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Data aggregation is a fundamental operation in Internet of Things (IoT) applications, e.g., distributed Internet-based industrial control and computing systems. As IoT devices are increasingly connected to the system via resource-constrained wireless communication links, it is critical to perform communication-efficient data aggregation to answer complex queries (e.g., skyline queries and equality joins) from IoT applications. In this paper, we investigate the problem of constructing an aggregation tree (AT) for complex queries with the minimum communication cost. As complex queries have a dynamic size of intermediate results, existing Steiner tree-based approaches for traditional query operators, e.g., MIN and top- , cannot be directly applied. We first formalize the aggregation gain by jointly considering the data pruning power (the size of data points that can be pruned during the aggregation for complex queries …