We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

翻译：我们研究了一种基于拉取的通信系统，其中感知代理通过查询控制策略更新执行代理，该策略根据观测到的信息源演化以及每次更新对实现特定目标的效用进行调整。为此，控制器在每个时隙决定是否拉取更新，预测源端可能发生的情况以及该更新在端点上可能产生的有效影响。因此，源演化中的时间变化能够调整查询到达时机，以捕获重要更新。影响程度由一种称为有效等级（GoE）的度量决定，该度量融合了所传达更新的新鲜度和效用属性。通过应用迭代算法，我们推导出在成本约束下最大化所传达数据包长期平均GoE的查询决策。我们的分析和数值结果表明，在广泛的查询到达率范围内，所提出的查询策略比现有的周期性和概率性查询策略表现出更高的有效性。