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

David E. Ruíz-Guirola, Onel L. A. López, Samuel Montejo-Sánchez, Israel Leyva Mayorga, Zhu Han, Petar Popovski

TL;DR

This work tackles energy-neutral IoT networks where devices harvest ambient energy, modeling each device with a $4$-state Markov chain, EH via a modulated Poisson process, and battery dynamics as a discrete-time Markov chain. It introduces a KNN-based duty-cycling strategy together with WuR-enabled on-demand wake-up to exploit spatio-temporal activity correlations and to manage energy intelligently, aiming to minimize misdetections while keeping energy consumption low. The authors provide two optimization routes (exhaustive and KNN-based), analyze computational complexity, and validate the approach through extensive simulations, demonstrating up to $11\times$ reduction in misdetection probability and around $50\%$ energy savings in high-density networks. The proposed framework offers a scalable mechanism for sustaining large EH-IoT deployments in future 6G-like networks by balancing sensing duty, wake-up signaling, and energy harvesting dynamics.

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.

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

TL;DR

This work tackles energy-neutral IoT networks where devices harvest ambient energy, modeling each device with a -state Markov chain, EH via a modulated Poisson process, and battery dynamics as a discrete-time Markov chain. It introduces a KNN-based duty-cycling strategy together with WuR-enabled on-demand wake-up to exploit spatio-temporal activity correlations and to manage energy intelligently, aiming to minimize misdetections while keeping energy consumption low. The authors provide two optimization routes (exhaustive and KNN-based), analyze computational complexity, and validate the approach through extensive simulations, demonstrating up to reduction in misdetection probability and around energy savings in high-density networks. The proposed framework offers a scalable mechanism for sustaining large EH-IoT deployments in future 6G-like networks by balancing sensing duty, wake-up signaling, and energy harvesting dynamics.

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.
Paper Structure (11 sections, 8 equations, 6 figures, 1 algorithm)

This paper contains 11 sections, 8 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Battery State Evolution
  4. On-demand Wake-up and Transition Probabilities
  5. Optimization Framework
  6. Heuristic & KNN-based Duty Cycling Optimization
  7. Complexity Analysis
  8. Simulations
  9. Benchmarks
  10. Performance Comparison
  11. Conclusions

Figures (6)

  • Figure 1: Illustration of an IoT network where the BS controls and collects information from various IoTDs. The impact of an event on the surrounding IoTDs is modeled through a probability activation function that decays with the distance (meters) from the event epicenter to the IoTDs.
  • Figure 2: Device's operation states modeled as a four states discrete-time Markov chain.
  • Figure 3: Illustration of the battery level evolution by considering the energy consumption and EH models. Transmission (TX) results in the highest energy depletion, while sleep is a low-energy consumption state. Sensing results in a depletion smaller than TX but greater than in a sleep state.
  • Figure 4: Percentage of misdetected events.
  • Figure 5: Mean Energy consumption per device per TTI.
  • ...and 1 more figures