A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks
Abstract
:1. Introduction
- Implemented an RFR-based channel estimation method utilising Feature-based and Sequence-based strategies, demonstrating enhanced accuracy and efficiency for LP-IoT channel estimation.
- Conducted a comparative analysis against existing research and ANN-based methods, highlighting the substantial improvement in estimation error and training and testing time.
- Validated the lightweightness of developed RFR models over ANN-based models by deploying them on the Raspberry Pi 4 Model B platform.
2. Related Work
3. System Model
4. RFR-Based LP-IoT Channel Estimation Models
4.1. RFR(F) Model
Training and Evaluation
4.2. RFR(S) Model
Training and Evaluation
5. Deployment on LP-IoT Device
6. Results and Discussion
6.1. Estimation
6.2. Comparison
6.3. Complexity Analysis
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Distance (s) (m) | Condition (s) 0 (LoS), 1 (NLoS) | Category (g) 0 (L1), 1 (L2 to L13), 2 (L14 to L40). | RSSI () (dBm) |
---|---|---|---|
3 | 0 | 0 | −67.4 |
3 | 1 | 0 | −65.2 |
3 | 0 | 1 | −63 |
3 | 1 | 1 | −56 |
0.2 | 0 | 2 | −40 |
0.3 | 0 | 2 | −47 |
0.4 | 0 | 2 | −44 |
0.5 | 0 | 2 | −46 |
0.6 | 0 | 2 | −43 |
0.7 | 0 | 2 | −49 |
0.8 | 0 | 2 | −50 |
1.9 | 0 | 2 | −65 |
2 | 0 | 2 | −63 |
Training | Testing | Training Time (s) | Testing Time (s) | ||
---|---|---|---|---|---|
MSE | RMSE | MSE | RMSE | ||
Feature-based RFR(F) Estimation Model | |||||
3.16 | 1.77 | 3.20 | 1.79 | 0.0134 | 0.0010 |
Feature-based ANN(F) Estimation Model [30] | |||||
5.91 | 2.43 | 5.30 | 2.30 | 87.2227 | 0.0040 |
Current Research (TinyDRaGon Model) in [17] | |||||
- | - | 5.15 | 2.27 | 600 | 45 |
Current Research (Random Forest Model) in [24] | |||||
- | - | 38.06 | 6.17 | - | - |
Selected Sequence | Training | Testing | Training Time (s) | Testing Time (s) | ||
---|---|---|---|---|---|---|
MSE | RMSE | MSE | RMSE | |||
Sequence-based RFR Estimation Model | ||||||
[3, 0, 1] | 1.66 | 1.28 | 5.65 | 2.37 | 0.0010 | 0.0007 |
[3, 1, 1] | 0.92 | 0.96 | 1.41 | 1.19 | 0.0008 | 0.0006 |
[2, 0, 2] | 0.40 | 0.63 | 3.83 | 1.95 | 0.0007 | 0.0006 |
[2, 1, 2] | 1.36 | 1.16 | 0.26 | 0.50 | 0.0008 | 0.0007 |
[1, 0, 2] | 0.45 | 0.67 | 0.74 | 0.86 | 0.0008 | 0.0006 |
[1, 1, 2] | 3.11 | 1.76 | 2.90 | 1.70 | 0.0014 | 0.0008 |
[0.5, 0, 2] | 0.43 | 0.66 | 0.79 | 0.89 | 0.0006 | 0.005 |
[0.5, 1, 2] | 0.55 | 0.74 | 0.34 | 0.58 | 0.0010 | 0.0011 |
Sequence-based ANN Estimation Model [30] | ||||||
[3, 0, 1] | 2.94 | 1.71 | 9.40 | 3.06 | 0.7091 | 0.0006 |
[3, 1, 1] | 1.23 | 1.11 | 1.51 | 1.23 | 0.4962 | 0.0005 |
[2, 0, 2] | 0.91 | 0.95 | 4.36 | 2.08 | 0.369 | 0.0009 |
[2, 1, 2] | 2.86 | 1.69 | 0.35 | 0.59 | 0.360 | 0.0010 |
[1, 0, 2] | 0.94 | 0.96 | 1.57 | 1.25 | 0.422 | 0.0005 |
[1, 1, 2] | 10.13 | 3.18 | 2.37 | 1.54 | 0.4804 | 0.0018 |
[0.5, 0, 2] | 0.90 | 0.95 | 1.43 | 1.19 | 0.739 | 0.0005 |
[0.5, 1, 2] | 1.45 | 1.20 | 0.21 | 0.46 | 0.381 | 0.0004 |
Current Research (TinyDRaGon Model) in [17] | ||||||
- | - | - | 10.24 | 3.20 | - | - |
Current Research (Random Forest Model) in [24] | ||||||
- | - | - | 38.06 | 6.17 | - | - |
LP-IoT Deployment Platform-Raspberry Pi Model B | ||||
---|---|---|---|---|
Analysis Phase | Training | Testing | ||
Complexity Parameters | Memory Usage (MBs) | CPU Utilisation (%) | Memory Usage (MBs) | CPU Utilisation (%) |
Feature-based RFR Model | 923 | 2.1 | 925 | 1.27 |
Feature-based ANN Model | 1095.68 | 99.25 | 1095.68 | 7.65 |
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Arif, S.; Khan, M.A.; Rehman, S.u. A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks. Appl. Sci. 2025, 15, 3535. https://doi.org/10.3390/app15073535
Arif S, Khan MA, Rehman Su. A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks. Applied Sciences. 2025; 15(7):3535. https://doi.org/10.3390/app15073535
Chicago/Turabian StyleArif, Samrah, M. Arif Khan, and Sabih ur Rehman. 2025. "A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks" Applied Sciences 15, no. 7: 3535. https://doi.org/10.3390/app15073535
APA StyleArif, S., Khan, M. A., & Rehman, S. u. (2025). A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks. Applied Sciences, 15(7), 3535. https://doi.org/10.3390/app15073535