Learning-based Scheduling for Information Gathering with QoS Constraints

The problem of scheduling packets from multiple sources over unreliable channels has attracted much attention due to its great practicability in the Internet of things systems. Most previous work focuses on the throughput/energy consumption/operational cost optimization or the setting that the channel information is known a priori. In this paper, we consider a more generic setting to this problem where packets from different sources have different values, and each heterogeneous source has a distinct Quality of Service (QoS) requirement. The information about packet value and channel reliability is unknown in advance, and the controller schedules sources over time to maximize its collected packet values while providing a QoS guarantee for each source. For the stationary case where packet values are independent and identically distributed (i.i.d.), we propose an efficient learning policy based on linear-programming (LP) methodology. Our proof shows that it meets the QoS constraint of each source and only incurs a logarithmic regret. In the special case that the channel reliability is known a priori, our algorithm can further guarantee a bounded regret. Furthermore, in the case of non-stationary packet values, we apply the sliding window technique to our LP-based algorithm and prove that it still guarantees a sublinear regret while meeting each source’s QoS requirement. Finally, we provide numerical simulations to support our theoretical results.