Biochip Design and Health Informatics Using IoT and SDN

Suman Lata Tripathi is a Professor in Department of Electronics and Telecommunication at Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India. Akanksha Gupta is an Associate Professor in the Pharmaceutical Chemistry Department at the United Institute of Pharmacy, Prayagraj, India. Abhinav Gupta is an Assistant Professor with the Electronics Engineering Department at Rajkiya Engineering College, Sonbhadra, India. Anurag Sewak is an Assistant Professor in the Computer Science and Engineering Department at Rajkiya Engineering College, Sonbhadra, India. Vivek Srivastava is an Assistant Professor in the Computer Science and Engineering Department at Government Polytechnic Chopan, Sonbhadra, India.

• About the Editors xiiiList of Contributors xvPreface xixAcknowledgments xxii1 Emergent TFET Design and Challenges for Low-Power Biosensors 1Swati Dixit, Varun Mishra, and Manisha Pattanaik1.1 Introduction 11.2 Single-Gate Extended Source TFET and Dielectric Modulated Double-Gate TFET 11.3 Dual-Source Dual-Channel Trench Gate Vertical TFET and Embedded Source Vertical TFET 31.4 Ge Source n+ Pocket and Recessed Drain Line TFET for Biosensor 41.5 InAs Source Dual Metal-Stacked Gate Oxide Heterojunction TFET 51.6 n+ Pocket Vertical Junction TFET 61.7 Inverted T-Shaped Negative Capacitance TFET 71.8 Dielectric Modulated III–IV Compound Semiconductor-Based Pocket-Doped Tfet (me-dg-tfet) 81.9 Conclusion 9References 92 Biochips and Lab-on-a-Chip Systems: VLSI Applications in Medical Diagnostics 11Irfan Ahmad Pindoo and Suman Lata Tripathi2.1 Introduction 112.2 Introduction to Biochips and LoC Systems 122.2.1 Biochips 122.2.2 LoC System 132.2.3 Importance in Modern Healthcare 142.3 Historical Evolution in Microfluidics and Bio-MEMS 152.3.1 Convergence of VLSI and Biomedical Engineering 152.4 Core Components of Biochips and LoC Systems 162.4.1 Microfluidics 162.4.2 Sensing and Detection Mechanisms 172.4.2.1 Optical Sensors 172.4.2.2 Electrochemical Sensors 182.4.2.3 Mechanical Sensors 182.5 Design and Fabrication Techniques 182.5.1 Materials for Biochips and LoCs 182.5.2 Fabrication Methods 192.5.3 Challenges During Design and Fabrication 202.6 Applications in Medical Diagnostics 222.6.1 Point-of-Care Testing 222.6.2 Disease Detection and Monitoring 232.6.3 Genomics and Proteomics 232.6.4 Emerging Applications 232.7 Role of VLSI in Enhancing LoC Systems 242.7.1 Signal Processing and Data Acquisition 242.7.2 Smart Diagnostics 242.7.3 Integration with IoT and Telemedicine 252.7.4 Security and Privacy 262.8 Challenges and Limitations 262.8.1 Technical Challenges 262.8.2 Regulatory and Commercialization Hurdles 272.8.3 Ethical Considerations 272.9 Conclusion 27References 283 Performance Enhancement of Biochips Using Negative Capacitance-Based Junctionless Nanowire for Low-Power VLSI Design 35Manish Kumar Rai, Suman Lata Tripathi, Abhinav Gupta, and Sanjeev Rai3.1 Introduction 353.2 Related Work 363.2.1 Nanowire FET-Based Biosensors 363.2.2 Advancements in JLNW Transistors 373.2.3 Integration of JLNWs in Biosensors 373.2.4 Surface Functionalization Techniques 373.2.5 Biosensing Applications of JLNWs 373.2.6 Challenges and Recent Advances 373.3 Motivation and Proposed Work 383.4 NCJLNW Device Structure and Simulation Setup 383.5 Simulation Result and Discussion 403.5.1 Detection of Neutral Biomolecules 413.5.2 Detection of Charged Biomolecules 433.5.3 Sensitivity Calculation 433.6 Conclusion 43References 434 Application of Wearable and Implantable Medical Devices Using VLSI 47Alok Kumar, Vivek Patel, Tarun Kumar Gupta, and Abhinav Gupta4.1 Introduction 474.2 Electronic Wearables 484.3 Implantable Medical Devices 494.4 VLSI in Compact and Energy-Efficient Wearable and Implantable Medical Devices Design 504.5 Applications of Wearables and Implantable Medical Devices 534.5.1 Biosensors for Disease Detection 534.5.2 Pacemakers and Neurostimulators 544.5.3 Implantable Piezoelectric Nanogenerators 544.5.4 Deep Brain Stimulation 544.5.5 Implantable Drug Delivery Systems 544.6 Opportunities and Challenges 554.7 Conclusion 56References 565 Drug Discovery Using Biochip Technology 63Yuman Tariq and Irfan Ahmad Pindoo5.1 Introduction 635.1.1 Overview of Drug Discovery 635.1.2 Introduction to Biochip Technology 645.2 Fundamentals of Biochip Technology 655.2.1 Design and Components of Biochips 655.2.2 Design and Components of Biochips 675.2.2.1 DNA Microarrays 675.2.2.2 Protein Microarrays 685.2.2.3 Lab-on-a-Chip Systems 685.2.2.4 Organ-on-a-Chip and Tissue Chips 685.3 Applications of Biochips in Drug Discovery 705.3.1 High-Throughput Screening 705.3.1.1 Rapid Screening of Compound Libraries 705.3.1.2 Target Validation and Hit-to-Lead Optimization 705.3.2 Toxicity and Efficacy Assessment 715.3.2.1 Preclinical Toxicity Testing Using Organ-on-a-Chip Models 715.3.2.2 Biomarker Discovery for Patient Stratification 715.3.3 Personalized Medicine and Precision Drug Development 725.3.3.1 Pharmacogenomics Using Biochips for Tailored Therapies 725.3.3.2 Patient-Derived Biochip Models for Individualized Testing 735.4 Microfluidics for Drug Discovery and Development 735.5 Integration with AI and Machine Learning 745.5.1 Data Analysis from Biochip-Generated Datasets 755.5.2 Predictive Modeling for Drug Response 755.6 Challenges and Limitations 765.6.1 Biological and Clinical Relevance 765.6.1.1 Translational Gaps Between In Vitro Models and Human Outcomes 765.6.1.2 Validation and Standardization of Biochip Data 765.6.2 Ethical and Regulatory Considerations 775.6.2.1 Biocompatibility and Safety of Biochip Materials 775.6.2.2 Regulatory Pathways for Biochip-Based Drug Approvals 785.7 Future Perspectives 785.7.1 Potential Breakthroughs 795.7.2 Impact on Pharmaceutical Industry 795.7.3 Ethical Considerations 795.8 Conclusion 80References 806 Biochip System for High-Throughput Drug Screening 87Pratiksha Singh, Akanksha Gupta, Abhishek Tripathi, and Alok Mukerjee6.1 Introduction 876.1.1 Overview of the Drug Discovery Process 876.1.2 Significance of High-Throughput Drug Screening 876.1.3 Principle of Biochip Systems in Screening of Drugs 896.2 Basics of Biochip Technology 896.2.1 Definition and Components of Biochips 896.2.2 Production and Fabrication of Biochip 926.2.3 Modification of Material and Surface for Biochip Application 926.2.4 Integration of Biochip System for HTS 926.3 High-Throughput Screening Using Biochip 946.3.1 Principle and Importance of HTS 946.3.2 Contribution of Biochip in Improving HTS 956.3.3 Advantages of HTS in Drug Discovery 956.4 Importance of Biochip System in Drug Discovery 966.4.1 For Target Identification and Validation 966.4.2 For Toxicity and Side Effect Testing 976.4.3 For Biomarker Identification and Personalized Medicine 976.4.4 Disease Models and Upcoming Biochip Technologies in Drug Screening 976.5 Interpretation and Analysis of Data in HTS Technology 986.6 Associated Challenges in Biochip System 986.6.1 Scale-Up of Large-Scale Drug Screening 986.6.2 Regulatory and Ethical Issues 986.7 Future Prospects and Conclusion 98References 997 Fundamentals of IoT and Advancements: Architectures and Protocols 105Brijendra Pratap Singh, Vimal Kumar, Rajnish Chaturvedi, Sandeep Mishra, Vijay Dwivedi, Naveen Kumar, and Dibya Ranjan Das Adhikary7.1 Introduction 1057.2 Sensor Technology 1077.3 Biochip and AIoT Devices 1097.4 Architecture and Protocols 1107.5 AIoT Security 1137.6 Biochips and IoT in Healthcare 1147.7 Challenges 1167.8 Conclusion and Future Directions 117References 1178 SDN Basic and Architecture 119Amit Kumar Singh and Mayank Pandey8.1 Introduction 1198.1.1 Need for Programmable Networks 1208.1.2 Limitations of Traditional Networks in Healthcare 1218.2 SDN Architecture and Its Components 1218.2.1 Data Plane 1228.2.2 Control Plane 1228.2.3 Application Plane 1238.2.4 OpenFlow SDN Switches 1238.2.5 OpenFlow 1238.2.6 SDN Controller 1248.3 Data Plane Programmability: Overcoming OpenFlow Challenges in SDN 1258.3.1 P4 Language 1268.3.2 P4 Compiler 1268.3.3 Behavioral Model (BMv2) 1268.3.4 P4 Ecosystem 1278.3.5 Technological Considerations of Programmable Data Plane 1278.3.6 Other Data Plane Programming Solutions 1288.4 SDN in Healthcare: A Deeper Dive into Applications 1288.4.1 Real-Time Patient Monitoring 1298.4.2 Telemedicine Optimization 1308.4.3 Secure Medical Data Transfer 1308.4.4 Disaster Recovery and Failover 1308.5 Why Healthcare Needs SDN 1318.5.1 Challenges and Limitations for Introducing SDN in Healthcare 1328.5.2 Future of SDN in Healthcare 1328.6 Conclusion 133References 1339 Integration of Medical Devices with IoT for Remote Patient Monitoring 135Shivani Gupta, Abhishek Tiwari, Amod Kumar Tiwari, and Anurag Sewak9.1 Introduction 1359.1.1 Overview of Internet of Medical Things 1359.1.2 Applications of IoMT in Healthcare 1369.1.2.1 Remote Patient Monitoring 1369.1.2.2 Smart Medical Devices 1369.1.2.3 Telemedicine and Virtual Health 1369.1.2.4 Smart Hospitals 1369.1.2.5 AI-Driven Diagnostics 1369.1.3 Benefits of IoMT 1369.1.4 Communication Protocol 1369.2 Integration of Sensors and Devices with RPM 1389.2.1 Wearable Devices 1389.2.2 Sensor Integration in Wearable Technology 1409.3 Digital Advancement in Healthcare 1419.3.1 Patient Care Impact 1419.3.2 Health Data Utilization 1419.3.3 Operational Efficiency 1429.3.4 Remote Healthcare Monitoring 1429.3.5 Chatbot-Driven Virtual Health Assistant 1429.4 Challenges and Future Direction of Smart Healthcare System 1429.4.1 Body Movement Affecting Sensor Accuracy 1429.4.2 Temperature Changes Affecting Sensor Performance 1439.4.3 Limited Range of Transmission 1439.4.4 QoS in IoMT Networks 1449.5 Conclusion and Future Scope 144References 14410 IoT-Enabled Healthcare Systems: Design, Implementation, and Challenges 147Shrish Bajpai, Divya Sharma, and Amit Kumar Pandey10.1 Introduction 14710.2 Architecture of Healthcare IoT 15010.3 Implementation of IoT in Healthcare 15110.3.1 Identify Use Cases and Requirements 15210.3.2 Select Appropriate IoT Devices and Technologies 15210.3.3 Ensure Interoperability and Integration 15310.3.4 Address Data Security and Privacy 15410.3.5 Establish Data Management and Analytics Capabilities 15410.3.6 Plan for Change Management and Staff Training 15510.4 Challenges of IoT in Healthcare 15510.4.1 Security in IoTs 15610.4.2 Data Handling and Resource Management of Healthcare IoTs 15710.4.3 Interoperability 15810.4.4 Stakeholder Collaboration and Implementation 15810.5 Conclusion 159References 16011 SDN-Enabled Healthcare Networks: Enhancing Connectivity and Security 167Nitin Shukla, Shabir Ali, Neeraj Jain, Ram Kishan Dewangan, and Akhilesh Kumar11.1 Introduction 16711.1.1 Overview of Healthcare Network Requirements 16711.1.2 Introduction to Current Healthcare Technology Trends 16711.1.3 Importance of Reliable Connectivity and Robust Security in Healthcare 16811.1.4 Introduction to Software-Defined Networking and Its Relevance in Healthcare 16811.2 Background Study 16911.2.1 Definition and Principles of SDN 16911.2.2 Key Components of SDN 16911.2.3 Operational Advantages of SDN 17011.3 Traditional Healthcare Network Infrastructure: Issues and Limitations 17111.3.1 Overview of Traditional Network Architectures 17111.3.2 Difficulty in Managing Dynamic Healthcare Demands 17111.4 Motivation for Adopting SDN in Healthcare Networks 17211.4.1 Dynamic and Scalable Network Management 17311.4.2 Enhanced Network Security Capabilities 17311.4.3 Efficient Data Management and Handling 17411.4.4 Real-World Deployment Cases 17411.4.5 Future-Proofing Healthcare Networks 17411.5 SDN in Healthcare: Architecture and Implementation 17511.5.1 Healthcare Use Cases Empowered by SDN 17611.5.2 Telemedicine Expansion 17611.5.3 Real Healthcare Deployments: Evidence from the Field 17711.6 SDN-Enhanced Security in Healthcare Networks 17711.6.1 Tailored Security for Heterogeneous Healthcare Networks 17811.6.2 Lightweight Cryptography and Data Privacy 17911.6.3 Cyberattack Detection and Resilience 17911.6.4 Adaptive Response and Self-Healing Networks 17911.6.5 Secure Interoperability and Edge Trust 18011.7 Integration of SDN with Emerging Technologies in Healthcare 18011.7.1 SDN with AI: Making Networks Smarter and Safer 18111.7.2 SDN with Blockchain: Building Trust in Data Access 18111.7.3 SDN with Fog and Edge Computing: Reducing Delay in Healthcare 18111.7.4 Full Integration: Combining AI, Blockchain, Fog, and SDN 18211.8 Future Challenges and Research Directions 18211.8.1 Integration with Existing Systems 18211.8.2 Data Privacy and Legal Rules 18311.8.3 Performance, Reliability, and Energy Use 18311.8.4 Building Trust and Usability 18311.8.5 Future Research Opportunities 18311.9 Conclusion 184References 18412 Applications of SDN in Healthcare and Drug Delivery Systems 187Ankit Faldu, Ashish Patel, Atul Patel, Anjali Mahavar, Unnati Patel, Jay Nanavati, and Bhargav Vyas12.1 Introduction 18712.1.1 Overview of SDN 18712.1.2 Importance of SDN 18812.1.3 Benefits 18812.2 Role of SDN in Healthcare 18812.2.1 AI-Driven Network Management 18812.2.2 Real-Time Data Analytics and IoT-Enabled Patient Monitoring 18912.2.3 Security Enhancement in SDN for Healthcare 18912.3 SDN in Pharmaceutical Supply Chain Optimization 18912.3.1 Blockchain Supply Chain Management 18912.4 SDN for Telemedicine and Remote Surgical Applications 19012.4.1 Predictive Models for Network Congestion in Hospitals 19112.4.2 Adaptive Traffic Rerouting for Uninterrupted Telemedicine Services 19112.4.3 AI-Optimized SDN for Latency-Sensitive Remote Surgeries 19112.5 Cybersecurity in SDN-Enabled Healthcare Networks 19212.5.1 AI-Driven Threat Intelligence 19212.5.2 Compliance with Legal and Regulatory Standards 19312.6 Challenges in SDN Deployment for Healthcare 19312.6.1 Controller Bottlenecks and Interoperability with Legacy Systems 19412.6.2 Quantum-Resilient Encryption for Securing Sensitive Medical Data 19412.6.3 Resource Constraints in Large-Scale SDN Healthcare Deployments 19512.7 Future Prospects and Innovations in SDN for Healthcare 19512.7.1 Integration with 6G Networks and Neuromorphic Computing 19512.7.2 Autonomous Healthcare Network Management 19612.8 Conclusion 196References 19713 Enhancing Security and Privacy of Bioinformatics Using IoT with Hardware Implementation of Midori128 Cipher 199Pulkit Singh, K Abhimanyu Kumar Patro, Pallavi Joshi, Shipra Upadhyay, and B Sridhar13.1 Introduction 19913.2 Related Work 20113.3 Motivation and Proposed Work 20313.4 Algorithm Overview 20313.4.1 Subcell 20313.4.2 Shuffle Cell 20413.4.3 mix Column 20413.4.4 Key Addition 20413.5 Proposed Methodology: Hardware Implementation 20413.6 Experimental Results and Discussions 20613.7 Conclusion 207References 20714 Emerging Trends in Healthcare Technology: The Role of AI, Big Data, Blockchain, Cloud Computing, and Beyond 211Anjana Rani and Monika Saxena14.1 Introduction 21114.2 Need for Secure and Scalable Healthcare Systems 21214.2.1 Challenges of Traditional Healthcare Systems 21214.2.2 Role of Emerging Technologies in Addressing These Challenges 21214.3 Role of Cloud Computing, AI, and Big Data in Healthcare 21314.3.1 Cloud Computing in Healthcare 21314.3.2 AI in Healthcare 21414.3.3 Big Data in Healthcare 21414.4 Blockchain Technology in Healthcare 21414.4.1 Consensus Algorithms and Their Limitations 21514.4.2 Advantages of Blockchain Integration in Healthcare 21514.5 Proposed Framework for IoMT 21614.5.1 Methodology 21614.5.2 Proposed Hybrid Consensus Model 21614.5.3 Proposed Hybrid Cryptographic Approach 21714.6 Performance Evaluation and Result 21714.7 Conclusion and Future Scope 219References 220Index 223