Advances, Systems and Applications
Table 1 A literature review of related work
From: Software architecture for pervasive critical health monitoring system using fog computing
S. No | Them of the paper | Observations |
|---|---|---|
[5] | DTLS certificate-based end-user license authentication and smart gateway for mobility | The suggested strategy calls for developing a reliable and effective architecture for DTLS certificate-based end-user license authentication and smart gateway-based mobility support but has latency and power consumption problems. |
[10] | IoT-based healthcare architecture (CAHE) | Having bandwidth problem. |
[11] | message-based one-to-one intelligent M2M system for medical and PHD devices | Device-to-device messaging techniques have delay problems. |
[12] | cloud-based diagnostic framework (CHDF) assisted by IoT | Provides limited resources to the end users. |
[13] | an intelligent health monitoring architecture (IHMA | Delay and latency problems |
[14] | using drones in cloud-based health infrastructure along with remote servers and BANs for medical IoT | Security and sensitivity issues. |
[15] | Patient-based architecture for real-time medical data handling | Based on only two case studies. |
[16] | WBAN-based IoT healthcare system architecture for real-time analysis in terms of authentication, energy, and QoS | Delay, latency, and power consumption problems |
[17] | an all-encompassing architectural design for healthcare systems | Formalized their modeling process and used model-checking tools to validate attributes like dependability and availability |
[18] | the current state of the intelligent healthcare system in a smart environment -a public view | Based on the authors’ analysis of technology development, requirements, design, and modeling, these systems face challenges |
[19] | 6G aware fog federation architecture measuring cost and delay | No security mechanism is included |
[20] | Energy-efficient task offloading (EETO) based on hierarchical fog architecture to manage energy by a tradeoff | Resources allocation and time scheduling problems. |
[21] | an intelligent smart gateway (UTGATE) for IoT-based health monitoring systems using proof of concept | Delay and latency problems |
[22] | deep learning supported the analysis of heart disease using Health Fog | Energy consumption is ignored. |
[23] | Smart Fog Gateway (SFG) model for end-to-end wearable IoT devices | Authentication problems may occur. |
[24] | Healthcare monitoring framework (HMF) based on big data analytics in a cloud environment | Bandwidth and delay problems may occur. |
[25] | an evacuation system (CFaES) based on IoT and cloud-fog using Fuzzy KNN | Cost and time delay problems may occur. |
[26] | Edge of things computation (EoTC) model is used to minimize resource supply and data processing | Performance evaluation using QoS parameters is missing from this framework |
[27] | event-driven IoT architecture for dependable healthcare applications | Problems may occur in the real-time data processing |
[28] | SLA-HBDA model is developed to rank patient data for analysis | It is based on only one parameter and it ignores latency and other crucial QoS factors |
[29] | The IoT-based system is used for classifying streaming depending on the criticality | Factors like Time delay and latency are ignored. |
[17] | a generic algorithm-based energy-efficient model for a multisensory secure IoT system | A Delay problem may occur. |
[30] | traffic type-based architecture for a decision on data handling by fog or cloud | Ignore crucial factors like security and bandwidth. |
[31] | based on a 4-tier design to identify job offloading factors. | Security and delay problems may occur. |