The Internet of Medical Things (IoMT) mobile devices such as ambulance, medical done, and emergency mobile medical equipment face severe signal distortions due to interference, end-to-end packet loss, handoff delays, and lower throughputs during mobility. Network mobility basic support protocol (NBSP) has been proposed using the IP-based Wi-Fi solution to solve these issues. However, the weak signal, extra signaling overhead, and higherdelays were identified during handover due to patients' excessive requisites, resulting in radio link failure. Therefore, this article proposes a novel resource-efficient flow-enabled distributed mobility anchoring (FDMA) framework enhancing the functionalities of the centralized network entities and mobility entities.The performance of the proposed FDMA framework is evaluated and compared with the standard NBSP and proxy NEMO (PNEMO) scheme in terms of the variable number of cell residence time and mobile routers, where the proposed framework outperformed NBSP and PNEMO schemes for IoMT Mobile devices in 5G network.

This research meticulously delves into the dynamic evolution of mobile networks, specifically charting the course from 4G to 6G within the context of smart cities. It carefully illuminates the distinct roles played by 5G and 6G, highlighting their unique mobility characteristics. Motivated by observed challenges in handover scenarios, particularly in urban and high-speed environments like trains, the study addresses the complexities of implementing Distributed Mobility Management during inter-handover shifts, revealing a 38% success rate in Handover Failure (HOF) recovery. A thorough analysis ensues, scrutinizing the message structures of various protocols and their extensions employed to address handover challenges. The paper not only identifies limitations but also proposes innovative protocol schemes to overcome inter-handover link failures and delays. Introducing the novel SRNEMO DMM framework, the paper aims to provide a comprehensive understanding of ongoing research efforts and potential future directions, reshaping the landscape of handover scenarios in high-traffic urban environments.

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.