  Mobile multi-robot teams deployed for monitoring or search-and-rescue
missions in urban disaster areas can greatly improve the quality of vital data
collected on-site. Analysis of such data can identify hazards and save lives.
Unfortunately, such real deployments at scale are cost prohibitive and robot
failures lead to data loss. Moreover, scaled-down deployments do not capture
significant levels of interaction and communication complexity. To tackle this
problem, we propose novel mobility and failure generation frameworks that allow
realistic simulations of mobile robot networks for large scale disaster
scenarios. Furthermore, since data replication techniques can improve the
survivability of data collected during the operation, we propose an adaptive,
scalable data replication technique that achieves high data survivability with
low overhead. Our technique considers the anticipated robot failures and robot
heterogeneity to decide how aggressively to replicate data. In addition, it
considers survivability priorities, with some data requiring more effort to be
saved than others. Using our novel simulation generation frameworks, we compare
our adaptive technique with flooding and broadcast-based replication techniques
and show that for failure rates of up to 60% it ensures better data
survivability with lower communication costs.
