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.
Opportunistic networks (ONs) are a newly emerging type of delay tolerant network ( DTN) systems that opportunistically exploit unplanned contacts among nodes to share information. As with all DTN environments ONs experience frequent and large delays, and an end-to-end path from the source to destination may only exist for a brief and unpredictable period of time. Such network conditions present unique challenges to message routing. In this paper, we present the design and performance analysis of a novel core-based routing protocol for ON routing. Under the assumption that messages will have a delivery time constraint, we then provide a set of analytical results for rapid modeling and performance evaluation for the basic performance metrics of message delay, message delivery ratio, and buffer occupancy. We have implemented our protocol in ns-2, and our simulation results show that our protocol is quite effective and our analysis is accurate. We propose CLEAR (Context and Location-based Efficient Allocation of Replicas), a dynamic replica allocation scheme for improving data availability in mobile ad-hoc peer-to-peer (M-P2P) networks. To manage replica allocation efficiently, CLEAR exploits user mobility patterns and deploys a super-peer architecture, which avoids both broadcast storm during replica allocation as well as broadcast-based querying. CLEAR considers different levels of replica consistency and load as replica allocation criteria. Our performance study indicates CLEAR's overall effectiveness in improving data availability in M-P2P networks.