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

arXiv: https://arxiv.org/abs/2405.06372 · HTML: https://mlsanity.com/paper/2405.06372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 6 of 6)

- [A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning](https://mlsanity.com/paper/2101.11246/) (2021)
- [ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy](https://mlsanity.com/paper/2102.13605/) (2021)
- [Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions](https://mlsanity.com/paper/2306.02444/) (2023)
- [Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message](https://mlsanity.com/paper/2202.00401/) (2022)
- [Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction](https://mlsanity.com/paper/2206.06058/) (2022)
- [Low-Power Wide-Area Networks for Sustainable IoT](https://mlsanity.com/paper/1810.10761/) (2018)

## Abstract (arXiv)

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.