# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### AI-Based Optimization in Edge Environments  
Edge computing-based healthcare and AAL systems require real-time data processing and fast decision-making to support the general goal of improved quality of life of individuals and more efficient health condition management. Thus, one of the main objectives when building such systems is to minimize the processing overhead of edge devices, preserve power, and improve energy efficiency. Many approaches in the literature focus on efficient resource management of edge nodes and on optimizing edge computing-based eHealth systems functioning by employing different AI algorithms and techniques. [PERSON] et al. (2019) explore innovative AI techniques to implement a cognitive data analysis algorithm based on convolutional neural networks (CNNs) for ECG waveform classification directly on resource-constrained, microcontroller-based computing platforms. This study significantly advances the field by optimizing algorithmic efficiency to operate within the limited power and computational capacities of edge devices. The use of lightweight models allows real-time processing of cardiovascular data, reducing the need for extensive power resources and enabling more widespread deployment in remote health monitoring scenarios. [PERSON] et al. (2019) address the limitations of having real-time ECG monitoring in resource-constrained devices while maintaining high accuracy. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Articles** & **Addressed Issues** & **Security/Privacy/AI Technique/Technology** \\ \hline ([PERSON] et al., 2018) & Blockchain + Attribute-based encryption \\ \hline ([PERSON] et al., 2018) & Blockchain \\ \hline ([PERSON] et al., 2018) & Privacy-aware FL \\ \hline ([PERSON] et al., 2018) & Blockchain + InterPlanetary File System (IPFS) \\ \hline ([PERSON] et al., 2018) & Cryptography \\ \hline ([PERSON] et al., 2018) & Distributed ledger technologies (DLT) + masked authenticated messaging \\ \hline ([PERSON] et al., 2018) & Two-phase encryption + RL \\ \hline ([PERSON] et al., 2018) & Blockchain + DApps \\ \hline ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) & Blockchain + Cryptography \\ \hline ([PERSON] et al., 2018) & Authorization; Real-time data processing; Privacy; Scalability & Differential privacy + six-way authentication \\ \hline ([PERSON] et al., 2018) & Symmetric polynomials + NTRU \\ \hline ([PERSON] et al., 2018) &
\begin{tabular}{c} Blockchain + FL \\ \end{tabular} \\ \hline ([PERSON] et al., 2018) & IoMT Data Management; Scalability; Computational overhead & Blockchain + DL \\ \hline ([PERSON] et al., 2018) & Certificate-based signcryption \\ \hline ([PERSON] et al., 2018) & Blockchain + Smart Agent \\ \hline ([PERSON] et al., 2018) & Distributed data privacy & DISTPAB algorithm + FL \\ \hline ([PERSON] et al., 2018) & 5G technologies + FL \\ \hline ([PERSON] et al., 2018) & Data privacy; Anonymity & F-Classify privacy-preserving model \\ \hline ([PERSON] et al., 2018) & Fully homomorphic encryption \\ \hline ([PERSON] et al., 2018) & DL + supervised ML + IDS technique \\ \hline ([PERSON] et al., 2018) & ML + bio-inspired + IDS techniques \\ \hline ([PERSON] et al., 2018) & Cyber-attack detection in IoMT; Anomaly detection and pattern recognition & FL + blockchain + IDS techniques \\ \hline ([PERSON] et al., 2018) & Transformer + FL + Support Vector Data \\  & Description (SVDD) \\ \hline ([PERSON] et al., 2018) & DL + IDS techniques \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._authors demonstrate how compressive sensing can reduce the amount of data necessary for accurate ECG monitoring, thus reducing energy consumption and hardware requirements on edge devices. Two Greedy-based algorithms (Subspace Pursuit and Orthogonal Matching Pursuit) are used to ensure that the monitoring is still reliable for continuous patient monitoring. [PERSON] et al.