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.
Heterogeneity in data has been used in prioritized epidemic routing (PREP) @cite_44 , were bundles of data are prioritized based on cost to destination, source, and expiration time. Costs are derived from per-link average availability'' information that is disseminated in an epidemic manner. PREP maintains a gradient of replication density that decreases with increasing distance from destination. In our work, we also assume that data are replicated based on different requirements or priorities. However, these requirements are due to the significance to survive the mission, as assumed by the human coordinators and not due to their producer, their destination or associated lifetime. Our adaptive replication technique adjusts (i.e., increases) the survivability acquired by the data as they are replicated across new robots, until their accumulated survivability meets their original requirements set by the coordinators.
@cite_44
2071738571
We describe PRioritized EPidemic (PREP) for routing in opportunistic networks. PREP prioritizes bundles based on costs to destination, source, and expiry time. Costs are derived from per-link "average availability" information that is disseminated in an epidemic manner. PREP maintains a gradient of replication density that decreases with increasing distance from the destination. Simulation results show that PREP outperforms AODV and Epidemic Routing by a factor of about 4 and 1.4 respectively, with the gap widening with decreasing density and decreasing storage. We expect PREP to be of greater value than other proposed solutions in highly disconnected and mobile networks where no schedule information or repeatable patterns exist.