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DFF Sapere Aude Scale-IoT


The general research question of SCALE-IoT is how can networking and storage systems scale
efficiently when faced with the introduction of tens of billions of Internet of Things (IoT) devices
generating massive amounts of dynamic data (different versions of the content, updated as
disconnected/powered-off devices return to the network). Our hypothesis is that a solution that is
scalable, reliable, compatible with current infrastructure, and with a low-carbon footprint can be
achieved by proposing ground-breaking coding theory concepts to manage dynamic data and reduce
cost of storage by an order of magnitude or more with respect to current systems. More specifically,
SCALE-IoT proposes an unconventional use of error-correcting network codes to allow for a
configurable and progressive data updating process in the network, which allows for the
system to update data when resources are available, e.g., as a consequence of dynamic data from
IoT services.