Overview on Data Handling, Task Management, and Communication Optimization Strategies in Internet of Medical Things

The Internet of Medical Things (IoMT) has transformed healthcare through real-time data collection, both remote and in-hospital, enabling more efficient diagnosis and treatment. However, existing IoMT systems face challenges in efficient data handling, reliable task scheduling, and secure communication optimization, which limit their scalability and clinical applicability. This paper addresses this gap by reviewing recent studies and evaluating methods across three critical layers of IoMT architectures: data handling, task management, and communication. The contributions of this paper are threefold: (1) the paper provides a comparative analysis of current data acquisition, aggregation, and prioritization strategies, identifying their limitations in real-time medical applications; (2) the paper evaluates task scheduling algorithms, highlighting how edge, fog, and cloud computing can be leveraged for improved resource allocation and latency reduction; and (3) the paper examines communication optimization approaches, including routing protocols, highlighting on their role in ensuring secure, uninterrupted data transmission. The review provides a comparative analysis between the existing methods in each section, highlighting strengths and limitations, and proposing future directions to further optimize IoMT architectures for scalability and improved performance.