Fresh Data Delivery: Joint Sampling and Routing for Minimizing the Age of Information


ATASAYAR A. U., Li A., Arl Ç., UYSAL E.

26th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2025, Texas, Amerika Birleşik Devletleri, 27 - 30 Ekim 2025, ss.291-300, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1145/3704413.3764413
  • Basıldığı Şehir: Texas
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.291-300
  • Anahtar Kelimeler: age of information, markov decision process, routing policy, sampling policy, threshold policy
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper, we extend the freshness-oriented sampling problem by incorporating controlled delay statistics through heterogeneous routing options, where the Age of Information (AoI) serves as the metric for data freshness. Our objective is to jointly optimize sampling and routing policies to minimize the long-term average AoI, where the sender can choose to forward each status update over one of the available routes, which have distinct delay statistics. This problem is an infinite-horizon Semi-Markov Decision Process (SMDP) with an uncountable state space and a hybrid action space, consisting of discrete routing choices and continuous waiting times. We develop an efficient algorithm to solve this problem and theoretically establish that the optimal policy exhibits a threshold structure, characterized by: (i) a threshold-based monotonic handover mechanism for optimal routing, where the switching order aligns with the decreasing order of mean delays; and (ii) a multi-threshold piecewise linear waiting mechanism for optimal sampling, where the total number of thresholds is upper bounded by 2N - 1, given N selectable routes. We implement the proposed algorithm in a satellite-terrestrial integrated routing scenario, and simulation results reveal an intriguing insight: routes with higher average delay or variance can still contribute to minimizing AoI.