Tools: Edge-to-cloud Swarm Coordination For Coastal Climate Resilience...
My fascination with this problem began not in a clean lab, but on a storm-battered coastline. I was part of a research team deploying simple, solar-powered sensors to monitor erosion. We had a dozen Raspberry Pi units with cameras and environmental sensors, each dutifully collecting data. The problem became apparent after the first major storm: several units were damaged, others had communication dropouts, and the data we did get was a fragmented, incoherent picture. The sensors were dumb endpoints, oblivious to each other and to the larger environmental context. They couldn't adapt their sampling rate as a storm approached, couldn't share processing load when one unit failed, and couldn't collaboratively decide which data was critical enough to prioritize for satellite uplink.
This experience was a profound lesson in the limitations of isolated IoT. It sparked a multi-year exploration into how we could transform a collection of low-power, autonomous devices into a coordinated, intelligent swarm capable of planning for coastal climate resilience. Through studying distributed systems papers, experimenting with federated learning frameworks, and building prototype swarms in simulation and on real hardware, I learned that the solution wasn't just better hardware—it was a radical rethinking of the coordination architecture between the extreme edge and the cloud.
One interesting finding from my experimentation with early prototypes was that a purely decentralized (peer-to-peer only) swarm, while robust, was too slow to converge on complex planning models. Conversely, a purely cloud-centric model (all data uploaded for central processing) drained batteries with constant transmission and failed completely during network outages. The hybrid Edge-to-Cloud Swarm Coordination model emerged as the necessary paradigm.
The architecture follows a hierarchical federated model. The cloud trains a global "resilience planning" model. This model is distilled and deployed to the swarm. The swarm then operates this model in a federated way, with each node learning from its local environment and periodically sharing model updates with neighbors. Critical anomalies or consensus-based predictions are sent upstream to the cloud for global model refinement.
The corresponding C++ code on the edge device handles message routing:
Training neural networks on microcontrollers is challenging. Through studying TinyML papers, I learned to use weight quantization and gradient sparsifica
Source: Dev.to
