A Survey on Stochastic Modeling and Machine Learning Techniques for Optimizing Internet of Medical Things (IoMT) Systems

The Internet of Medical Things (IoMT) has the potential to transform healthcare, but faces challenges like energy consumption, data scheduling, latency, privacy, joint offloading, limited data, and security. To address these issues, novel architectures and training techniques, such as generative adversarial networks and stochastic gradient descent, are needed. Inter-pretative models can aid decision-making, while low-resource environment suitability and ethical considerations are crucial to prevent bias in healthcare. This study emphasizes the role of stochastic modeling in optimizing energy use, enhancing data scheduling, reducing latency, improving privacy mechanisms, and optimizing joint offloading strategies. The survey reviews the current state of IoMT and advocates for advancements through stochastic methods.