On Decentralized In-network Aggregation in Real-World Scenarios with Crowd Mobility

Recently proposed applications for monitoring the behavior of real-world crowds with wireless sensor nodes rely on decentralized in-network aggregation. Although some of the aggregation algorithms for wireless sensor networks seem appealing for such applications, we are not aware of any deployments of these algorithms in real-world scenarios with crowd mobility. As a step toward filling this gap, we thus discuss our experiences with decentralized in-network aggregation from a few such deployments involving up to 177 nodes. We compare two main classes of algorithms for basic aggregates. We show that algorithms based on probabilistic, order- and duplicate-insensitive sketches outperform algorithms based on gradual variance reduction. To this end, however, they have to be adapted considerably to minimize the traffic, latency, and errors of the aggregation process, and to account for some real-world issues. In short, while the algorithms do have a potential for the envisioned crowd-monitoring applications, deploying them is not trivial.