Energy and Age-Aware MAC for Low-Power Massive IoT

TL;DR

This work tackles minimizing the time-averaged Age of Information (AoI) in a large-scale IoT network powered by energy harvesting. It introduces an energy- and age-aware, threshold-based ALOHA-like MAC where each device transmits with probability $p$ only if its energy state and AoI satisfy a joint condition, with $p$ itself being a function of normalized energy (and optionally shaped as constant, linear, or elliptical). The approach couples two discrete-time Markov chains for battery energy and AoI, derives stationary distributions, and optimizes policy parameters to balance fresh updates against energy constraints. Key findings show substantial AoI reductions (up to around 60% relative to prior age-dependent schemes) and good scalability as the device count grows, with the elliptical $p$-function often performing best for smaller networks. The results suggest practical gains for massive IoT deployments where energy availability fluctuates and timely status updates are critical, and point to extensions to fading channels and adaptive energy costs.

Abstract

Efficient multiple access remains a key challenge for emerging Internet of Things (IoT) networks comprising a large set of devices with sporadic activation, thus motivating significant research in the last few years. In this paper, we consider a network wherein IoT sensors capable of energy harvesting (EH) send updates to a central server to monitor the status of the environment or machinery in which they are located. We develop energy-aware ALOHA-like multiple access schemes for such a scenario using the Age of Information (AoI) metric to quantify the freshness of an information packet. The goal is to minimize the average AoI across the entire system while adhering to energy constraints imposed by the EH process. Simulation results show that applying the designed multiple access scheme improves performance from 24% up to 90% compared to previously proposed age-dependent protocols by ensuring low average AoI and achieving scalability while simultaneously complying with the energy constraints considered.

Figures (7)

• Figure 1: Illustration of the system model considered in this work. Each device is equipped with its own battery and is subject to independent energy arrivals. The devices send updates to a central server and wait for acknowledgments.
• Figure 2: Transmission probability options when $D=30$. The optimal values for $c$ are $c=2.0$ in the linear case and $c=1.2$ in the elliptical case.
• Figure 3: Discrete time Markov chain for energy levels.
• Figure 4: Discrete time Markov chain for .
• Figure 5: Comparison between applying no policy and applying the threshold-based policy with $p=1$ as a function of $D$.