Database queries, in particular, event-driven continuous queries, are useful for many pervasive computing applications, such as video surveillance. In order to enable these applications, we have developed a pervasive query processing framework called Aorta. Unlike traditional database systems, a pervasive query processor requires systems support for managing a large number of networked, heterogeneous devices. In this paper, we present the communication, synchronization, and scheduling mechanisms in Aorta. Even though these techniques have their roots in distributed and parallel systems, we show how these techniques are customized and applied for pervasive query processing. In essence, communication between heterogeneous devices enables network data independence, synchronization on devices protects action atomicity, and scheduling works for adaptive, cost-based multi-query optimization. We have conducted empirical studies on our prototype as well as simulation studies to evaluate the system performance.