Age-Gain-Dependent Random Access for Event-Driven Periodic Updating

TL;DR

The paper tackles reducing network AoI in event-driven IoT systems with decentralized random access by leveraging age gains to prioritize transmissions. It introduces a basic T-AGDSA with offline-optimizable fixed parameters via a two-layer DTMC model, and an enhanced T-AGDSA that adapts thresholds and transmission probabilities to maximize the estimated network EAR per slot using a Bayes-based posterior over local age and age gains. Numerical results show that both schemes achieve substantial AoI improvements over existing approaches across diverse network configurations, with the enhanced scheme providing larger gains in settings with tighter contention and measurable online computation costs. The work offers practical, implementable strategies for AoI reduction in large-scale, event-driven IoT deployments and sheds light on when to prefer offline-optimized versus online adaptive AGDRA approaches.

Abstract

This paper considers utilizing the knowledge of age gains to reduce the network average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the unavoided contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling approach by a multi-layer discrete-time Markov chains (DTMCs), where an external infinite-horizon DTMC manages the jumps between the beginnings of frames and an internal finite-horizon DTMC manages the evolution during an arbitrary frame. Such modelling enables that optimal access parameters can be obtained offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network \textit{expected AoI reduction} (EAR) per slot, which captures the essential for improving the contribution of the throughput to the AoI performance. To estimate the network EAR, we require each device to use Bayes' rule to keep a posteriori joint probability distribution of local age and age gain of an arbitrary device based on the channel observations. Numerical results validate our theoretical analysis and demonstrate the advantage of the proposed schemes over the existing schemes in a wide range of network configurations.

Figures (9)

• Figure 1: Time Structure.
• Figure 2: An example of $h_{n,t}$, $w_{n,t}$, $g_{n,t}$ and $\Gamma_t$ evolving over time under enhanced T-AGDSA when $D=4$. The black solid arrowhead indicates the arrival of an update, whereas the black hollow arrowhead indicates the successful transmission of the most recently generated update of device $n$ in the corresponding slot.
• Figure 3: Flowchart illustrating the basic idea of analyzing basic T-AGDSA.
• Figure 4: The external DTMC $\bm{X}$.
• Figure 5: The internal absorbing DTMC $\bm{Y}$.