Wireless sensor networks involve very large numbers of small, low-power, wireless devices. Given their unattended nature, and their potential applications in harsh environments, we need a monitoring infrastructure that indicates system failures and resource depletion. In this paper, we briefly describe an architecture for sensor network monitoring, then focus on one aspect of this architecture: continuously computing aggregates (sum, average, count) of network properties (loss rates, energylevels etc., packet counts). Our contributions are two-fold. First, we propose a novel tree construction algorithm that enables energy-efficient computation of some classes of aggregates. Second, we show through actual implementation and experiments that wireless communication artifacts in even relatively benign environments can significantly impact the computation of these aggregate properties. In some cases, without careful attention to detail, the relative error in the computed aggregates can be as much as 50%. However, by carefully discarding links with heavy packet loss and asymmetry, we can improve accuracy by an order of magnitude.

In this paper we investigate the problem of finding minimum delay application-layer multicast trees, such as the trees constructed in overlay networks. It is accepted that shortest path trees are not a good solution for the problem since such trees can have nodes with very large degree, termed high load nodes. The load on these nodes makes them a bottleneck in the distribution tree, due to computation load and access link bandwidth constrains. Many previous solutions limited the maximal degree of the nodes by introducing arbitrary constraints. In this work, we show how to directly map the node load to the delay penalty at the application host, and create a new model that captures the trade offs between the desire to select shortest path trees and the need to constraint the load on the hosts.