Update or Wait: How to Keep Your Data Fresh

Sun Y., UYSAL BIYIKOĞLU E., Yates R. D., Koksal C. E., Shroff N. B.

IEEE TRANSACTIONS ON INFORMATION THEORY, vol.63, no.11, pp.7492-7508, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 63 Issue: 11
  • Publication Date: 2017
  • Doi Number: 10.1109/tit.2017.2735804
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.7492-7508
  • Keywords: Terms Age-of-information, status information update, constrained semi-Markov decision process, date freshness, MARKOV DECISION-PROCESSES, OPTIMAL POLICIES, PROTECTION
  • Middle East Technical University Affiliated: Yes


In this paper, we study how to optimally manage the freshness of infimmation updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution).