This paper's strength lies not just in applying cloud computing to citizen science, but in its detailed description of overcoming the strict and now somewhat anachronistic constraints of Google App Engine (circa 2011) to aggregate noisy, bursty, and potentially out-of-order discrete events ("picks" from sensors) from a large, decentralized, and unreliable network.

The methods developed to handle limited request times, asynchronous execution with minimal shared state (Memcache, Datastore Entity Groups), challenging geospatial querying (Numeric Geocells), and inherent system errors/downtime (explicitly tolerating data loss and reordering) offer a unique case study in designing anomaly detection systems under extreme resource and reliability limitations.

A promising, unconventional research direction could leverage these constraint-driven design patterns for building anomaly detection systems in highly decentralized, low-resource, or fundamentally unreliable contexts where traditional robust distributed systems approaches are infeasible or too costly.

Modern serverless architectures and cheaper edge processing could be used not to simply implement the system with more power, but to simulate and evaluate the fundamental resilience and efficiency of the paper's minimalist, constraint-optimized algorithms (geocells for spatial bucketing, temporal bucketing of discrete events, explicit error tolerance) across various noise distributions, network topologies (including intermittent connectivity), and event types.