上海科技大学知识管理系统

In this paper, we study a remote monitoring Internet of Things (IoT) network, in which an access point (AP) is deployed to aggregate some specific time-varying environment parameters from multiple groups of IoT devices using over-the-air computation (AirComp). As each group of IoT devices monitors a different type of parameters, the AP schedules a group of IoT devices to transmit their status updates each time. To evaluate the freshness of the aggregated data, we introduce the concept of the age of aggregated information (AoAI). We analyze the average AoAI by modelling the instantaneous AoAI evolution of each group as a Discrete-Time Markov Chain and characterize the tradeoff between the average AoAI and mean-squared error (MSE). We further develop two scheduling policies to ensure fair scheduling and achieve the minimum AoAI, respectively. Simulations validate our theoretical analysis and demonstrate the effectiveness of the proposed scheduling algorithms. © 2022 IEEE.