Telemedicine was introduced on connected IoT based resource networking infrastructure. With technological evolution and artificial intelligence, the framework has expanded the grips beyond connecting remote patients. Augmented Intelligence (AuI) adds value to current telemedicine framework by coordinating with resource management pools of IoT devices and communication channels to provide a reliable enterprise ecosystem of smart cities. In this article, a novel approach for resource recommendation in telemedicine via AuI and IoT is proposed. The framework acquires data from the existing eHealth infrastructure of smart cities and telemedicine environment of IoT to provide an intelligent recommendation based on telemedicine services. The proposed framework is based on smart Enterprise Management System (EMS) for eHealth services. The proposed Augmented Intelligent Telemedicine (AITel) framework postulates the 94.83% accuracy from augmented intelligence assisted telemedicine and would create a reliable ecosystem on resource recommendations to build a resilient healthcare system for remote patients, communities and healthcare infrastructure.

The autonomous connected ambulance (ACA) has been an unprecedented necessity in the demand–supply management sector of the healthcare sector. However, the traditional prototypes designed for such an unmanned vehicle do not match the demands of the advanced communication technologies incorporated in today’s sophisticated distributed networks. As a result, in the current era of edge computing strengthened by many AI-enabled algorithms, there is an urgent need to design a route resource recommendation (R3) protocol for ACA under Edge-AI. Designing such a protocol requires addressing the major challenges to optimize the routes for ACA and, thereby, enhance the services of emergency eHealth centers through a governing telehealth monitoring administrator. Therefore, in this article, a dedicated and novel ACA-R3 protocol is proposed to address the issues of connectivity and resource management in ACA to optimize the routes for ACA. The ACA-R3 protocol abides by the operational standards of both the eHealth protocol and governing protocol. The primary objective of the current research work was to reduce the handover time and simplify the patient-information exchange during the time of demanded emergencies. The proposed ACA-R3 protocol can enhance the collaborative distributive resource management for reliable decision making from the data generated by the GPS tracking unit in ACA and Edge-AI. The experimental results obtained from three different cases of traffic congestion are evaluated, validated, and reported in this article.

Medical devices connected via internet are open to cyber-attacks, hence the privacy preservation of the user and the devices plays an important role. In this paper, Medical Edge-devices privacy enhanced technique based on the federated learning principles is proposed on the distributed coordination of devices and computational system. The technique generates a dedicated token via Centralized Authentication Authority (CAA) for the active users at the local Federated Learning (FL) cum training model. Further the technique introduces dual layer validation of tokens via Authentication Authority Valuation Layer (AAVL) and Centralized Server Firewall Authentication Layer (CSFAL). The purpose of these layers is to optimize the active and inactive tokens, assigning tokens and revoking the inactive tokens from the user groups via distributed federated learning model. The technique is validated on 1500 edge devices of IoT/IoMT on 5 clusters servers of global model. The technique has demonstrated an accuracy of 96% in training and optimizing the IoMT device operations via privacy policies proposed in this paper.

Over the past decade, there has been a significant surge in consumer application services and server connectivity, and this trend is expected to double in 2030. The primary contributors to the increased demand for network resources are devices connected through third-party service providers and mobile operators. Many prominent consumer services rely on a client-server architecture, which can introduce latency delays in the communication channel. Additionally, peer-to-peer (P2P) communication places a substantial load on eHealth servers, leading to service delays. In this research paper, we propose a model for scheduling heterogeneous workloads and recommending resources for eHealth edgecloud connectivity using Federated Split Learning (FSL) model for smart cities. Distributed FSL offers a robust solution for handling both direct and indirect user requests through a distributed mobile core operator stack. This technique empowers eHealth administrators to locally learn optimal policies and make informed decisions by prioritizing resource allocation and scheduling. We demonstrate the effectiveness of this technique through an active simulation server designed for track-driven caching policy and local policy scheduling, ultimately enhancing resource recommendation in eHealth applications. The proposed technique is focused on the development of a heterogeneous workload recommendation system and obtained accuracy of 89.63% over 200 users trails.