# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Edge Offloading and Computational Distribution
They introduce the Joint Task Offloading and Scheduling (JTOS) framework, composed of task offloading (based on a Fuzzy Multi-Criteria Method (FMCM)) sequencing, and scheduling, which notably reduces processing time and communication delays in dynamic environments. [PERSON] et al. [80] tackle the limitations of 5G networks and propose a dynamic QoS management system using software-defined networking (SDN) to support high-data-rate services and reduce latencies in upcoming 6G networks. The solution was extensively tested in simulated environments reflecting real-life network conditions and managed to dynamically adjust QoS parameters according to needs. [PERSON] et al. [81] explore the application of SDN to improve mobility management in the industrial internet. This paper introduces a dynamic network management scheme that adapts in real time to changing conditions, offering cost-effective solutions that enhance network flexibility and scalability. [PERSON] et al. [82] emphasize the role of federated learning in smart city data management, processing data at the edge to minimize latency and maintain data privacy with blockchain integration. \begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Technologies Used** & **Main Contribution** \\ \hline
[77] & Blockchain & Architecture for secured decentralized system \\ \hline
[78] & Blockchain, NFTs & Ensure decentralized and secure resource allocation \\ \hline
[79] & JTOS & Reducing delays in critical IoT applications \\ \hline
[81] & SDN, NFV & Improved mobility management \\ \hline \end{tabular}
\end{table} 
Table 4: Overview of techniques in edge offloading. Figure 10: An overview of edge offloading flow in the computing continuum. [PERSON] et al. [83] explore resource allocation challenges in edge and fog computing environments, particularly within healthcare applications. They implement the Effective Prediction and Resource Allocation Method (EPRAM), using Deep Reinforcement Learning and Probabilistic Neural Networks (PNNs) to enhance efficiency. Thus, they successfully manage to optimize resource allocation decision-making and predict health-related events from the recorded data. [PERSON] et al. [84] discuss integrating IoT-aware technologies and AI to manage real-time critical healthcare tasks, enhancing response times and data privacy through edge computing. The study shows that by using different AI algorithms such as DL, FL, and Continual Learning (CL) the system not only removes the constant need for cloud computing but also enables environments that need fast data processing and action taking. [PERSON] et al. [85] utilize two-layer federated learning that uses async updates of the local model in a pandemic response network. They employ unmanned aerial vehicles (UAVs) and user devices to collaboratively teach and update the global model using local data, thus enabling optimization in health monitoring and response mechanisms. [PERSON] et al. [86] developed an AI-enabled fall detection system using neuromorphic hardware and algorithms like YOLO and SVM on edge devices, highlighting significant reductions in power consumption and enhanced detection accuracy. Another important component of this approach is using neuromorphic hardware, which is specifically designed to mimic the interactions between synapses, thus speeding up and increasing the efficiency of the ML models. [PERSON] et al. [87] present a custom LogNNet, a neural network architecture optimized for edge computing applications in healthcare. This system leverages convolutional neural networks (CNNs) to efficiently process medical data at the edge, significantly reducing the need for data transmission to centralized systems and enhancing real-time data analysis capabilities. The main contribution of this work is its ability to perform complex computations on resource-constrained devices while maintaining high accuracy