Biochip Design and Health Informatics Using IoT and SDN
Description
Enables readers to design biochips, use IoT for real-time health data, and apply software-defined networking (SDN) to manage healthcare networks
Biochip Design and Health Informatics Using IoT and SDN offers a comprehensive view on how biochips can be integrated with IoT and SDN technologies to revolutionize healthcare systems, providing a unique solution for building smart, interconnected health informatics systems. It explains how biochips combined with IoT enable continuous, real-time health data monitoring, helping healthcare professionals improve patient outcomes through timely, accurate diagnostics and treatments.
The book addresses key concerns around data security and patient privacy in IoT-based healthcare systems, along with implications of scalability, flexibility, and efficiency using SDN, allowing effective management of health data. By emphasizing the scalability and adaptability of IoT and SDN in healthcare, the book helps readers design systems that can evolve with technological advancements, ensuring they remain relevant and effective in the future.
Edited by a team of highly qualified experts, this book includes information on:
- Fundamentals of IoT and SDN, reviewing basic architectures and protocols for each
- Biochips' critical role in high-throughput screening, biosensing, and real-time data acquisition for pharmaceutical research
- How IoT and SDN technologies accelerate drug screening, pharmacokinetic modeling, and personalized medicine applications, enabling effective remote management of patients
- Integration of VLSI based biochip technology, IoT, and SDN to address critical challenges such as data security, network architecture design, privacy concerns, and resource optimization in drug discovery workflows
Biochip Design and Health Informatics Using IoT and SDN is an essential, timely reference for professionals, university professors, research scientists in biochips and nanotechnology, and PhD and graduate students working at the intersection of VLSI and biomedical engineering, health informatics, and network systems.
More details
Other editions
Additional editions
Persons
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.
Content
About the Editors xiii
List of Contributors xv
Preface xix
Acknowledgments xxii
1 Emergent TFET Design and Challenges for Low-Power Biosensors 1
Swati Dixit, Varun Mishra, and Manisha Pattanaik
1.1 Introduction 1
1.2 Single-Gate Extended Source TFET and Dielectric Modulated Double-Gate TFET 1
1.3 Dual-Source Dual-Channel Trench Gate Vertical TFET and Embedded Source Vertical TFET 3
1.4 Ge Source n+ Pocket and Recessed Drain Line TFET for Biosensor 4
1.5 InAs Source Dual Metal-Stacked Gate Oxide Heterojunction TFET 5
1.6 n+ Pocket Vertical Junction TFET 6
1.7 Inverted T-Shaped Negative Capacitance TFET 7
1.8 Dielectric Modulated III-IV Compound Semiconductor-Based Pocket-Doped Tfet (me-dg-tfet) 8
1.9 Conclusion 9
References 9
2 Biochips and Lab-on-a-Chip Systems: VLSI Applications in Medical Diagnostics 11
Irfan Ahmad Pindoo and Suman Lata Tripathi
2.1 Introduction 11
2.2 Introduction to Biochips and LoC Systems 12
2.2.1 Biochips 12
2.2.2 LoC System 13
2.2.3 Importance in Modern Healthcare 14
2.3 Historical Evolution in Microfluidics and Bio-MEMS 15
2.3.1 Convergence of VLSI and Biomedical Engineering 15
2.4 Core Components of Biochips and LoC Systems 16
2.4.1 Microfluidics 16
2.4.2 Sensing and Detection Mechanisms 17
2.4.2.1 Optical Sensors 17
2.4.2.2 Electrochemical Sensors 18
2.4.2.3 Mechanical Sensors 18
2.5 Design and Fabrication Techniques 18
2.5.1 Materials for Biochips and LoCs 18
2.5.2 Fabrication Methods 19
2.5.3 Challenges During Design and Fabrication 20
2.6 Applications in Medical Diagnostics 22
2.6.1 Point-of-Care Testing 22
2.6.2 Disease Detection and Monitoring 23
2.6.3 Genomics and Proteomics 23
2.6.4 Emerging Applications 23
2.7 Role of VLSI in Enhancing LoC Systems 24
2.7.1 Signal Processing and Data Acquisition 24
2.7.2 Smart Diagnostics 24
2.7.3 Integration with IoT and Telemedicine 25
2.7.4 Security and Privacy 26
2.8 Challenges and Limitations 26
2.8.1 Technical Challenges 26
2.8.2 Regulatory and Commercialization Hurdles 27
2.8.3 Ethical Considerations 27
2.9 Conclusion 27
References 28
3 Performance Enhancement of Biochips Using Negative Capacitance-Based Junctionless Nanowire for Low-Power VLSI Design 35
Manish Kumar Rai, Suman Lata Tripathi, Abhinav Gupta, and Sanjeev Rai
3.1 Introduction 35
3.2 Related Work 36
3.2.1 Nanowire FET-Based Biosensors 36
3.2.2 Advancements in JLNW Transistors 37
3.2.3 Integration of JLNWs in Biosensors 37
3.2.4 Surface Functionalization Techniques 37
3.2.5 Biosensing Applications of JLNWs 37
3.2.6 Challenges and Recent Advances 37
3.3 Motivation and Proposed Work 38
3.4 NCJLNW Device Structure and Simulation Setup 38
3.5 Simulation Result and Discussion 40
3.5.1 Detection of Neutral Biomolecules 41
3.5.2 Detection of Charged Biomolecules 43
3.5.3 Sensitivity Calculation 43
3.6 Conclusion 43
References 43
4 Application of Wearable and Implantable Medical Devices Using VLSI 47
Alok Kumar, Vivek Patel, Tarun Kumar Gupta, and Abhinav Gupta
4.1 Introduction 47
4.2 Electronic Wearables 48
4.3 Implantable Medical Devices 49
4.4 VLSI in Compact and Energy-Efficient Wearable and Implantable Medical Devices Design 50
4.5 Applications of Wearables and Implantable Medical Devices 53
4.5.1 Biosensors for Disease Detection 53
4.5.2 Pacemakers and Neurostimulators 54
4.5.3 Implantable Piezoelectric Nanogenerators 54
4.5.4 Deep Brain Stimulation 54
4.5.5 Implantable Drug Delivery Systems 54
4.6 Opportunities and Challenges 55
4.7 Conclusion 56
References 56
5 Drug Discovery Using Biochip Technology 63
Yuman Tariq and Irfan Ahmad Pindoo
5.1 Introduction 63
5.1.1 Overview of Drug Discovery 63
5.1.2 Introduction to Biochip Technology 64
5.2 Fundamentals of Biochip Technology 65
5.2.1 Design and Components of Biochips 65
5.2.2 Design and Components of Biochips 67
5.2.2.1 DNA Microarrays 67
5.2.2.2 Protein Microarrays 68
5.2.2.3 Lab-on-a-Chip Systems 68
5.2.2.4 Organ-on-a-Chip and Tissue Chips 68
5.3 Applications of Biochips in Drug Discovery 70
5.3.1 High-Throughput Screening 70
5.3.1.1 Rapid Screening of Compound Libraries 70
5.3.1.2 Target Validation and Hit-to-Lead Optimization 70
5.3.2 Toxicity and Efficacy Assessment 71
5.3.2.1 Preclinical Toxicity Testing Using Organ-on-a-Chip Models 71
5.3.2.2 Biomarker Discovery for Patient Stratification 71
5.3.3 Personalized Medicine and Precision Drug Development 72
5.3.3.1 Pharmacogenomics Using Biochips for Tailored Therapies 72
5.3.3.2 Patient-Derived Biochip Models for Individualized Testing 73
5.4 Microfluidics for Drug Discovery and Development 73
5.5 Integration with AI and Machine Learning 74
5.5.1 Data Analysis from Biochip-Generated Datasets 75
5.5.2 Predictive Modeling for Drug Response 75
5.6 Challenges and Limitations 76
5.6.1 Biological and Clinical Relevance 76
5.6.1.1 Translational Gaps Between In Vitro Models and Human Outcomes 76
5.6.1.2 Validation and Standardization of Biochip Data 76
5.6.2 Ethical and Regulatory Considerations 77
5.6.2.1 Biocompatibility and Safety of Biochip Materials 77
5.6.2.2 Regulatory Pathways for Biochip-Based Drug Approvals 78
5.7 Future Perspectives 78
5.7.1 Potential Breakthroughs 79
5.7.2 Impact on Pharmaceutical Industry 79
5.7.3 Ethical Considerations 79
5.8 Conclusion 80
References 80
6 Biochip System for High-Throughput Drug Screening 87
Pratiksha Singh, Akanksha Gupta, Abhishek Tripathi, and Alok Mukerjee
6.1 Introduction 87
6.1.1 Overview of the Drug Discovery Process 87
6.1.2 Significance of High-Throughput Drug Screening 87
6.1.3 Principle of Biochip Systems in Screening of Drugs 89
6.2 Basics of Biochip Technology 89
6.2.1 Definition and Components of Biochips 89
6.2.2 Production and Fabrication of Biochip 92
6.2.3 Modification of Material and Surface for Biochip Application 92
6.2.4 Integration of Biochip System for HTS 92
6.3 High-Throughput Screening Using Biochip 94
6.3.1 Principle and Importance of HTS 94
6.3.2 Contribution of Biochip in Improving HTS 95
6.3.3 Advantages of HTS in Drug Discovery 95
6.4 Importance of Biochip System in Drug Discovery 96
6.4.1 For Target Identification and Validation 96
6.4.2 For Toxicity and Side Effect Testing 97
6.4.3 For Biomarker Identification and Personalized Medicine 97
6.4.4 Disease Models and Upcoming Biochip Technologies in Drug Screening 97
6.5 Interpretation and Analysis of Data in HTS Technology 98
6.6 Associated Challenges in Biochip System 98
6.6.1 Scale-Up of Large-Scale Drug Screening 98
6.6.2 Regulatory and Ethical Issues 98
6.7 Future Prospects and Conclusion 98
References 99
7 Fundamentals of IoT and Advancements: Architectures and Protocols 105
Brijendra Pratap Singh, Vimal Kumar, Rajnish Chaturvedi, Sandeep Mishra, Vijay Dwivedi, Naveen Kumar, and Dibya Ranjan Das Adhikary
7.1 Introduction 105
7.2 Sensor Technology 107
7.3 Biochip and AIoT Devices 109
7.4 Architecture and Protocols 110
7.5 AIoT Security 113
7.6 Biochips and IoT in Healthcare 114
7.7 Challenges 116
7.8 Conclusion and Future Directions 117
References 117
8 SDN Basic and Architecture 119
Amit Kumar Singh and Mayank Pandey
8.1 Introduction 119
8.1.1 Need for Programmable Networks 120
8.1.2 Limitations of Traditional Networks in Healthcare 121
8.2 SDN Architecture and Its Components 121
8.2.1 Data Plane 122
8.2.2 Control Plane 122
8.2.3 Application Plane 123
8.2.4 OpenFlow SDN Switches 123
8.2.5 OpenFlow 123
8.2.6 SDN Controller 124
8.3 Data Plane Programmability: Overcoming OpenFlow Challenges in SDN 125
8.3.1 P4 Language 126
8.3.2 P4 Compiler 126
8.3.3 Behavioral Model (BMv2) 126
8.3.4 P4 Ecosystem 127
8.3.5 Technological Considerations of Programmable Data Plane 127
8.3.6 Other Data Plane Programming Solutions 128
8.4 SDN in Healthcare: A Deeper Dive into Applications 128
8.4.1 Real-Time Patient Monitoring 129
8.4.2 Telemedicine Optimization 130
8.4.3 Secure Medical Data Transfer 130
8.4.4 Disaster Recovery and Failover 130
8.5 Why Healthcare Needs SDN 131
8.5.1 Challenges and Limitations for Introducing SDN in Healthcare 132
8.5.2 Future of SDN in Healthcare 132
8.6 Conclusion 133
References 133
9 Integration of Medical Devices with IoT for Remote Patient Monitoring 135
Shivani Gupta, Abhishek Tiwari, Amod Kumar Tiwari, and Anurag Sewak
9.1 Introduction 135
9.1.1 Overview of Internet of Medical Things 135
9.1.2 Applications of IoMT in Healthcare 136
9.1.2.1 Remote Patient Monitoring 136
9.1.2.2 Smart Medical Devices 136
9.1.2.3 Telemedicine and Virtual Health 136
9.1.2.4 Smart Hospitals 136
9.1.2.5 AI-Driven Diagnostics 136
9.1.3 Benefits of IoMT 136
9.1.4 Communication Protocol 136
9.2 Integration of Sensors and Devices with RPM 138
9.2.1 Wearable Devices 138
9.2.2 Sensor Integration in Wearable Technology 140
9.3 Digital Advancement in Healthcare 141
9.3.1 Patient Care Impact 141
9.3.2 Health Data Utilization 141
9.3.3 Operational Efficiency 142
9.3.4 Remote Healthcare Monitoring 142
9.3.5 Chatbot-Driven Virtual Health Assistant 142
9.4 Challenges and Future Direction of Smart Healthcare System 142
9.4.1 Body Movement Affecting Sensor Accuracy 142
9.4.2 Temperature Changes Affecting Sensor Performance 143
9.4.3 Limited Range of Transmission 143
9.4.4 QoS in IoMT Networks 144
9.5 Conclusion and Future Scope 144
References 144
10 IoT-Enabled Healthcare Systems: Design, Implementation, and Challenges 147
Shrish Bajpai, Divya Sharma, and Amit Kumar Pandey
10.1 Introduction 147
10.2 Architecture of Healthcare IoT 150
10.3 Implementation of IoT in Healthcare 151
10.3.1 Identify Use Cases and Requirements 152
10.3.2 Select Appropriate IoT Devices and Technologies 152
10.3.3 Ensure Interoperability and Integration 153
10.3.4 Address Data Security and Privacy 154
10.3.5 Establish Data Management and Analytics Capabilities 154
10.3.6 Plan for Change Management and Staff Training 155
10.4 Challenges of IoT in Healthcare 155
10.4.1 Security in IoTs 156
10.4.2 Data Handling and Resource Management of Healthcare IoTs 157
10.4.3 Interoperability 158
10.4.4 Stakeholder Collaboration and Implementation 158
10.5 Conclusion 159
References 160
11 SDN-Enabled Healthcare Networks: Enhancing Connectivity and Security 167
Nitin Shukla, Shabir Ali, Neeraj Jain, Ram Kishan Dewangan, and Akhilesh Kumar
11.1 Introduction 167
11.1.1 Overview of Healthcare Network Requirements 167
11.1.2 Introduction to Current Healthcare Technology Trends 167
11.1.3 Importance of Reliable Connectivity and Robust Security in Healthcare 168
11.1.4 Introduction to Software-Defined Networking and Its Relevance in Healthcare 168
11.2 Background Study 169
11.2.1 Definition and Principles of SDN 169
11.2.2 Key Components of SDN 169
11.2.3 Operational Advantages of SDN 170
11.3 Traditional Healthcare Network Infrastructure: Issues and Limitations 171
11.3.1 Overview of Traditional Network Architectures 171
11.3.2 Difficulty in Managing Dynamic Healthcare Demands 171
11.4 Motivation for Adopting SDN in Healthcare Networks 172
11.4.1 Dynamic and Scalable Network Management 173
11.4.2 Enhanced Network Security Capabilities 173
11.4.3 Efficient Data Management and Handling 174
11.4.4 Real-World Deployment Cases 174
11.4.5 Future-Proofing Healthcare Networks 174
11.5 SDN in Healthcare: Architecture and Implementation 175
11.5.1 Healthcare Use Cases Empowered by SDN 176
11.5.2 Telemedicine Expansion 176
11.5.3 Real Healthcare Deployments: Evidence from the Field 177
11.6 SDN-Enhanced Security in Healthcare Networks 177
11.6.1 Tailored Security for Heterogeneous Healthcare Networks 178
11.6.2 Lightweight Cryptography and Data Privacy 179
11.6.3 Cyberattack Detection and Resilience 179
11.6.4 Adaptive Response and Self-Healing Networks 179
11.6.5 Secure Interoperability and Edge Trust 180
11.7 Integration of SDN with Emerging Technologies in Healthcare 180
11.7.1 SDN with AI: Making Networks Smarter and Safer 181
11.7.2 SDN with Blockchain: Building Trust in Data Access 181
11.7.3 SDN with Fog and Edge Computing: Reducing Delay in Healthcare 181
11.7.4 Full Integration: Combining AI, Blockchain, Fog, and SDN 182
11.8 Future Challenges and Research Directions 182
11.8.1 Integration with Existing Systems 182
11.8.2 Data Privacy and Legal Rules 183
11.8.3 Performance, Reliability, and Energy Use 183
11.8.4 Building Trust and Usability 183
11.8.5 Future Research Opportunities 183
11.9 Conclusion 184
References 184
12 Applications of SDN in Healthcare and Drug Delivery Systems 187
Ankit Faldu, Ashish Patel, Atul Patel, Anjali Mahavar, Unnati Patel, Jay Nanavati, and Bhargav Vyas
12.1 Introduction 187
12.1.1 Overview of SDN 187
12.1.2 Importance of SDN 188
12.1.3 Benefits 188
12.2 Role of SDN in Healthcare 188
12.2.1 AI-Driven Network Management 188
12.2.2 Real-Time Data Analytics and IoT-Enabled Patient Monitoring 189
12.2.3 Security Enhancement in SDN for Healthcare 189
12.3 SDN in Pharmaceutical Supply Chain Optimization 189
12.3.1 Blockchain Supply Chain Management 189
12.4 SDN for Telemedicine and Remote Surgical Applications 190
12.4.1 Predictive Models for Network Congestion in Hospitals 191
12.4.2 Adaptive Traffic Rerouting for Uninterrupted Telemedicine Services 191
12.4.3 AI-Optimized SDN for Latency-Sensitive Remote Surgeries 191
12.5 Cybersecurity in SDN-Enabled Healthcare Networks 192
12.5.1 AI-Driven Threat Intelligence 192
12.5.2 Compliance with Legal and Regulatory Standards 193
12.6 Challenges in SDN Deployment for Healthcare 193
12.6.1 Controller Bottlenecks and Interoperability with Legacy Systems 194
12.6.2 Quantum-Resilient Encryption for Securing Sensitive Medical Data 194
12.6.3 Resource Constraints in Large-Scale SDN Healthcare Deployments 195
12.7 Future Prospects and Innovations in SDN for Healthcare 195
12.7.1 Integration with 6G Networks and Neuromorphic Computing 195
12.7.2 Autonomous Healthcare Network Management 196
12.8 Conclusion 196
References 197
13 Enhancing Security and Privacy of Bioinformatics Using IoT with Hardware Implementation of Midori128 Cipher 199
Pulkit Singh, K Abhimanyu Kumar Patro, Pallavi Joshi, Shipra Upadhyay, and B Sridhar
13.1 Introduction 199
13.2 Related Work 201
13.3 Motivation and Proposed Work 203
13.4 Algorithm Overview 203
13.4.1 Subcell 203
13.4.2 Shuffle Cell 204
13.4.3 mix Column 204
13.4.4 Key Addition 204
13.5 Proposed Methodology: Hardware Implementation 204
13.6 Experimental Results and Discussions 206
13.7 Conclusion 207
References 207
14 Emerging Trends in Healthcare Technology: The Role of AI, Big Data, Blockchain, Cloud Computing, and Beyond 211
Anjana Rani and Monika Saxena
14.1 Introduction 211
14.2 Need for Secure and Scalable Healthcare Systems 212
14.2.1 Challenges of Traditional Healthcare Systems 212
14.2.2 Role of Emerging Technologies in Addressing These Challenges 212
14.3 Role of Cloud Computing, AI, and Big Data in Healthcare 213
14.3.1 Cloud Computing in Healthcare 213
14.3.2 AI in Healthcare 214
14.3.3 Big Data in Healthcare 214
14.4 Blockchain Technology in Healthcare 214
14.4.1 Consensus Algorithms and Their Limitations 215
14.4.2 Advantages of Blockchain Integration in Healthcare 215
14.5 Proposed Framework for IoMT 216
14.5.1 Methodology 216
14.5.2 Proposed Hybrid Consensus Model 216
14.5.3 Proposed Hybrid Cryptographic Approach 217
14.6 Performance Evaluation and Result 217
14.7 Conclusion and Future Scope 219
References 220
Index 223
Preface
Biochip technology, Internet of Things (IoT), and Software-Defined Networks (SDN) have the potential to revolutionize healthcare by making medical systems smarter, more personalized, and efficient. Biochips can detect and monitor health conditions, while IoT allows for the continuous transmission of this data in real-time, enabling healthcare providers to make faster and more informed decisions. SDN, on the other hand, helps manage and optimize the networks needed to handle this data, ensuring smooth and secure communication across devices and systems. However, despite their potential, the healthcare industry faces significant challenges in adopting these technologies. The first major issue is data security as healthcare data is highly sensitive, and the large volumes of information generated by biochips need to be transmitted and stored securely to prevent breaches. The amount of data collected through biochips and IoT devices can be overwhelming, making data management and storage as a critical challenge. Additionally, managing the large flow of data in healthcare effectively requires scalable SDN networks, but integrating these solutions into existing systems poses significant challenges. Hence, there is a need for a guide that explains how to design biochips, use IoT for real-time health data, and apply SDN to manage networks in healthcare.
This book, titled Biochip Design and Health Informatics Using IoT and SDN, provides a deep dive into the amalgamation of the biochip technology, IoT, and SDN for healthcare informatics involving advanced drug discovery and development. In the rapidly evolving landscape of precision medicine, biochips play a crucial role in high-throughput screening, biosensing, and real-time data acquisition for pharmaceutical research. The use of IoT and SDN technologies helps in accelerating drug screening, pharmacokinetic modeling, and personalized medicine applications. These technologies enable real-time monitoring and remote management of patients, making healthcare more accessible and efficient. The design of biochips is based on lab-on-a-chip technology that depends on VLSI for real-time analysis and diagnostics. VLSI technology plays a crucial role in the miniaturization and enhancement of medical devices, enabling the development of low-power, high-performance chips that can be integrated into wearable and implantable devices for continuous health monitoring.
To support seamless communication between the medical devices of health infrastructure and wearable sensory units, IoT as a connectivity framework facilitates the creation of a smart healthcare network that ensures secure and real-time data transmission. Deployment of IoT and allied technologies helps in efficient and remote monitoring of patients in real-time. The network traffic between IoT-connected devices carries carry sensitive real-time patient data that requires efficient and secure handling. SDN offers a flexible and scalable solution for managing network traffic, optimizing communication pathways, and ensuring efficient data transmission within biochip-based ecosystems. Integration of VLSI-based biochip technology, IoT, and SDN addresses critical challenges such as data security, network architecture design, privacy concerns, and resource optimization in drug discovery workflows.
This book showcases a comprehensive study and exploration of emerging interdisciplinary technologies that are capable of revolutionizing modern healthcare system. The early chapters start with the fundamental device-level integration and innovation that emphasize the role of advanced scalable MOS transistors and VLSI in miniaturization and performance enhancement of medical devices. Chapter 1 introduces the reader to the critical need for energy-efficient biosensor design, leveraging tunnel field-effect transistors (TFETs) for ultra-low-power operation. The chapter presents a comparative analysis of multiple TFET architectures designed for biosensing and also suggests possibilities to deploy TFET-based biosensors in medical diagnostics and health management systems. Chapter 2 discusses the integration of biochips and lab-on-a-chip systems to offer insights into the convergence of VLSI with biomedical diagnostics. It emphasizes the role of MEMS and nanotechnology for improved sensor sensitivity, the convergence of VLSI with microfluidics for real-time data acquisition, and usage of AI for intelligent medical diagnostics. The next chapter explores advanced device structures designed to improve the efficiency and sensitivity of medical devices. Chapter 3 particularly discusses the design, fabrication, and uses of negative capacitance junctionless nanowires (NCJLNW) biosensors that are highly sensitive and are suitable for wearable health monitoring systems and point-of-care diagnostics. This is followed by a discussion of real-world applications of these devices in Chapter 4, where an extensive study of wearable and implantable systems for real-time health monitoring is presented. Additionally, Moving forward from device-level to high-level bioinformatics applications, the book explores the role of biochips in accelerating pharmaceutical research. Chapters 5 and 6 emphasize the amalgamation of biochips with microfluidics that are capable of biosensing applications and support rapid compound screening and biomolecular analysis. These chapters demonstrate the impact of biochips in diminishing drug development time and cost along with improved accuracy and predictive reliability. The advancements in multiplexed biochips and real-time monitoring guarantee refined precision medicine and streamlined pharmaceutical workflows.
The next segment of the book addresses the role of IoT and SDN in the healthcare setup for real-time patient monitoring, medical diagnostics, and smart drug delivery. Chapter 7 focuses on the fundamental concepts of IoT, its architecture, and protocols that serve as a conceptual framework for the design, development, and usage of IoT-enabled medical devices. It also throws light on advancements in architecture and protocols of IoT devices related to AI and security. Chapter 8 provides an introduction to basic concepts of SDN including its principles, components, and advantages over traditional networking. The chapter explores the architectural framework of SDN with emphasis on control and data plane separation, centralized network management, and programmability. The fundamentals of IoT and SDN lay down the foundation for understanding practical implications of these technologies in the healthcare scenario, where IoT sensors, edge computing, and SDN-based centralized control facilitate real-time monitoring of patients along with early diagnosis and post-hospitalization care. Chapter 9 explores how IoT is revolutionizing healthcare by enabling continuous and real-time patient monitoring beyond traditional clinical settings with the use of Internet of Medical Things (IoMT). The chapter discusses the integration of medical devices with the sensing and communication infrastructure of IoT for continuous real-time patient monitoring, personalized treatment, and improved clinical decision-making. Further, Chapter 10 discusses the design schemes, implementation methodologies, and several critical challenges to establish IoT-enabled healthcare systems. The chapter presents the current state-of-the-art of IoT-based systems with emphasis on the architecture, technologies, applications, problems, opportunities, open-source platforms, and operating systems. The next two chapters focus on the role of SDN in providing more agile, secure, and efficient healthcare services. Chapter 11 describes the basic principles and operational advantages of SDN in the healthcare setup. It also highlights the problems of the traditional healthcare network infrastructures and their scalable solutions using the features of centralized control, programmability, and flexibility provided by decoupling of data and control planes in SDN-based networks. Additionally, Chapter 12 throws light on the advanced applications of SDN in healthcare and drug delivery systems, especially blockchain-secured pharmaceutical supply chains, AI-driven network management, and real-time data analytics. The chapter also investigates the possibility of integrating SDN with multi-access edge computing (MEC) to enable improved real-time monitoring of patients by the use of IoT-enabled medical devices. Further, Chapter 13 proposes a lightweight cryptographic block cipher to be implemented in the hardware of IoT devices, specifically tailored for resource-constrained bioinformatics systems. The chapter introduces an S-box that uses simple logic gates to offer a hardware architecture for a lightweight Midori cipher that can be used to enhance the security and privacy of patient data.
In the final two chapters of the book, recent and emerging trends in healthcare technology have been discussed, and some case studies showcasing real-life applications of the technologies have also been included. Finally Chapter 14 provides a forward-looking perspective on how emerging technologies like cloud computing, blockchain, big data, and AI improve the healthcare system by addressing important aspects like scalability, interoperability, data integrity, security, transparency, system efficiency, and smarter decision-making. The chapter highlights how advanced data analytics, decentralized systems, and scalable cloud platforms can be integrated to build predictive, resilient, and patient-centric modern healthcare systems.
Altogether, this book makes an effort to converge diverse but interdisciplinary domains, like semiconductor technology, IoT devices and...
