Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity

TL;DR

This paper introduces a correlated activity-aware duty cycling scheme for energy-harvesting IoT networks, significantly reducing energy use and misdetection rates by leveraging spatial-temporal activity correlations.

Contribution

It proposes a novel duty cycling management method based on K-nearest neighbors that incorporates activity correlations and energy harvesting models, improving energy efficiency and detection accuracy.

Findings

Up to 11 times lower misdetection probability.

50% reduction in energy consumption.

Effective in high-density IoT scenarios.

Abstract

This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. The aim is to efficiently manage the duty cycling of the IoTDs, so as to prolong their battery life and reduce instances of low-energy availability. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy. This is done by incorporating spatial and temporal correlations among IoTDs' activity, as well as their energy harvesting capabilities. We also allow the base station to wake up specific IoTDs if more information about an event is needed upon initial detection. Our proposed scheme shows significant improvements in energy savings and performance, with up to 11 times lower misdetection probability and 50\% lower energy consumption for high-density scenarios compared to a random duty cycling benchmark.