoT Applications: Requirements and Adaptivity Patterns for a Fall-Detection Ambient Assisting Living Application -- 5.1 Introduction -- 5.2 Requirements to System Design: The Fall-Detection Application -- 5.2.1 Case Study Overview -- 5.2.2 Requirements Gathering and Analysis -- 5.2.2.1 Overview of Modelling Languages -- 5.2.2.2 Requirements Engineering Methods -- 5.2.3 System Design -- 5.2.4 Adaptivity Requirements -- 5.3 Proposed Pattern-Based Approach: Current IoT -- 5.3.1 Patterns Applied -- 5.3.1.1 Adaptation Detection Pattern -- 5.3.1.2 Case-Based Reasoning Pattern -- 5.3.1.3 Centralised Architecture Pattern -- 5.3.2 System Design/Implementation Using the Proposed Pattern-Based Approach -- 5.4 Problems with Current Fall-Detection Systems and Pattern-Based Run-Time Adaptation: The Future IoT -- 5.5 Conclusions and Future Work -- References -- 6 Small Footprint JavaScript Engine -- 6.1 Introduction -- 6.2 Analysis of Small Footprint Engines -- 6.2.1 Runtime Data Structure -- 6.2.2 Garbage Collection -- 6.2.3 ECMA-262 Coverage -- 6.2.4 Size and Performance -- 6.3 Proposed Engine -- 6.3.1 Overall Structure of Duktape -- 6.3.2 Improved String Concatenation and Join -- 6.3.3 Array Putprop Fastpath.
6.3.4 Bitwise Operation/Compare with Native Stack -- 6.3.5 Indirect Threading -- 6.3.6 Lazy Built-in Objects Construction -- 6.4 Experimental Results -- 6.4.1 Environment -- 6.4.2 Performance -- 6.4.3 Footprint -- 6.4.4 Binary Size -- 6.5 Summary and Future Work -- References -- 7 VirISA: Recruiting Virtualization and Reconfigurable Processor ISA for Malicious Code Injection Protection -- 7.1 Introduction -- 7.2 Threat Model -- 7.3 Motivation, Background, and Related Work -- 7.4 Proposed Code Injection Protection Architecture -- 7.4.1 VirISA Main Functionality -- 7.4.2 VirISA Architecture -- 7.4.3 VirISA Permutation Table Rules -- 7.5 Conclusions -- References -- Part III Opportunities, Challenges and Limits in WSN Deployment for IoT -- 8 Deployment Strategies of Wireless Sensor Networks for IoT: Challenges, Trends, and Solutions Based on Novel Tools and HW/SW Platforms -- 8.1 Introduction -- 8.2 Research Trends and State-of-the-Art Approaches -- 8.2.1 A New Perspective for On-Site WSN Deployments -- 8.3 General Overview of the Proposed System -- 8.4 On-Site Deployment and Maintainability Optimization Mechanism -- 8.4.1 Modeling of the Deployment and Maintenance Methodology -- 8.4.2 Deployment Optimization Mechanism -- 8.5 Wireless Mesh Networking Optimization Approach -- 8.6 System Implementation -- 8.7 Experimental Test Cases and Discussion -- 8.8 Conclusions and Future Perspectives -- References -- 9 Wireless Sensor Networks for the Internet of Things: Barriers and Synergies -- 9.1 Introduction -- 9.2 WSN Programming Models and Tools -- 9.2.1 Low-Level Programming -- 9.2.1.1 Operating System-Level Programming -- 9.2.1.2 Virtual Machine or Middleware -- 9.2.2 High-Level Programming -- 9.2.2.1 Model-Based Development -- 9.2.2.2 Group-Level Programming -- 9.2.2.3 Network-Level Programming (Macroprogramming).
9.2.3 Evaluation of Existing WSN Programming Models and Tools -- 9.2.3.1 Low-Level Programming Evaluation -- 9.2.3.2 High-Level Programming Evaluation -- 9.3 WSN Hardware and Server-Side Support -- 9.3.1 WSN Hardware -- 9.3.1.1 Microcontroller -- 9.3.1.2 RF Device -- 9.3.1.3 RF Antenna -- 9.3.1.4 Energy Supply -- 9.3.1.5 Transducers -- 9.3.1.6 Package -- 9.3.1.7 Hardware Nodes -- 9.3.1.8 Server-Side Support -- 9.4 Semi-Automated WSN HW/SW Application Synthesis -- 9.4.1 Semi-Automated Development Flow Overview -- 9.4.2 Automated Hardware-Software Synthesis Tool Overview -- 9.4.3 Automated Synthesis Tool Input Interface -- 9.4.4 Structure of Top-Level and Library Components -- 9.4.5 System Synthesis Process -- 9.4.6 Synthesis Use for Legacy Designs -- 9.5 Synergies for WSN Development Tools and Platforms -- 9.6 Conclusion -- References -- 10 Event Identification in Wireless Sensor Networks -- 10.1 Introduction -- 10.2 Wireless Sensor Network Characteristics -- 10.3 Event Detection Challenges in WSNs -- 10.4 Data Fusion Categorization -- 10.4.1 Data Fusion Algorithmic Approaches -- 10.4.2 Levels of Data Fusion -- 10.5 Classification of Event Detection Approaches -- 10.5.1 Model-Based Approaches -- 10.5.1.1 Arithmetic Model-Based Approaches -- 10.5.1.2 Map-Based Approaches -- 10.5.1.3 Probabilistic/Statistical Model-Based Approaches -- 10.5.2 Pattern Matching-Based Approaches -- 10.5.2.1 Prototype Matching Techniques -- 10.5.2.2 Signature Matching Techniques -- 10.5.3 Artificial Intelligence and Machine Learning-Based Approaches -- 10.5.3.1 Supervised Learning -- 10.5.3.2 Unsupervised Learning -- 10.5.3.3 Fixed Width Clustering -- 10.6 Performance and Behavioral Characteristics of Event Detection Techniques -- 10.6.1 Processing Model of Event Detection -- 10.6.2 Technique's Scalability -- 10.6.3 Sensor Data Types.
10.6.4 Time Constrained Performance Demands -- 10.6.5 Density -- 10.6.6 Evaluation Approach -- 10.7 Conclusions -- References -- Part IV Efficient Data Management and Decision Making for IoT -- 11 Integrating IoT and Fog Computing for Healthcare Service Delivery -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Fog Computing -- 11.3.1 Definition -- 11.3.2 Characteristics -- 11.3.3 Benefits of Integration with the IoT Technology for Healthcare Service Provisioning -- 11.4 Integration of IoT-Fog and Cloud -- 11.4.1 System Model -- 11.4.2 Fog Server Architecture -- 11.5 Use Case Scenarios -- 11.5.1 Daily Monitoring and Healthcare Service Provisioning -- 11.5.2 Extended eCall Service Delivery -- 11.6 Conclusion -- References -- 12 Supporting Decision Making for Large-Scale IoTs: Trading Accuracy with Computational Complexity -- 12.1 Introduction -- 12.2 The Smart Thermostat Usecase -- 12.2.1 System Modeling -- 12.3 Challenges and Motivation -- 12.4 Support Decision Making with the Fuzzy Inference Systems -- 12.4.1 Fuzzy Inference System's Architecture -- 12.5 Communication Links -- 12.6 Evaluation -- 12.7 Conclusions -- References -- 13 Fuzzy Inference Systems Design Approaches for WSNs -- 13.1 Data Classification in WSNs -- 13.1.1 Fuzzy Logic Critical Definitions -- 13.2 Design of a Fuzzy System -- 13.3 Literature Review of Applying Fuzzy Logic in WSN Scenarios -- 13.4 Designing a Health Status FIS for Wireless Sensor Networks Application Scenario -- 13.4.1 Challenges of Applying Data Mining Techniques in WSNs -- 13.4.2 Use Case Scenario -- 13.4.2.1 Healthcare Assessment Fuzzy Inference System -- 13.4.2.2 WSN QoS Assessment Fuzzy Inference System -- 13.4.2.3 Evaluation of Both Systems -- 13.4.3 Centralized Implementation of the HealthCare Assessment FIS in a WSN Platform.
13.4.4 Decentralized Implementation of the HealthCare Assessment FIS in a WSN Platform.
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Intro -- Preface -- Contents -- Part I Platforms and Design Methodologies for IoT Hardware -- 1 Power-Shaping Configurable Microprocessors for IoT Devices -- 1.1 Introduction -- 1.2 Microprocessor Design Constraints for the Internet of Things -- 1.2.1 Microprocessors for the Internet of Things -- 1.3 The Role of Power Consumption in IoT Devices -- 1.3.1 Localized Effects of Power Consumption -- 1.3.2 Globalized Effects of Power Consumption -- 1.3.3 Reliability of Integrated Circuits -- 1.4 Low Power Design Opportunities for IoT Processors -- 1.4.1 Dynamic Voltage/Frequency Scaling -- 1.4.2 GALS Design Style and Its Advantages for IoT Processors -- 1.4.3 Body (Substrate) Biasing -- 1.4.3.1 Impact of Substrate Biasing on Latch-Up -- 1.4.3.2 Case Study 1: DVFS and Body Biasing on Standard "Bulk" Processes -- 1.5 CMOS FD-SOI Technology and Related Opportunities for IoT Processor Design -- 1.5.1 Application of Forward Body Biasing in FD-SOI -- 1.5.2 Case Study 2: FBB on FD-SOI Technology -- 1.6 Conclusions -- References -- 2 Formal Design Flows for Embedded IoT Hardware -- 2.1 Introduction -- 2.2 Background and Existing Work -- 2.2.1 High-Level and Logic Synthesis -- 2.2.2 HLS Scheduling -- 2.2.3 Allocation and Binding Tasks -- 2.2.4 History of High-Level Synthesis Tools -- 2.2.5 Next Generation High-Level Synthesis Tools -- 2.3 Synthesis for Low Power -- 2.4 The C-Cubed Hardware Synthesis Flow -- 2.5 Back-End Compiler Inference Logic Rules -- 2.6 Inference Logic and Back-End Transformations -- 2.7 The PARCS Optimizer -- 2.8 Generated Hardware Architectures -- 2.9 Generated Hardware Execution Platform -- 2.10 Experimental Results and Conclusions -- 2.11 Conclusions and Future Work -- References -- 3 AXIOM: A Flexible Platform for the Smart Home -- 3.1 Introduction -- 3.2 Smart Home Scenarios -- 3.3 The AXIOM Platform -- 3.4 Thread Management.

3.5 The OmpSs Programming Model -- 3.5.1 OmpSs@FPGA -- 3.5.2 OmpSs@cluster -- 3.6 Some Initial Experiments -- 3.6.1 Methodology -- 3.6.2 Matrix Multiplication Benchmark -- 3.6.3 Experiments -- 3.7 Conclusions -- References -- Part II Simulation, Modeling and Programming Frameworks for IoT -- 4 Internet of Things Simulation Using OMNeT++ and Hardware in the Loop -- 4.1 Introduction -- 4.2 OMNeT++ -- 4.3 Related Work -- 4.4 Robots in Assisted Living Environments -- 4.5 Concept -- 4.5.1 Modified Scheduler -- 4.5.2 HiL Interfaces -- 4.5.3 Messages -- 4.6 Case Study -- 4.7 Conclusion -- References -- 5 Towards Self-Adaptive IoT Applications: Requirements and Adaptivity Patterns for a Fall-Detection Ambient Assisting Living Application -- 5.1 Introduction -- 5.2 Requirements to System Design: The Fall-Detection Application -- 5.2.1 Case Study Overview -- 5.2.2 Requirements Gathering and Analysis -- 5.2.2.1 Overview of Modelling Languages -- 5.2.2.2 Requirements Engineering Methods -- 5.2.3 System Design -- 5.2.4 Adaptivity Requirements -- 5.3 Proposed Pattern-Based Approach: Current IoT -- 5.3.1 Patterns Applied -- 5.3.1.1 Adaptation Detection Pattern -- 5.3.1.2 Case-Based Reasoning Pattern -- 5.3.1.3 Centralised Architecture Pattern -- 5.3.2 System Design/Implementation Using the Proposed Pattern-Based Approach -- 5.4 Problems with Current Fall-Detection Systems and Pattern-Based Run-Time Adaptation: The Future IoT -- 5.5 Conclusions and Future Work -- References -- 6 Small Footprint JavaScript Engine -- 6.1 Introduction -- 6.2 Analysis of Small Footprint Engines -- 6.2.1 Runtime Data Structure -- 6.2.2 Garbage Collection -- 6.2.3 ECMA-262 Coverage -- 6.2.4 Size and Performance -- 6.3 Proposed Engine -- 6.3.1 Overall Structure of Duktape -- 6.3.2 Improved String Concatenation and Join -- 6.3.3 Array Putprop Fastpath.

6.3.4 Bitwise Operation/Compare with Native Stack -- 6.3.5 Indirect Threading -- 6.3.6 Lazy Built-in Objects Construction -- 6.4 Experimental Results -- 6.4.1 Environment -- 6.4.2 Performance -- 6.4.3 Footprint -- 6.4.4 Binary Size -- 6.5 Summary and Future Work -- References -- 7 VirISA: Recruiting Virtualization and Reconfigurable Processor ISA for Malicious Code Injection Protection -- 7.1 Introduction -- 7.2 Threat Model -- 7.3 Motivation, Background, and Related Work -- 7.4 Proposed Code Injection Protection Architecture -- 7.4.1 VirISA Main Functionality -- 7.4.2 VirISA Architecture -- 7.4.3 VirISA Permutation Table Rules -- 7.5 Conclusions -- References -- Part III Opportunities, Challenges and Limits in WSN Deployment for IoT -- 8 Deployment Strategies of Wireless Sensor Networks for IoT: Challenges, Trends, and Solutions Based on Novel Tools and HW/SW Platforms -- 8.1 Introduction -- 8.2 Research Trends and State-of-the-Art Approaches -- 8.2.1 A New Perspective for On-Site WSN Deployments -- 8.3 General Overview of the Proposed System -- 8.4 On-Site Deployment and Maintainability Optimization Mechanism -- 8.4.1 Modeling of the Deployment and Maintenance Methodology -- 8.4.2 Deployment Optimization Mechanism -- 8.5 Wireless Mesh Networking Optimization Approach -- 8.6 System Implementation -- 8.7 Experimental Test Cases and Discussion -- 8.8 Conclusions and Future Perspectives -- References -- 9 Wireless Sensor Networks for the Internet of Things: Barriers and Synergies -- 9.1 Introduction -- 9.2 WSN Programming Models and Tools -- 9.2.1 Low-Level Programming -- 9.2.1.1 Operating System-Level Programming -- 9.2.1.2 Virtual Machine or Middleware -- 9.2.2 High-Level Programming -- 9.2.2.1 Model-Based Development -- 9.2.2.2 Group-Level Programming -- 9.2.2.3 Network-Level Programming (Macroprogramming).

9.2.3 Evaluation of Existing WSN Programming Models and Tools -- 9.2.3.1 Low-Level Programming Evaluation -- 9.2.3.2 High-Level Programming Evaluation -- 9.3 WSN Hardware and Server-Side Support -- 9.3.1 WSN Hardware -- 9.3.1.1 Microcontroller -- 9.3.1.2 RF Device -- 9.3.1.3 RF Antenna -- 9.3.1.4 Energy Supply -- 9.3.1.5 Transducers -- 9.3.1.6 Package -- 9.3.1.7 Hardware Nodes -- 9.3.1.8 Server-Side Support -- 9.4 Semi-Automated WSN HW/SW Application Synthesis -- 9.4.1 Semi-Automated Development Flow Overview -- 9.4.2 Automated Hardware-Software Synthesis Tool Overview -- 9.4.3 Automated Synthesis Tool Input Interface -- 9.4.4 Structure of Top-Level and Library Components -- 9.4.5 System Synthesis Process -- 9.4.6 Synthesis Use for Legacy Designs -- 9.5 Synergies for WSN Development Tools and Platforms -- 9.6 Conclusion -- References -- 10 Event Identification in Wireless Sensor Networks -- 10.1 Introduction -- 10.2 Wireless Sensor Network Characteristics -- 10.3 Event Detection Challenges in WSNs -- 10.4 Data Fusion Categorization -- 10.4.1 Data Fusion Algorithmic Approaches -- 10.4.2 Levels of Data Fusion -- 10.5 Classification of Event Detection Approaches -- 10.5.1 Model-Based Approaches -- 10.5.1.1 Arithmetic Model-Based Approaches -- 10.5.1.2 Map-Based Approaches -- 10.5.1.3 Probabilistic/Statistical Model-Based Approaches -- 10.5.2 Pattern Matching-Based Approaches -- 10.5.2.1 Prototype Matching Techniques -- 10.5.2.2 Signature Matching Techniques -- 10.5.3 Artificial Intelligence and Machine Learning-Based Approaches -- 10.5.3.1 Supervised Learning -- 10.5.3.2 Unsupervised Learning -- 10.5.3.3 Fixed Width Clustering -- 10.6 Performance and Behavioral Characteristics of Event Detection Techniques -- 10.6.1 Processing Model of Event Detection -- 10.6.2 Technique's Scalability -- 10.6.3 Sensor Data Types.

10.6.4 Time Constrained Performance Demands -- 10.6.5 Density -- 10.6.6 Evaluation Approach -- 10.7 Conclusions -- References -- Part IV Efficient Data Management and Decision Making for IoT -- 11 Integrating IoT and Fog Computing for Healthcare Service Delivery -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Fog Computing -- 11.3.1 Definition -- 11.3.2 Characteristics -- 11.3.3 Benefits of Integration with the IoT Technology for Healthcare Service Provisioning -- 11.4 Integration of IoT-Fog and Cloud -- 11.4.1 System Model -- 11.4.2 Fog Server Architecture -- 11.5 Use Case Scenarios -- 11.5.1 Daily Monitoring and Healthcare Service Provisioning -- 11.5.2 Extended eCall Service Delivery -- 11.6 Conclusion -- References -- 12 Supporting Decision Making for Large-Scale IoTs: Trading Accuracy with Computational Complexity -- 12.1 Introduction -- 12.2 The Smart Thermostat Usecase -- 12.2.1 System Modeling -- 12.3 Challenges and Motivation -- 12.4 Support Decision Making with the Fuzzy Inference Systems -- 12.4.1 Fuzzy Inference System's Architecture -- 12.5 Communication Links -- 12.6 Evaluation -- 12.7 Conclusions -- References -- 13 Fuzzy Inference Systems Design Approaches for WSNs -- 13.1 Data Classification in WSNs -- 13.1.1 Fuzzy Logic Critical Definitions -- 13.2 Design of a Fuzzy System -- 13.3 Literature Review of Applying Fuzzy Logic in WSN Scenarios -- 13.4 Designing a Health Status FIS for Wireless Sensor Networks Application Scenario -- 13.4.1 Challenges of Applying Data Mining Techniques in WSNs -- 13.4.2 Use Case Scenario -- 13.4.2.1 Healthcare Assessment Fuzzy Inference System -- 13.4.2.2 WSN QoS Assessment Fuzzy Inference System -- 13.4.2.3 Evaluation of Both Systems -- 13.4.3 Centralized Implementation of the HealthCare Assessment FIS in a WSN Platform.

13.4.4 Decentralized Implementation of the HealthCare Assessment FIS in a WSN Platform.

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