On Maximizing Sampling Time of RF-Harvesting Sensor Nodes Over Random Channel Gains

TL;DR

This paper develops a stochastic programming approach to optimize the charging and sampling times of RF-powered sensor nodes under random channel conditions, minimizing energy shortfalls in IoT environments.

Contribution

It introduces a novel stochastic model for maximizing sampling time of RF-harvesting sensor nodes considering random channel gains, with solutions for single and multiple time slots.

Findings

The proposed model effectively minimizes expected energy shortfall penalties.

Numerical experiments validate the approach across Gaussian, Rayleigh, and Rician channel distributions.

The method provides near-optimal charging and sampling times in simulated environments.

Abstract

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a {\em sampling time} to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the {\em expected} penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: {\em single} and {\em multi} time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next $T$ slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.