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

TL;DR

This paper introduces a lightweight n-step predictor for low-power, lossy networks that accurately forecasts received power, enabling better adaptive transmission control in harsh environments.

Contribution

It proposes a novel lightweight predictor for received power that maintains high accuracy despite packet loss, improving adaptive power control in extreme conditions.

Findings

Achieves over 90% prediction accuracy with high-rate radios

Attains 85% accuracy with lower-rate radios

Demonstrates effectiveness through extensive real-world deployments

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).