都市災害環境における移動ロボットネットワークのデータ生存性
Data Survivability in Networks of Mobile Robots in Urban Disaster Environments
都市災害での大規模マルチロボット監視を想定し、現実的な移動・故障シミュレーション枠組みと、故障率や優先度を考慮した適応的データ複製手法を提案し、洪水型・ブロードキャスト型より低通信コストで高いデータ生存性を示した。
著者: Nicolas Kourtellis, Adriana Iamnitchi, Cristian Borcea, Robin Murphy
分類: cs.RO, cs.DC, cs.NI
原文アブストラクト
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.
