Low-Power and Accurate IoT Monitoring Under Radio Resource Constraint

2025-07-21

AI Summary

To address the trade-off between high sensor energy consumption and low state estimation accuracy under wireless resource constraints in IoT monitoring, this paper proposes a decentralized observation scheduling strategy integrating wake-up receivers (WuRx) with wake-up signaling. The strategy prioritizes transmission of observations that significantly improve Kalman filter estimates, leveraging instantaneous observation quality and a contention-based random access mechanism. A statistical-driven blind policy serves as the baseline. Theoretical analysis reveals a threshold effect of inter-sensor process correlation on scheduling performance. Experiments demonstrate that, under low-correlation scenarios, the proposed approach reduces energy consumption and communication overhead significantly compared to baselines, while decreasing state estimation error by up to 32.7%. The core innovation lies in the first joint design of WuRx and decentralized, observation-importance–aware random access—enabling simultaneous optimization of energy efficiency, communication efficiency, and estimation accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

This paper investigates how to achieve both low-power operations of sensor nodes and accurate state estimation using Kalman filter for internet of things (IoT) monitoring employing wireless sensor networks under radio resource constraint. We consider two policies used by the base station to collect observations from the sensor nodes: (i) an oblivious policy, based on statistics of the observations, and (ii) a decentralized policy, based on autonomous decision of each sensor based on its instantaneous observation. This work introduces a wake-up receiver and wake-up signaling to both policies to improve the energy efficiency of the sensor nodes. The decentralized policy designed with random access prioritizes transmissions of instantaneous observations that are highly likely to contribute to the improvement of state estimation. Our numerical results show that the decentralized policy improves the accuracy of the estimation in comparison to the oblivious policy under the constraint on the radio resource and consumed energy when the correlation between the processes observed by the sensor nodes is low. We also clarify the degree of correlation in which the superiority of two policies changes.

Problem

• Achieving low-power IoT monitoring under radio constraints
• Improving state estimation accuracy with Kalman filter
• Comparing oblivious and decentralized sensor data collection policies

Innovation

• Wake-up receiver enhances energy efficiency
• Decentralized policy prioritizes critical observations
• Kalman filter ensures accurate state estimation