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The lifetime of an Internet of Things (IoT) system consisting of battery-powered devices can be increased by minimizing the number of transmissions per device while not excessively deteriorating the correctness of the overall IoT monitoring. We propose a graph signal processing based algorithm for partitioning the sensor nodes into disjoint sampling sets. The sets can be sampled on a roundrobin basis and each one contains enough information to reconstruct the entire signal within an acceptable error bound. Simulations on different models of graphs, based on graph theory and on real-world applications, show that our proposal consistently outperforms state-ofthe-art sampling schemes, with no additional computational burden.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes.However, its definition tacitly assumes that new information is used at any time, which is not always the case: the instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query: if the monitoring process is not using the value, the age of the last update is irrelevant. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC) and with limited link availability. Our results show that QAoI-aware optimization can significantly reduce the average and worst-case perceived age for both periodic and stochastic queries.