Collaborative Research: NeTS-NBD: Construction of Robust and Efficient Disruption Tolerant Networks

传统的有线和无线网络都具有节点之间的端到端路径相对稳定的特性。然而，并非所有需要通信的环境都允许创建稳定的端到端路径。 例如，严重地震灾害的后果将包括建筑物倒塌、人员被困在废墟中、公用设施和道路受损以及火灾和二次爆炸。在这种情况下，即使是低速率的通信能力，对于分享重要信息，如幸存者的数量和位置以及救援人员的活动，也是极其宝贵的。 为了在这些有挑战性的环境中提供通信，网络协议必须被明确地设计为尽管网络组件的可用性和性能频繁中断（即，链接和节点）。这种系统被称为容断网络（DTNs）。这项工作的重点是构建DTNs，远远超出了寻找单播路径的任务。相反，这些DTN在不确定性和攻击下是鲁棒的，并且在节点和链路资源的使用上是高效的。具体而言，该项目侧重于以下基本功能：组通信，单跳传输和电源管理。这项工作是在合作大学的试验台上进行的。 该项目提供了算法、协议和平台，以创建强大而高效的DTN，从而在最关键的环境中实现通信，并揭示了可能在挑战较少的网络中具有更广泛适用性的成本-性能权衡。

Traditional networks, both wired and wireless, have the property that end-to-end paths between nodes are relatively stable. However, not all environments that require communication will allow the creation of stable end-to-end paths. For example, the aftermath of a severe earthquake disaster will include collapsed buildings, persons trapped in debris, damaged utilities and roads, as well as fires and secondary explosions. Under this situation, the ability to communicate, even at low rates, is extremely valuable for sharing vital information, such as the number and location of survivors and the activities of rescue workers. To provide communication in these challenged environments, the network protocols must be explicitly designed to perform despite frequent disruptions in the availability and performance of network components (i.e., links and nodes). The resulting systems are termed Disruption Tolerant Networks (DTNs).This work focuses on the construction of DTNs that go far beyond the task of finding unicast paths. Instead, these DTNs are robust under uncertainty and attack, and are highly efficient in their use of the node and link resources. Specifically, this project focuses on the following fundamental functions: group communication, single-hop transfers, and power management. The work is grounded by experimentation in testbeds at the collaborating universities. The project provides algorithms, protocols, and platforms to create robust and efficient DTNs enabling communication in the most critical of environments, as well as exposes cost-performance tradeoffs that might have broader applicability in less challenged networks.