Cloud Network Management : An IoT Based Framework

Data storage, processing, and management at remote location over dynamic networks is the most challenging task in cloud networks. Users’ expectations are very high for data accuracy, reliability, accessibility, and availability in pervasive cloud environment. It was the core motivation for the Cloud Networks Internet of Things (CNIoT). The exponential growth of the networks and data management in CNIoT must be implemented in fast growing service sectors such as logistic and enterprise management. The network based IoT works as a bridge to fill the gap between IT and cloud networks, where data is easily accessible and available. This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has following salient features, A cloud-based next generation networking technologies. Cloud-based IoT and mobility management technology. The proposed book is a reference for research scholars and course supplement for cloud-IoT related subjects such as distributed networks in computer/ electrical engineering. Sanjay Kumar Biswash is working as an Assistant professor in NIIT University, India. He held Research Scientist position, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia. He was PDF at LNCC, Brazil and SDSU, USA. He was a visiting researcher to the UC, Portugal. Sourav Kanti Addya is working as an Assistant professor in NITK, Surathkal, India. He was a PDF at IIT Kharagpur, India. He was a visiting scholar at SDSU, USA. He obtained national level GATE scholarship. He is a member of IEEE, ACM. Cover Half Title Title Page Copyright Page Dedication Contents Foreword Preface Editors Contributors Abstract Part I: Evolution of IoT, Cloud Network and Network Mobility 1. Evolution of Cloud Fog IoT Interconnection Networks 1.1 Introduction 1.2 Motivation and Contributions 1.3 Evolution of Traditional cloud networks 1.4 Into the Fog 1.5 IoT Fog Cloud Interplay 1.5.1 Challenges in IoT Fog Cloud Interplay 1.5.1.1 Resource Management 1.5.1.2 Inter and Intra Stratum Communication 1.5.1.3 Cloud Fog Federation 1.5.2 Applications of IoT Fog Cloud Interplay 1.5.2.1 Healthcare Applications 1.5.2.2 Connected Vehicles 1.5.2.3 Smart City Applications 1.6 Research Challenges and Solution Approach 1.7 Conclusion 2. Edge or Cloud: What to Choose? 2.1 Introduction 2.2 Background & Related Work 2.2.1 Edge Based Learning 2.2.2 Cloud Computing 2.2.3 K Means 2.3 Experiment 2.3.1 Edge Based Learning Procedure 2.3.2 Cloud Based Learning Procedure 2.3.3 Experimental Objectives 2.3.4 Setup 2.4 Analysis 2.4.1 CPU Utilization 2.4.2 Memory Utilization 2.4.3 Data Transmission Rate 2.4.4 Power Consumption 2.4.5 Energy Consumption 2.4.6 Summary 2.5 Findings 2.5.1 Edge Based Learning 2.5.2 Cloud based Learning 2.5.3 Comparison 2.6 Conclusion 3. The Survey, Research Challenges, and Opportunities in ICN 3.1 Introduction 3.2 Internet architecture and working 3.2.1 Research challenges and issues 3.3 Information Centric Networks (ICN) 3.3.1 Important terminologies used in ICN 3.3.2 Concepts and components of Information Centric Networking 3.3.2.1 ICN Naming Scheme 3.3.2.2 Routing in ICN 3.3.2.3 In Network Caching 3.3.3 ICN Architectures 3.3.3.1 Data Oriented Network Architecture (DONA) 3.3.3.2 Named data Networking 3.3.3.3 Other architectures 3.3.4 Information Centric Networking based Internet of Things 3.3.4.1 Why ICN for IoT? 3.3.4.2 IoT Architecture Requirements 3.3.4.3 Significance of ICN for IoT 3.3.4.4 IoT Requirements Mapping to ICN Characteristics 3.3.4.5 ICN IoT network architectures 3.3.4.6 In network Computation in Edge Computing and Cloud Computing 3.4 Conclusion Part II: Standards and Protocol 4. Security in Cloud Based IoT 4.1 Introduction 4.2 Motivation and Contribution 4.3 Research Method and Research Challenge 4.4 Cloud based IoT: Technologies and Design Issues 4.4.1 Design Issues 4.5 Cloud Based IoT: Security Threats 4.5.1 Cloud Security Threats 4.5.2 IoT and Cloud based IoT Security Threats 4.6 Implementation aspects of Cloud based IoT 4.7 Concluding Remarks 5. Cloud Enabled Body Area Network 5.1 Abstract 5.2 Introduction 5.3 Bio Sensor Nodes 5.4 Body Area Network 5.4.1 Communication Architecture 5.4.2 Physical and MAC Layers of BAN 5.5 Cryptographic Building Blocks 5.5.1 Cryptographic Hash Function 5.5.2 Homomorphic Encryption 5.5.3 Bilinear Pairing 5.5.4 Attribute Based Encryption 5.6 Privacy and Security 5.6.1 Security Notions in Cloud enabled BAN 5.6.2 Attacks and Threats in Cloudenabled BAN 5.6.3 Existing Security and Privacy Solutions in Cloud enabled BAN 5.7 Authentication in BAN 5.8 Key Management in BAN 5.9 Conclusion 6. Trust and Access Controls in IoT to Avoid Malicious Activity 6.1 Introduction 6.2 Threats, Vulnerabilities, and Access control Requirement in Internet of Things 6.2.1 Threats 6.2.2 Vulnerabilities 6.2.3 The importance of Access controls and Trust of users 6.3 Literature Review 6.4 Problem formation 6.4.1 Improved Trust calculation 6.5 Simulations 6.6 Access Controls on Sensitive Data 6.6.1 Algorithm 1: 6.6.2 Algorithm 2: 6.7 Conclusions 7. A Layered Internet of Things (IoT) Security Framework: Attacks, Counter Measures and Challenges 7.1 Introduction 7.2 Related Work 7.3 Taxonomy of IoT Attacks 7.3.1 Physical Layer Attacks (PLA) 7.3.1.1 Physical Node Tampering 7.3.1.2 Malicious Node Injection 7.3.1.3 RFID Tag Cloning 7.3.2 Wireless Sensor Network Layer Attacks (NLA) 7.3.2.1 Jamming Attack 7.3.2.2 Side Channel Attack 7.3.2.3 MAC Spoofing 7.3.3 Data Sensing and Acquisition Layer Attacks (DSAL) 7.3.3.1 Malicious Code 7.3.3.2 Traffic Monitoring 7.3.3.3 Inefficient Logging 7.3.4 Internet Layer Attacks (ILA) 7.3.4.1 Jamming Attack 7.3.4.2 False Routing 7.3.4.3 Alteration and Spoofing 7.3.5 Service Layer Attacks (SLA) 7.3.5.1 Account Hijacking 7.3.5.2 VM Escape 7.3.5.3 Malicious VM Creation 7.3.6 Data Abstraction Layer Attacks (DALA) 7.3.6.1 Malicious node Injection 7.3.6.2 Improper Queries 7.3.6.3 Malicious Insider 7.3.7 Interface Layer Attacks (ILA) 7.3.7.1 Reverse Engineering 7.3.7.2 Reprogramming Attack 7.3.7.3 DDoS Attack 7.4 Proposed IoT Security Framework 7.4.1 Perception Layer 7.4.2 Wireless Sensor Network Layer 7.4.3 Data Sensing and acquisition layer 7.4.4 Internet Layer 7.4.5 Service Layer 7.4.6 Data Abstraction Layer 7.4.7 Interface Layer 7.5 Case Study: Implementation of Denial of Service Attack in Home Automation 7.5.1 A brief description of attack 7.5.2 Experimental Test bed Details 7.5.3 Execution Steps 7.6 Research and Challenges 7.7 Conclusion Part III: Engineering and Applications for IoT Cloud Network 8. A Novel Framework of Smart Cities Using Internet of Things (IoT): Opportunities and Challenges 8.1 Introduction 8.1.1 IoT infrastructure for smart city 8.1.1.1 Network centric IoT 8.1.1.2 Cloud centric IoT 8.1.1.3 Data Centric IoT 8.1.1.4 Human Centric IoT 8.2 Smart City Hierarchy 8.2.1 Associated communication technology for realizing smart cities 8.3 Proposed Framework 8.3.1 Sensing Layer 8.3.2 Data Abstraction layer 8.3.3 Base station layer 8.3.4 Edge server layer 8.3.5 Cloud computing layer 8.3.6 Application layer 8.4 Case Study 8.4.1 Smart traffic management 8.4.2 Smart Healthcare 8.5 Open Challenges and opportunities 8.6 Conclusion 9. Interoperability and Information Sharing Paradigm for IoT Enabled Healthcare 9.1 Introduction 9.2 Mobile Health and the Internet of Medical Things 9.3 Enabling Precision & Personalized Medicine 9.4 Health Data Ownership in IoT and the Cloud 9.4.1 IoT Data Ownership Challenges 9.4.1.1 Consent for Data Capture 9.4.1.2 Verifying Data Ownership: Local Identity Management and Authentication 9.4.2 Healthcare Data Ownership 9.4.2.1 Electronic Health Record (EHR) 9.4.2.2 Personal Health Record 9.4.2.3 Bridging Medical Data Ownership: Combining EHR and PHR 9.5 Enabling IoMT Information Sharing in Healthcare 9.5.1 Collecting Data from IoMT Devices 9.5.2 Traditional Health Record Information Exchange for Informa tion Federation 9.5.2.1 Regulating Provider Access to PHR Data 9.5.2.2 Providing Emergency Data Access 9.5.3 Ensuring Data Integrity from IoMT Sensors 9.5.4 Privately Replicating and Sharing Large Datasets 9.5.5 Maintaining Consensus in Large Scale Federated Systems 9.5.6 Providing Emergency Access to Real Time IoMT Data 9.6 Achieving Heterogeneous Data Interoperability 9.6.1 Interoperability Architecture Overview 9.6.2 Current Interoperability Standards 9.6.3 Future Standards and Alternative Methods 9.7 Challenges & Opportunities 10. Cloud Computing Based Intelligent Healthcare System 10.1 Introduction 10.2 Building an intelligent healthcare system 10.3 Early detection and prediction of brain tumor using Intel ligent Cloud 10.3.1 Classification using different models 10.3.2 Image Inversion 10.4 Experiments and Results 10.4.1 Naive Bayes Classifier Model 10.4.2 CNN Model 10.4.3 Image Inversion 10.4.4 Summary and Discussion 10.5 Research Challenges and possible solutions 10.6 Conclusion 10.7 Acknowledgement 11. IoT Cloud Network for Healthcare 11.1 Introduction to modern health computing 11.2 Overcoming the challenges 11.2.1 Security and privacy of patient data 11.2.2 Lack of uniformity among connected mobile devices 11.2.3 Vulnerable data transmissions 11.2.4 Patient readiness 11.2.5 Awareness about IoTs 11.2.6 Paralysis of Data Analysis 11.3 Cloud computing over the intelligent healthcare system 11.4 IoT and smart health system paradigms 11.4.1 History of IoT in healthcare 11.4.2 Role of IoT in Healthcare 11.4.3 Challenges of IoT in healthcare 11.4.4 Future of IoT in healthcare 11.4.5 Patient centered care 11.4.6 Teleconsultation and Remote Patient monitoring 11.4.7 Wearable sensors 11.4.8 Insideable devices 11.4.9 Mobile apps 11.4.10 Electronic Medical Records (EMR) 11.4.11 Health portals 11.4.12 Big data 11.4.13 The human genome project 11.4.14 Personalized and precision medicine 11.4.15 3D Printing 11.4.16 Artificial intelligence in healthcare 11.5 New Design and Performance of IoT cloud for Smart Healthcare and Monitor system 11.5.1 Disruptions in Internet 11.5.2 Diversity of Protocols 11.5.3 No Special Testing Tools Were Made for Healthcare Applica tions 11.5.4 Difficulties in Performing Healthcare IoT Performance Testing 11.5.5 Mobile technology in revolution of Smart Healthcare 11.5.6 Financial challenges 11.5.7 SaaS helps improve delivery of Hospital services 11.5.8 The benefits of cloud computing 11.5.9 Cloud security and regulatory compliance 11.5.10 Spend less money, serve more patients 11.5.11 mHealth in action 11.5.11.1 IoMT Platforms 11.5.11.2 Amazon Web Services IoT 11.5.11.3 Qualcomm Life 11.5.11.4 Data Flow 11.5.11.5 Azure IoT Suite 11.5.11.6 Intel IoT 11.5.12 Compliance and Regulations 11.5.12.1 HIPAA Rules 11.5.12.2 HITECH Act 11.5.12.3 HITRUST 11.5.12.4 PCI 11.5.13 What We See in Future 11.5.13.1 Healthcare Robots 11.5.13.2 The Brain Computer Interface Bibliography Index Human Action Recognition Is A Challenging Area Presently. The Vigor Of Research Effort Directed Towards This Domain Is Self Indicative Of This. With The Ever-increasing Involvement Of Computational Intelligence In Our Day To Day Applications, The Necessity Of Human Activity Recognition Has Been Able To Make Its Presence Felt To The Concerned Research Community. The Primary Drive Of Such An Effort Is To Equip The Computing System Capable Of Recognizing And Interpreting Human Activities From Posture, Pose, Gesture, Facial Expression Etc. The Intent Of Human Activity Recognition Is A Formidable Component Of Cognitive Science In Which Researchers Are Actively Engaged Of Late. Features: A Systematic Overview Of The State-of-the-art In Computational Intelligence Techniques For Human Action Recognition; Emphasized On Different Intelligent Techniques To Recognize Different Human Actions; Discussed About The Automation Techniques To Handle Human Action Recognition; Recent Research Results And Some Pointers To Future Advancements In This Arena. In The Present Endeavour The Editors Intend To Come Out With A Compilation That Reflects The Concerns Of Relevant Research Community. The Readers Would Be Able To Come Across Some Of The Latest Findings Of Active Researchers Of The Concerned Field. It Is Anticipated That This Treatise Shall Be Useful To The Readership Encompassing Students At Undergraduate And Postgraduate Level, Researchers Active As Well As Aspiring, Not To Speak Of The Senior Researchers. Data storage, processing, and management at remote location over dynamic networks is the most challenging task in cloud networks. Users’ expectations are very high for data accuracy, reliability, accessibility, and availability in pervasive cloud environment. It was the core motivation for the __Cloud Networks Internet of Things__ (CNIoT). The exponential growth of the networks and data management in CNIoT must be implemented in fast growing service sectors such as logistic and enterprise management. The network based IoT works as a bridge to fill the gap between IT and cloud networks, where data is easily accessible and available. This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has following salient features, * A cloud-based next generation networking technologies. * Cloud-based IoT and mobility management technology. Sanjay Kumar Biswash is working as an Assistant professor in NIIT University, India. He held Research Scientist position, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia. He was PDF at LNCC, Brazil and SDSU, USA. He was a visiting researcher to the UC, Portugal. Sourav Kanti Addya is working as an Assistant professor in NITK, Surathkal, India. He was a PDF at IIT Kharagpur, India. He was a visiting scholar at SDSU, USA. He obtained national level GATE scholarship. He is a member of IEEE, ACM. "Data storage, processing, and management from remote locations over dynamic networks is the most challenging task in cloud networks. Users' expectations are very high for data accuracy, reliability, accessibility, and availability in a pervasive cloud environment. It was the core motivation for the Cloud Networks Internet of Thigns (CNIoT). The exponential growth of the networks and data management in CNIoT must be implemented in fast-growing serivce sectors such as logistic and enterprise management. The network-based IoT works to fill the gap between IT and cloud networks, where data is easily accessible and available. This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has the following salient features: a focus on cloud-based next generation networking technologies and cloud-based IoT and mobility management technology. The book is a reference for research scholars and course supplement for cloud-IoT related subjects such as distributed networks in computer/electrical engineering."--Back cover