# On the Role of Age of Information in the Internet of Things

arXiv: https://arxiv.org/abs/1812.08286 · HTML: https://mlsanity.com/paper/1812.08286/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks](https://mlsanity.com/paper/2209.09206/) (2022)
- [Value of Information and Timing-aware Scheduling for Federated Learning](https://mlsanity.com/paper/2312.10512/) (2023)
- [Collaborative Optimization of the Age of Information under Partial Observability](https://mlsanity.com/paper/2312.12977/) (2023)
- [Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning](https://mlsanity.com/paper/2502.04399/) (2025)
- [From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey](https://mlsanity.com/paper/2211.11259/) (2022)
- [The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics](https://mlsanity.com/paper/2401.10265/) (2024)
- [GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network](https://mlsanity.com/paper/2409.00036/) (2024)
- [AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2510.16414/) (2025)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning](https://mlsanity.com/paper/2607.27626/) (2026)

## Abstract (arXiv)

In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT). We start by summarizing the concept of AoI and its variants with emphasis on the differences between AoI and other well-known performance metrics in the literature, such as throughput and delay. Building on this, we explore freshness-aware IoT design for a network in which IoT devices sense potentially different physical processes and are supposed to frequently update the status of these processes at a destination node (such as a cellular base station). Inspired by the recent interest, we also assume that these IoT devices are powered by wireless energy transfer by the destination node. For this setting, we investigate the optimal sampling policy that jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices with the goal of minimizing long-term weighted sum-AoI. Using this, we characterize the achievable AoI region. We also compare this AoI-optimal policy with the one that maximizes average throughput (throughput-optimal policy), and demonstrate the impact of system state on their structures. Several promising directions for future research are also presented.