Internet of Medical Things Integrating IoT with Healthcare for Remote Monitoring and Diagnosis

¹ Associate Professor, Department of Computer Science and Engineering (Data Science), New Horizon College of Engineering, Outer Ring Rd, Near Marathalli, Kaverappa Layout, Kadubeesanahalli, Bengaluru, Karnataka, India
² Assistant Professor, Department of Information Technology, EASA College of Engineering & Technology, Coimbatore, Tamil Nadu, India
³ Assistant Professor, Department of CSE, CVR College of Engineering, Hyderabad, Telangana, India
⁴ Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
⁵ Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, A-4, Rohtak Road, Paschim Vihar, Delhi, India
⁶ Assistant Professor, Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

suganya.nhce@gmail.com
sasini.karpagam@gmail.com
lavanya.chintamalla89@gmail.com
szrashidcce@yahoo.com
mohit.t.bvcoe@gmail.com
vijiece@newprinceshribhavani.com

Abstract

The Internet of Medical Things (IoMT): It is changing the healthcare sector in various ways by coupling the crucial aspects of IoT to monitor and diagnose patients remotely. Existing literature regarding IoMT applications has identified the high security vulnerabilities, unrealized real-world implementations, poor scalability, and high latency, but there are no proposed solutions to these challenges. It presents a robust Internet of Medical Things (IoMT) architecture which is real-time, secure, scalable, and enables remote health monitoring. By leveraging edge computing, AI, and blockchain-based security, the framework improves data privacy, reduces latency, and increases energy efficiency. In contrast to earlier studies that discuss specific conditions, the current work generalizes IoMT applications for a variety of ailments, enabling personalized healthcare solutions through artificial intelligence (AI)–driven analytics. In addition, the proposed system is designed to be interoperable such that it supports seamless integration across different IoT healthcare devices. Using predictive analytics, this system facilitates early disease detection and preventative healthcare action, fostering better patient outcomes and fewer hospital visits. This study also presents the design of an energy-efficient IoMT network to prolong the lifetime and viability of IoMT devices. In conclusion, this research expands on the future of remote healthcare by providing solutions to the scalability, privacy and real-time decision-making challenges, thereby developing an IoMT system that is robust, future-proof and adaptable to smart healthcare applications.