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Prediction of Received Power in Low-Power and Lossy Networks Deployed in Rough Environments

Waltenegus Dargie

TL;DR

This work addresses reliable adaptive transmission in low-power IoT networks deployed in rough environments by introducing a lightweight $n$-step predictor for received power. The predictor uses minimum mean square estimation combined with function approximation and Gram-Schmidt orthonormalization to avoid matrix inversion, enabling robust forecasting even with successive packet losses. Empirical deployments on four water bodies show average prediction accuracy exceeding $90\%$ for the CC2538 at 250 kbps and about $85\%$ for the CC1200 at 50 kbps, illustrating practical viability with limited computational resources. The approach offers a topology-agnostic alternative to heavier MMSE or Kalman-based methods, with clear implications for improving energy efficiency in harsh, lossy wireless sensor networks.

Abstract

Cost-efficient and low-power sensing nodes enable to monitor various physical environments. Some of these impose extreme operating conditions, subjecting the nodes to excessive heat or rainfall or motion. Rough operating conditions affect the stability of the wireless links the nodes establish and cause a significant amount of packet loss. Adaptive transmission power control (ATPC) enables nodes to adapt to extreme conditions and maintain stable wireless links and often rely on knowledge of the received power as a closed-feedback system to adjust the power of outgoing packets. However, in the presence of a significant packet loss, this knowledge may not reflect the current state of the receiver. In this paper we propose a lightweight n-step predictor which enables transmitters to adapt transmission power in the presence of lost packets. Through extensive practical deployments and testing we demonstrate that the predictor avoids expensive computation and still achieves an average prediction accuracy exceeding 90% with a low-power radio that supports a transmission rate of 250 kbps (CC2538) and 85\% with a low-power radio that supports 50 kbps (CC1200).

Prediction of Received Power in Low-Power and Lossy Networks Deployed in Rough Environments

TL;DR

This work addresses reliable adaptive transmission in low-power IoT networks deployed in rough environments by introducing a lightweight -step predictor for received power. The predictor uses minimum mean square estimation combined with function approximation and Gram-Schmidt orthonormalization to avoid matrix inversion, enabling robust forecasting even with successive packet losses. Empirical deployments on four water bodies show average prediction accuracy exceeding for the CC2538 at 250 kbps and about for the CC1200 at 50 kbps, illustrating practical viability with limited computational resources. The approach offers a topology-agnostic alternative to heavier MMSE or Kalman-based methods, with clear implications for improving energy efficiency in harsh, lossy wireless sensor networks.

Abstract

Cost-efficient and low-power sensing nodes enable to monitor various physical environments. Some of these impose extreme operating conditions, subjecting the nodes to excessive heat or rainfall or motion. Rough operating conditions affect the stability of the wireless links the nodes establish and cause a significant amount of packet loss. Adaptive transmission power control (ATPC) enables nodes to adapt to extreme conditions and maintain stable wireless links and often rely on knowledge of the received power as a closed-feedback system to adjust the power of outgoing packets. However, in the presence of a significant packet loss, this knowledge may not reflect the current state of the receiver. In this paper we propose a lightweight n-step predictor which enables transmitters to adapt transmission power in the presence of lost packets. Through extensive practical deployments and testing we demonstrate that the predictor avoids expensive computation and still achieves an average prediction accuracy exceeding 90% with a low-power radio that supports a transmission rate of 250 kbps (CC2538) and 85\% with a low-power radio that supports 50 kbps (CC1200).
Paper Structure (12 sections, 30 equations, 11 figures, 3 tables)

This paper contains 12 sections, 30 equations, 11 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deployment
  4. Background
  5. Model
  6. Application of MS Estimation
  7. Orthonormal Model Parameters
  8. Evaluation
  9. CC2538 System-on-Chip
  10. CC1200 Radio Chip
  11. Comparison
  12. Conclusion

Figures (11)

  • Figure 1: Low-power and waterproof IoT sensing nodes deployed on the surface of different water bodies.
  • Figure 2: Link quality fluctuation due to the motion of water. TOP: CC2538 SoC. BOTTOM: CC1200 Radio.
  • Figure 3: Comparison of two types of received powers. The blue line indicates a strong fluctuation in the absence of an adaptive transmission power. The red line describes a scenario in which the transmission power is adapted to the underlying condition, so that the received power is always above a set threshold.
  • Figure 4: The autocorrelation function of the received power for the different deployment environments. TOP: CC1200. BOTTOM: CC2538.
  • Figure 5: Communication scenario.
  • ...and 6 more figures