# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Edge Offloading and Computational Distribution
\begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Technologies Used** & **Main Contribution** \\ \hline
[80] & SDN, NFV & Enhancing network flexibility \\ \hline
[83] & DL, PNN & Improving latency and resource use in fog computing \\ \hline
[84] & DL, CL & Enhancing real-time decision-making \\ \hline
[82] & FL, Blockchain & Improving latency and data privacy \\ \hline
[85] & FL, UAV & Collective data processing \\ \hline
[86] & Neuromorphic HW, DL & Enhancing accuracy and reducing power consumption \\ \hline
[87] & CNN, LogNNet & Neural Network designed for edge computing, fine-tuned for medical data analysis \\ \hline
[88] & SVM, ANFIS & Facilitate data processing across layers \\ \hline
[89] & MAS & Efficient handling of healthcare-related tasks \\ \hline
[90] & Real-time data processing pipelines & Improved efficiency in remote monitoring \\ \hline
[91] & Simulator Edge/Fog & Improving latency and data privacy \\ \hline
[92] & Wearable-based chemical & Enhanced data processing and analysis \\  & sensing & techniques \\ \hline
[93] & DPSO & Optimizing task distribution \\ \hline
[94] & DAG, S2S, DCP & Collaborative and task placement optimization \\ \hline
[95] & SVM, DT & Reduce network layer overhead \\ \hline
[96] & FL, Personalization techniques & Adapt node to specific needs, enable \\  & & heterogeneity \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._and speed in medical diagnostics. [PERSON] et al. [88] explore the integration of blockchain technology within a fog computing environment to enhance security and privacy in healthcare services. The paper introduces a novel approach to disease prediction and secure data handling by employing a two-part algorithm: rule-based clustering using Support Vector Machine (SVM) for efficiently organizing patient health records, and an Adaptive Neuro Fuzzy Inference System (ANFIS) for disease prediction, focusing on cardiovascular diseases and diabetes. [PERSON] et al. [89] address task allocation and load scheduling in fog-cloud architectures, focusing on improving the responsiveness and energy efficiency of healthcare monitoring systems through efficient computational task distribution. The system is based on a Multi-Agent System (MAS), which contains algorithms for dynamic task allocation and load scheduling, offering real-time capabilities to ECG data manipulation and management. [PERSON] et al. [90] discuss the integration of IoT devices within healthcare processes to improve remote monitoring and diagnostics. IoT devices for continuous heart monitoring, are deployed to the edge, and different AI algorithms facilitate the analysis of heartbeats and arrhythmias detection in real time. [PERSON] et al. [91] present an approach designed to minimize latency and enhance real-time responsiveness in healthcare monitoring systems. By processing data closer to the point of data generation, this architecture significantly reduces the need for data transmission to central servers, thus optimizing operational efficiency and response times in critical medical scenarios. [PERSON] et al. [92] address advancements in data processing and analysis for wearable IoT systems in environmental and healthcare monitoring. Wearable chemical sensing devices and data analysis algorithms can be employed to improve the timeliness and accuracy of health assessments. Adaptive Heuristic Edge-assisted Fog Computing Design (AHE-FCD) is a comprehensive system that addresses the current challenges in the healthcare environment [93]. To ensure that the computational resources are used efficiently, the authors use heuristic algorithms to optimize the offloading and distribution of tasks between edge and fog layers. Discrete Particle Swarm Optimization (DPSO) is used in mobile edge computing environments, optimizing task distribution by prioritizing tasks based on factors such as resource availability and task priority. The proposed architecture facilitates real-time medical analysis, enhances the reliability of healthcare systems, and contributes to better patient-oriented outcomes.