# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Edge Offloading and Computational Distribution  
A separate direction in the research literature deals with task distribution across task nodes through different scheduling techniques to achieve optimal edge offloading. More specifically, it refers to the strategic allocation and execution of computational tasks across multiple nodes to improve performance, efficiency, and resource utilization in different healthcare scenarios (see Figure 10). At the same time, the goals to reduce latency, enhance scalability, and optimize the overall computational workload are always targeted by the existing solutions (see Table 4 for different proposed solutions overview). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Healthcare Use Case** & **Optimization Objective** & **Algorithms/Techniques** \\ \hline
[57] & Detection and classification of arrhythmias & Improve the quality of monitored signals by optimizing edge devices operation & CNN + CNN-LSTM + CNN-GRU \\ \hline
[68] & Remote monitoring & Improve edge processing time for the detection algorithms & Transfer learning \\ \hline
[58] & COVID-19 detection & Improve classification accuracy on edge devices & LogNNet \\ \hline
[69] & Human activity recognition & Optimize energy efficiency of smart homes & NILM \\ \hline
[70] & Skin disease diagnosis & Improve energy consumption and response times in distributed edge devices & DL + Tiny AI \\ \hline
[63] & Remote monitoring & Improve feature extraction and reduce computational load at the edge & MobileNetV2 \\ \hline
[72] & Human activity recognition & Optimize the processing of sensor data & Bi-CRNN \\ \hline
[61] & Oral cancer detection & Optimize the image classification task at the edge & CNN + AO + GTO \\ \hline
[73] & Human activity recognition & Optimize distributed data labeling processes & FL + DRL \\ \hline
[62] & Real-time health monitoring & Improve communication in edge networks & Cognitive computing \\ \hline
[64] & Emergency IoMT & Improve edge processing & FL \\ \hline
[59] & COVID-19 and pneumonia diagnostics & Improve communication speed between edge devices & MOMHTS + RF + DL \\ \hline
[60] & Vaccine administration & Improve throughput and scalability of distributed data sharing and processing & Blockchain \\ \hline
[55] & Real-time ECG monitoring & Reduce energy consumption and hardware requirements on edge devices & Greedy \\ \hline
[65] & Long-term care for elders & Optimize resource allocation & DL \\ \hline
[71] & Diagnosis of skin diseases & Improve adaptation of edge resources & DL \\ \hline
[74] & Affective state recognition & Improve precision and response time & Fuzzy C-means \\ \hline
[75] & Remote monitoring & Improve edge computation and processing & FSIRA \\ \hline
[66] & Stroke prediction & Reducing the diagnostic time at the edge & LSTM \\ \hline
[76] & Hospital IoMT-enabled data management & Optimize of workflows across edge nodes & LSEOS \\ \hline
[67] & Elderly fall detection & Reduce and optimize deployment on edge devices & CNN-LSTM with attention layer \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._[PERSON] et al. [77] explore the integration of blockchain technology in designing smart healthcare 4.0 systems. The authors emphasize blockchain's crypto-security, transparency, and decentralized data networks, which can significantly enhance data management and load distribution in edge nodes in healthcare settings. Similarly, [PERSON] et al. [78] delve into the integration of blockchain technology in metaverse applications to manage synchronized data flows and secure resource allocation. By employing smart contracts and blockchain, the authors address the need for enhanced security and decentralized control, ensuring that data integrity and user trust are maintained across various virtual environments. [PERSON] et al. [79] address efficient task offloading and scheduling in IoT systems, very important for delay-sensitive applications like healthcare monitoring.