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

2025-01-25

AI Summary

To address link instability, high packet loss rates, and highly dynamic signal strength in low-power sensor networks operating under harsh conditions—such as high temperature, rainfall, and mechanical vibration—this paper proposes a lightweight *n*-step received signal strength indicator (RSSI) predictor to enable adaptive transmission power control (ATPC) without real-time ACK feedback. This work presents the first low-overhead, robust time-series RSSI prediction method specifically designed for high-loss environments. Integrated with embedded edge optimizations tailored for CC2538 and CC1200 RF chips, it enables closed-loop power regulation on resource-constrained devices. Experimental evaluation demonstrates average RSSI prediction accuracies of 90% on CC2538 (250 kbps) and 85% on CC1200 (50 kbps), significantly reducing retransmissions and node energy consumption.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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).

Problem

• Wireless Signal Instability
• Sensor Network Communication
• Power Adjustment

Innovation

• Wireless Signal Prediction
• Adaptive Transmission Power Control
• Low-power Radio Devices