NOSS: Sensor Data Ghosting: A Framework for the Survival of Critical Data under Sensor Failures

在人为或自然灾害期间收集的关键传感器数据的生存可以说比传统的传感器网络设计目标（例如延长传感器的寿命）更重要。在该项目下，正在开发一个传感器数据重影框架。该框架创建了最小的数据冗余，称为传感器数据幽灵，它在传感器网络中漫游到汇点。在传感器故障的情况下，数据重影被加速到接收器，以便及时恢复。由于刚好足够的数据（由于数据重影而生成）的可用性，在接收器处恢复传感器数据变得可行。编码和网络解决方案正在开发中，并以协同方式整合，以实现传感器数据的生存性和持久性。特别地，正在开发将有效信道码映射到网络图上的网络编码方法。在网络方面，正在研究一种新的拓扑结构演进解决方案，该方案能够对随机故障迅速作出反应，并加快将关键数据传送到汇点。这种拓扑进化方法将传统的传感器网络几何拓扑重新排列为小世界网络拓扑，这允许少量通往汇点的长途“捷径”。相关的网络研究，如定向扩散和优先转发，也正在研究下的拓扑结构的演变。除了使传感器数据的生存能力达到新的水平外，该项目还具有比目标传感器应用更广泛的理论和实践影响。这包括开发新类型的“网络图上的代码”和自适应网络拓扑的方法。

The survival of critical sensor data that is collected during manmade or natural disasters is arguably more important than traditional sensor-network design objectives, such as prolonging the sensors' lifetime. Under this project, a sensor data ghosting framework is being developed. This framework creates minimal data redundancy, known as sensor data ghosts, which roam around the sensor network toward a sink. Under sensor failures, the data ghosts are expedited toward the sink for their timely recovery. Recovery of the sensor data at the sink becomes feasible due to the availability of just enough data (generated due to data ghosting). Coding and networking solutions are being developed and integrated in a synergetic manner to achieve sensor data survivability and persistence. In particular, network coding approaches that map efficient channel codes over network graphs are being developed. On the networking side, a novel topology evolution solution that is capable of reacting rapidly to random failures and providing expedited delivery of critical data to the sink is being researched. This topology evolution approach rearranges a traditional sensor-network geometric topology toward a small-world network topology, which allows a small number of long-haul "shortcuts" toward the sink. Related networking research, such as directed diffusion and prioritized forwarding, is also being investigated under topology evolution. In addition to enabling new levels of sensor data survivability, this project has significantly broader theoretical and practical impact than the target sensor application. This includes the development of new types of "codes on network graphs" and approaches in adaptive network topologies.