AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications
Abstract
:1. Introduction
2. Existing Works
3. Materials and Methods
3.1. Wearable Platform for ECG Collection
3.2. Proposed Fuzzy-Based Sustainable, Interoperable, and Reliable Algorithm
3.2.1. Joint Interoperability and Convergence Platform for CPS Based Connected Healthcare
3.2.2. Adaptive and Decisive Process
3.2.3. Similarity Matrix
4. Experimental Setup
4.1. Results
4.2. Evaluation
4.3. Validity of Results
5. Conclusions and Future Research
- More computational and processing complexity is observed while dealing with large datasets, due to the resource-constrained features of the IoT-based CPS device’s developed testbed.
- While establishing uniform standard for CPS based heterogenous healthcare, it is vital to analyze the interconnection between performance indicators such as convergence, delay, and interoperability, but it consumes more power and hence the charge drain in portable IoT devices.
- The development of the single chip wearable ECG platform for elderly patient monitoring by adopting CPS.
- The proposal of a fuzzy-based sustainable, interoperable, and reliable algorithm (FSIRA) for CPS-based connected healthcare.
- The proposal of the novel cloud-based 6G framework for CPS-based connected healthcare.
- The establishment of the relationship between reliability, PLR, convergence, delay, interoperability, and throughput for the CPS-based smart healthcare platform.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Comparison | Value of aij |
---|---|
A and B have same priority | 1 |
B has a higher priority than A | 3 |
A has higher priority than B | 5 |
A has a higher priority than B | 7 |
A is essential unlike B | 9 |
Correlation among all entities | 2, 4, 6, 8 |
Correlation for compensating loss factor | 1/3, 1/5, 1/7, 1/9 |
Parameter | Specifications |
---|---|
Number of IoT nodes | 10 |
Cell layout | Hexagonal |
Threshold RSSI | −85 dBm |
5 dBm | |
Duty-cycle | 1% |
Carrier frequency | 30 GHz |
6G Bandwidth | 60 GHz |
TP levels | {−6, −5, −4, −3, 2, 1, 0, 1, 2, 3, 4, 5, 6} |
eNodeB Tx power | 45 dBm |
Fading model | Rayleigh |
Channel model | CDL-D |
Operation time (T) | 4 min |
Delay | 1 ms |
Data packet length | 300 bytes |
Data packet interval | 50 s |
Data Rate | 1 Gbps |
Noise figure | 8 dB |
Noise PSD | −175 dBm/Hz |
Traffic model | 2 Mbps (adaptive) |
Processing delay | 0.1 ms |
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Sodhro, A.H.; Zahid, N. AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications. Sensors 2021, 21, 8039. https://doi.org/10.3390/s21238039
Sodhro AH, Zahid N. AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications. Sensors. 2021; 21(23):8039. https://doi.org/10.3390/s21238039
Chicago/Turabian StyleSodhro, Ali Hassan, and Noman Zahid. 2021. "AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications" Sensors 21, no. 23: 8039. https://doi.org/10.3390/s21238039
APA StyleSodhro, A. H., & Zahid, N. (2021). AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications. Sensors, 21(23), 8039. https://doi.org/10.3390/s21238039