Table 2 Comparative analysis of the proposed framework with existing SOTA frameworks.
From: A secured remote patient monitoring framework for IoMT ecosystems
Authors | Year | Dew | Fog | Edge | Cloud | Performance Metrics | Advantages | Limitations | Application Area |
|---|---|---|---|---|---|---|---|---|---|
The proposed scheme | 2024 | Y | Y | N | Y | Response time, energy, bandwidth | Low latency, enhanced security, scalability | Increased orchestration complexity | Health-care IoMT |
Vinu and Diwan22 | 2024 | Y | Y | N | N | Consensus delay, throughput | Decentr-alized, secure via blockchain | High consensus overhead | General IoT |
Zhao et al.23 | 2024 | Y | Y | N | Y | Cache hit ratio, latency reduction | Efficient caching, improved network performance | Limited to caching improvements | Smart cities IoT |
Ghosh and De24 | 2023 | Y | N | Y | Y | Delay reduction (28%), energy savings (12%) | Reduced network delay and power consumption | Reliant on stable D2D communication | Wirele-ss networks |
Karm-akar et al.25 | 2022 | Y | N | Y | Y | Data synchronization efficiency | Effective synchronization using statistical measures | Scalability issues in heterogeneous settings | Internet of Health Things (IoHT) |
Podder et al.26 | 2022 | Y | N | Y | Y | Prediction accuracy, latency | Lightweight CNN, enhanced gradient propagation | Dataset-specific tuning may be needed | Health-care biosignal monitoring |
Afaq and Manoc-ha27 | 2022 | Y | Y | N | Y | Precision, recall, accuracy | Effective prediction using GRU and Naïve Bayes | Limited generalizability across conditions | Health-care diagnostics |
Poonia et al.28 | 2021 | Y | Y | N | Y | Bandwidth usage, scalability | Real-time performance, scalable design | Complex multi-layer coordination | COVID-19 management, Healthcare IoT |
Mukher-jee et al.29 | 2021 | Y | N | Y | Y | Communic-ation delay, success rate | Improved connectivity and caching in UAV networks | Limited to UAV-supported scenarios | Smart cities, UAV-based IoT |
Jung-yeon and Kaddoum30 | 2021 | N | N | N | N | Offloading efficiency, resource utilization | Adaptive resource allocation via DRQN | Not applicable for dew-based scenarios | General fog computing in IoT |
Zhou et al.31 | 2020 | N | Y | N | N | Long-term delay reduction | Adaptive offloading based on task properties | May underperform in volatile environments | Task offloading in IoT |
Fan et al.32 | 2020 | N | Y | N | N | Offloading latency, resource allocation | Effective D2D pairing and offloading | Depends on proximity and D2D availability | Edge computing in IoT |
Yang et al.33 | 2019 | N | Y | N | N | Task completion time | Efficient paired offloading | Scalability may be limited | Fog computing in IoT |
Lan et al.34 | 2019 | N | Y | N | N | Resource allocation efficiency | Fair allocation using statistical measures | Complexity increases with user numbers | Resou-rce management in IoT |
Li et al.35 | 2019 | N | Y | N | N | Energy consumption, offloading delay | Energy efficiency | Overhead due to offloading computation | Energy constr-ained IoT |
Mutlag et al.36 | 2019 | N | Y | N | N | Scalability, real-time performance | Real-time scalability | High resource costs in large systems | E-health services |
Sodhro et al.37 | 2019 | N | N | Y | Y | Service placement efficiency | Geo-distributed intelligence improves service quality | Complex deployment strategy | Health-care IoT networks |
Ray et al.38 | 2019 | Y | N | N | Y | Real-time context analysis | Context-aware decisions at dew level | Limited scalability in extensive networks | Context-aware IoT applications |
Rahm-an et al.39 | 2018 | N | Y | N | Y | Local processing speed, storage efficiency | Provides real-time local processing | Dependent on gateway capacity | Embe-dded IoT applications |
