An AoI-oriented Time-Frequency Distributed Access Mechanism in Wireless Sensor Networks with Spectrum Division

2026-08-11

AI Summary

This work addresses the challenge of jointly achieving low communication overhead and high information freshness in large-scale randomly activated wireless sensor networks under spectrum partitioning. To this end, the paper proposes a deterministic time–frequency distributed access mechanism (D-TFDA) oriented toward minimizing the Age of Information (AoI). D-TFDA integrates centralized configuration with distributed execution, leveraging a token-based periodic time–frequency structure to provide conflict-free and predictable transmission opportunities. By uncovering structural properties of token allocation, the authors identify AoI-equivalent token clusters, transforming the optimal allocation problem into a linear program that drastically reduces the search space. A one-dimensional discrete-time Markov chain models the system’s steady-state behavior to analyze the long-term average AoI, enabling the design of a low-complexity auction-inspired heuristic algorithm. Simulations demonstrate that D-TFDA significantly outperforms optimized random-access baselines by reducing average AoI, eliminating collisions, and effectively exploiting heterogeneity in sensor-to-resource reliability.

Abstract

The increasing adoption of spectrum-division techniques enables concurrent uplink transmissions over multiple orthogonal resources, yet low-overhead access design with effective information freshness remains insufficiently studied for large-scale randomly activated sensor networks. In this paper, we apply the age of information (AoI) to measure information freshness and propose an AoI-efficient deterministic time-frequency distributed access (D-TFDA) mechanism. D-TFDA combines centralized configuration and distributed operation through a periodic token-based time-frequency structure, which provides sensors with collision-free and predictable transmission opportunities without considerable run-time overhead. We develop an analytical framework to characterize the long-term average AoI (AAoI) by exploiting the periodicity of the token assignment pattern and modeling the steady local state of each sensor with a one-dimensional discrete-time Markov chain (DTMC). We further reveal structural properties of the token assignment pattern and identify AoI-equivalent token clusters, which substantially reduce the search space of the AAoI-optimal token allocation problem. Based on this structure, we formulate the reduced problem as a linear programming (LP) problem and develop an AAoI-optimal search algorithm, together with an auction-inspired heuristic algorithm of lower complexity. Simulation results validate the proposed AAoI analysis, demonstrate the effectiveness of the token allocation algorithms, and show that D-TFDA achieves substantially lower AAoI than optimized random access baselines by avoiding collisions and exploiting heterogeneous sensor--resource transmission reliability.

Problem

• Age of Information
• Wireless Sensor Networks
• Spectrum Division
• Distributed Access
• Information Freshness

Innovation

• Age of Information
• Time-Frequency Access
• Token-based Scheduling
• Markov Chain Analysis
• Linear Programming Optimization