We investigate the performance of concurrent remote sensing from independent strategic sources, whose goal is to minimize a linear combination of the freshness of information and the updating cost. In the literature, this is often investigated from a static perspective of setting the update rate of the sources a priori, either in a centralized optimal way or with a distributed game-theoretic approach. However, we argue that truly rational sources would better make such a decision with full awareness of the current age of information, resulting in a more efficient implementation of the updating policies. To this end, we investigate the scenario where sources independently perform a stateful optimization of their objective. Their strategic character leads to the formalization of this problem as a Markov game, for which we find the resulting Nash equilibrium. This can be translated into practical smooth threshold policies for their update. The results are eventually tested in a sample scenario, comparing a centralized optimal approach with two distributed approaches with different objectives for the players.

翻译：我们研究了独立战略源并发遥感的性能，其目标是最小化信息新鲜度与更新成本的线性组合。现有文献通常从静态视角研究该问题，即预先设定源的更新速率（采用集中式最优方法或分布式博弈论方法）。然而，我们认为真正理性的源应当在充分了解当前信息年龄的基础上做出决策，从而实现更高效的更新策略实施。为此，我们研究了源独立执行其目标的状态优化场景。其战略特性促使该问题被形式化为马尔可夫博弈，并由此求得纳什均衡。这一均衡可转化为实用的平滑阈值更新策略。最终通过示例场景进行测试，将集中式最优方法与两种具有不同玩家目标的分布式方法进行了比较。