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Earth observation satellites, in low Earth orbits, are increasingly approaching near-continuous imaging of the Earth. Today, these satellites capture an image of every part of Earth every few hours. However, the networking capabilities haven’t caught up, and can introduce delays of few hours to days in getting these images to Earth. While this delay is acceptable for delay-tolerant applications like land cover maps, crop type identification, etc., it is unacceptable for latency-sensitive applications like forest fire detection or disaster monitoring. We design Serval to enable near-realtime insights from Earth imagery for latency-sensitive applications despite the networking bottlenecks by leveraging the emerging computational capabilities on the satellites and ground stations. The key challenge for our work stems from the limited computational capabilities and power resources available on a satellite. We solve this challenge by leveraging predictability in satellite orbits to bifurcate computation across satellites and ground stations. We evaluate Serval using trace-driven simulations and hardware emulations on a dataset comprising ten million images captured using the Planet Dove constellation comprising nearly 200 satellites. Serval reduces end-to-end latency for high priority queries from 71.71 hours (incurred by state of the art) to 2 minutes, and 90-th percentile from 149 hours to 47 minutes. 

Classical leader election protocols typically assume complete and correct knowledge of underlying membership lists at all participating nodes. Yet many edge and IoT settings are dynamic, with nodes joining, leaving, and failing continuously—a phenomenon called churn. This implies that in any membership protocol, a given node’s membership list may have entries that are missing (e.g., false positive detections, or newly joined nodes whose information has not spread yet) or stale (e.g., failed nodes that are undetected)—these would render classical election protocols incorrect. We present a family of four leader election protocols that are churn-tolerant (or c-tolerant). The key ideas are to: i) involve the minimum number of nodes necessary to achieve safety; ii) use optimism so that decisions are made faster when churn is low; iii) incorporate a preference for electing healthier nodes as leaders. We prove the correctness and safety of our c-tolerant protocols and show their message complexity is optimal. We present experimental results from both a trace- driven simulation as well as our implementation atop Raspberry Pi devices, including a comparison against Zookeeper.