Introduction: the Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: this article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.Result and Discussion: in consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.Conclusion: in terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience

Ultra-low latency, massive connections, and outstanding energy efficiency are the requisites of sixth-generation (6G) wireless networks. For 6G to reach every corner of the globe, terrestrial ultra-dense networks and NTNs, such as LEO satellites, work hand in hand. Energy efficiency and the dynamics of satellite-terrestrial heterogeneity are, however, typically disregarded by traditional routing algorithms. Introducing an adaptive routing architecture that considers energy for ultra-dense 6G networks that utilise satellite-terrestrial linkages. Using metrics like energy usage, congestion, link quality, and switching costs, SDN, cross-layer optimisation, and traffic prediction powered by machine learning choose the optimal paths on the fly. Energy efficiency, end-to-end delay, and packet delivery ratio all saw significant improvements in simulations across a variety of network configurations. For the communication infrastructures of the future, this demonstrates a sustainable and scalable routing method.