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Intelligent Secure Trustable Things.

Bibliographic Details
Main Author: Karner, Michael.
Other Authors: Peltola, Johannes., Jerne, Michael., Kulas, Lukas., Priller, Peter.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2024.
Edition:1st ed.
Series:Studies in Computational Intelligence Series
Subjects:
Electronic books.
Online Access:Click to View
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245 1 0 |a Intelligent Secure Trustable Things. 
250 |a 1st ed. 
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264 4 |c ©2024. 
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490 1 |a Studies in Computational Intelligence Series ;  |v v.1147 
505 0 |a Intro -- Acknowledgements -- Contents -- Contributors -- Abbreviations -- Introduction -- Going to the Edge: Bringing Artificial Intelligence and Internet of Things Together -- 1 Introduction -- 2 Objectives -- 3 Trustworthiness -- 4 Building on a Sound Basis -- 5 Driven Through Industrial Applications -- 6 Building Technology for Intelligent, Secure, Trustworthy Things -- 7 Reference Architecture for Trustworthy AIoT -- 8 Summary -- References -- The Development of Ethical and Trustworthy AI Systems Requires Appropriate Human-Systems Integration -- 1 Is Trustworthiness of AI a Problem? -- 2 Current Initiatives to Address Trustworthiness of AI -- 2.1 Guidelines and Regulations -- 2.2 Implementation Support -- 2.3 Observed Gaps in Current Initiatives -- 3 From Technology-Centered to Human-Centered Development of Smart Technologies -- 3.1 Orchestrating the Development of Ethical and Trustworthy AI -- 4 Conclusions -- References -- The InSecTT Reference Architecture -- 1 Introduction -- 2 AI in IoT Architectures -- 3 Overview of the InSecTT Architecture -- 3.1 Evolution of the Bubble -- 3.2 Modern Reference Architectures -- 4 Entity Model -- 5 Layered Model -- 5.1 Level 0 -- 5.2 Level 1 -- 5.3 Level 2 -- 5.4 Hardware Interfaces -- 6 Domain Model -- 7 Functionality Model -- 7.1 SW Interfaces -- 8 Information Model -- 9 AI Perspective of the Architecture -- 10 Example Use Cases Alignment -- 10.1 Overview -- 10.2 Entity Model -- 10.3 Functionality model -- 10.4 Interfaces -- 10.5 General Project Overview for Architecture Alignment -- References -- Structuring the Technology Landscape for Successful Innovation in AIoT -- 1 Motivation -- 2 How to Structure Research and Development to Enact an Ambitious Project Vision -- 3 Requirements and Constraints -- 4 Requirement Engineering Process -- 5 Navigating the Landscape: Planning R&amp -- D Work. 
505 8 |a 6 External Alignment -- 7 Documenting Scope, Work and Results -- 8 Progress Assessment and Validation -- 9 Demonstrators -- 10 Preparing for Market: Exploitation -- 11 InSecTT Exploitation Board (EB) -- 12 Use Case Marketplace -- 13 Open Innovation -- 14 Publications to Prepare Markets -- 15 Website and Social Networks -- 16 Industrial Conferences, Trade Fairs and Podcasts -- Technology Development -- InSecTT Technologies for the Enhancement of Industrial Security and Safety -- 1 Introduction -- 2 Background -- 2.1 Industrial Automation and Control Systems -- 3 Selected InSecTT Technologies Targeting Security and Safety -- 3.1 Access Control and Authentication Infrastructure -- 3.2 Intrusion Detection Systems -- 3.3 Tools, Simulators and Datasets -- 3.4 Safety and Security Analysis for AGV Platooning -- 4 Novelty and Applicability of Proposed Technologies -- 5 Conclusions and Future Perspectives -- References -- Algorithmic and Implementation-Based Threats for the Security of Embedded Machine Learning Models -- 1 Introduction -- 2 Threat Models -- 2.1 Formalism -- 2.2 Adversarial Objectives -- 2.3 The System Under Attack -- 2.4 Knowledge and Capacity of an Adversary -- 2.5 Attack Surface -- 3 A Panorama of Algorithmic Attacks -- 3.1 Confidentiality and Privacy Threats -- 3.2 Integrity-Based Attacks -- 3.3 Availability -- 4 A Focus on Physical Attacks -- 4.1 Model Extraction Based on Side-Channel Analysis -- 4.2 Weight-Based Adversarial Attacks -- 5 Protecting ML System -- 5.1 Embedded Authentication Mechanism -- 5.2 Main Defenses Against Algorithmic Attacks -- 5.3 Countermeasures Against Physical Attacks -- 6 Conclusion -- References -- Explainable Anomaly Detection of 12-Lead ECG Signals Using Denoising Autoencoder -- 1 Introduction -- 2 Anomaly Detection and Explainability in Deep Learning. 
505 8 |a 3 Denosisng Autoencoder as an Explainable Anomaly Detection Model for ECGs -- 3.1 ECG Data Sets -- 3.2 Model Architecture and Training -- 3.3 Results of Denoising and the Exploration of the Latent Space -- 4 Cloud-Based Service and Visualization of Explainable Anomaly Detection on ECGs -- 5 Conclusion -- References -- Indoor Navigation with a Smartphone -- 1 Introduction -- 2 Encoding Information in QR -- 3 Navigation -- 4 Local to Global Coordinates -- 5 Triage -- 6 Future Work -- 7 Conclusions -- References -- Reconfigurable Antennas for Trustable Things -- 1 Introduction -- 2 Electronically Steerable Parasitic Array Radiator Antenna for Trustable Things -- 2.1 Concept -- 2.2 Design -- 2.3 Realization -- 3 Applications -- 3.1 Direction of Arrival Estimation -- 3.2 Power Pattern Cross-Correlation Algorithm -- 3.3 Interpolation-Based Estimation -- 3.4 Multiplane Calibration for 2D DoA Estimation -- 3.5 DoA-Based Object Positioning -- 3.6 Single-Anchor Positioning System -- 3.7 Calibration-Free Indoor Localization -- 3.8 Other Applications -- References -- AI-Enhanced Connection Management for Cellular Networks -- 1 Introduction -- 2 Related Work -- 2.1 Data Rate Estimation -- 2.2 Interface Selection -- 3 Use Case and Research Challenge -- 4 Data Collection and Analysis -- 5 Uplink Data Rate Estimation -- 6 Interface Decision -- 7 Conclusion -- References -- Vehicle Communication Platform to Anything-VehicleCAPTAIN -- 1 Introduction -- 2 Problem Statement -- 3 VehicleCAPTAIN-A V2X Platform for Research and Development -- 3.1 The Platform -- 3.2 Message Library -- 3.3 ROS2 Support -- 4 Verification -- 4.1 Test Methodology -- 4.2 Results -- 4.3 Discussion -- 5 Key Performance Indicators -- 5.1 Use Cases Within InSecTT -- 5.2 Use Cases Within the Virtual Vehicle Research GmbH -- 6 Conclusion -- References. 
505 8 |a AI-Enhanced UWB-Based Localisation in Wireless Networks -- 1 Introduction -- 2 Method Overview -- 3 AI for Solving UWB-Based Localisation Challenges -- 3.1 Localisation Challenges -- 3.2 AI Algorithms in UWB-Based Localisation Systems -- 4 Overview of Related Work -- 5 Application Example -- 5.1 KNN for LOS/NLOS Detection -- 5.2 KNN for Error Mitigation and Trustworthiness -- 6 Conclusion -- References -- Industrial Applications -- Approaches for Automating Cybersecurity Testing of Connected Vehicles -- 1 Introduction -- 2 State of the Art and Related Work -- 3 Automotive Cybersecurtiy Lifecycle Management -- 3.1 Threat Modeling -- 4 Cybersecurity Testing -- 4.1 Learning-Based Testing -- 4.2 Model-Based Test Case Generation -- 4.3 Testing Platform -- 4.4 Automated Test Execution -- 4.5 Fuzzing -- 5 Conclusion -- References -- Solar-Based Energy Harvesting and Low-Power Wireless Networks -- 1 Introduction -- 1.1 Solar-Based Energy Harvesting -- 2 Low-Power Network Protocols -- 2.1 Bluetooth Low Energy -- 2.2 IEEE 802.15.4 and Thread -- 2.3 EPhESOS Protocol -- 2.4 UWB Localisation -- 3 Power Consumption in Different Scenarios -- 3.1 Measurement Setup and Hardware -- 3.2 Power Consumption with Increasing Update Period -- 4 Available Energy in Real-World Scenarios -- 5 Experimental Results -- 6 Conclusion -- References -- Location Awareness in HealthCare -- 1 Terminology and Technology -- 1.1 Positioning, Localization, Tracking and Navigation -- 1.2 RF-Based Indoor Localization Technologies -- 1.3 Non-RF Based Localization Technologies -- 2 Pedestrian Dead Reckoning (PDR) -- 3 Others -- 3.1 Outdoor Localization Technologies -- 3.2 Technology Overview -- 4 Designing an End-To-End IoT Solution -- 4.1 Commissioning -- 4.2 Low Power Wide Area Networks (LPWAN) -- 4.3 Battery Lifetime -- 4.4 Going from Indoor to Outdoor -- 4.5 APIs for Location Services. 
505 8 |a 4.6 Visualizing on a Map -- 4.7 Security and Privacy Aspects -- 5 Healthcare Use-Cases -- 5.1 Asset Tracking -- 5.2 Mass Casualty Incident (MCI) -- 5.3 Bed Management -- 5.4 Hospital Wayfinding -- 6 Use-Case Concept Demonstrator -- 6.1 Architecture -- 6.2 GeoJSON Server -- 6.3 Client Authentication -- 6.4 FHIR Compatibility -- 6.5 Location and Privacy -- 6.6 Additional Features -- 7 Conclusions/Next Steps -- References -- Driver Distraction Detection Using Artificial Intelligence and Smart Devices -- 1 Introduction -- 2 Definitions and Background -- 3 System Design -- 4 Machine Learning-Based Components -- 4.1 Use Case Definition and Components' Architecture -- 4.2 Data Acquisition and Pre-processing -- 4.3 Machine Learning Model Training and Experimental Results -- 4.4 Model Deployment on Smart Devices -- 5 Dashboard Application for Driver Distraction -- 6 Related Work -- 7 Conclusion and Future Work -- References -- Working with AIoT Solutions in Embedded Software Applications. Recommendations, Guidelines, and Lessons Learned -- 1 Introduction -- 2 Project Description and Goals -- 3 Project Design -- 4 Machine Learning in Embedded Systems -- 5 Communication Platform -- 5.1 Design Layout -- 5.2 Message Queuing with RabbitMQ -- 5.3 Inter-Process Messaging -- 6 Data Extraction -- 7 Training Data Set and Model -- 7.1 Design Stage 1 -- 7.2 Design Stage 2 -- 7.3 Alternative Model Setup -- 8 Cloud or Edge? -- 9 Security -- 10 Conclusion -- Appendix A -- Appendix B -- References -- Artificial Intelligence for Wireless Avionics Intra-Communications -- 1 Introduction -- 2 Use Case Objectives -- 3 Link Between Scenarios and Building Blocks -- 4 State of the Art -- 5 AI/IoT Added Value -- 6 Scenarios -- 6.1 Scenario 1: Interference Detection and Cancellation -- 6.2 Scenario 2: Verification and Validation of WAICs -- 6.3 Scenario 3: Battery-Less Devices. 
505 8 |a 7 Performance Evaluation. 
588 |a Description based on publisher supplied metadata and other sources. 
590 |a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.  
655 4 |a Electronic books. 
700 1 |a Peltola, Johannes. 
700 1 |a Jerne, Michael. 
700 1 |a Kulas, Lukas. 
700 1 |a Priller, Peter. 
776 0 8 |i Print version:  |a Karner, Michael  |t Intelligent Secure Trustable Things  |d Cham : Springer International Publishing AG,c2024  |z 9783031540486 
797 2 |a ProQuest (Firm) 
830 0 |a Studies in Computational Intelligence Series 
856 4 0 |u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=31498845  |z Click to View 

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