Low-Power and Accurate IoT Monitoring Under Radio Resource Constraint

TL;DR

This paper explores energy-efficient IoT sensor network policies that balance low power consumption with accurate state estimation, introducing a decentralized approach with wake-up signaling that outperforms oblivious methods under certain conditions.

Contribution

It proposes a decentralized transmission policy with wake-up signaling for IoT sensors, improving estimation accuracy while conserving radio resources and energy.

Findings

Decentralized policy outperforms oblivious policy at low process correlation.

Wake-up signaling enhances energy efficiency of sensor nodes.

The effectiveness of policies depends on the correlation degree of observed processes.

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.