Today, patients are demanding a newer and more sophisticated healthcare system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous healthcare surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for healthcare applications, as well as identify requirements and challenges of devices for various use cases. Edge computing applications primarily focus on the classification of health data involving vital sign monitoring and fall detection. Other low latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud based counterparts, but with lower computational complexity. Future research directions in edge computing for healthcare have been identified to offer a higher quality of life for users if addressed.

Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixthgeneration (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, the state-of-the-art research on different aspects of RIS-assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS-assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS-assisted network are highlighted.

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