In this work we study the problem of collecting protected data in ad-hoc sensor network using a mobile entity called MULE. The objective is to increase information survivability in the network. Sensors from all over the network, route their sensing data through a data gathering tree, towards a particular node, called the sink . In case of a failed sensor, all the aggregated data from the sensor and from its children is lost. In order to retrieve the lost data, the MULE is required to travel among all the children of the failed sensor and to re-collect the data. There is a cost to travel between two points in the plane. We aim to minimize the MULE traveling cost, given that any sensor can fail. In order to reduce the traveling cost, it is necessary to find the optimal data gathering tree and the MULE location. We are considering the problem for the unit disk graphs and Euclidean distance cost function. We propose a primal–dual algorithm that produces a $$\left( 20+\varepsilon \right) $$ 20 + ε -approximate solution for the problem, where $$\varepsilon \rightarrow 0$$ ε → 0 as the sensor network spreads over a larger area. The algorithm requires $$O\left( n^{3}\cdot \varDelta \left( G\right) \right) $$ O n 3 · Δ G time to construct a gathering tree and to place the MULE, where $$\varDelta \left( G\right) $$ Δ G is the maximum degree in the graph and n is the number of nodes.

中文翻译：

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使用 Mobile Mule 改进数据收集解决方案

在这项工作中，我们研究了使用称为 MULE 的移动实体在自组织传感器网络中收集受保护数据的问题。目标是提高网络中的信息生存能力。来自整个网络的传感器通过数据收集树将其传感数据路由到特定节点，称为接收器。如果传感器发生故障，来自传感器及其子传感器的所有聚合数据都将丢失。为了找回丢失的数据，MULE 需要在故障传感器的所有子节点之间移动并重新收集数据。在飞机上的两点之间旅行是有成本的。考虑到任何传感器都可能出现故障，我们的目标是最小化 MULE 的旅行成本。为了降低行进成本，需要找到最优的数据采集树和MULE位置。我们正在考虑单位圆盘图和欧几里得距离成本函数的问题。我们提出了一种原始对偶算法，该算法产生 $$\left( 20+\varepsilon \right) $$ 20 + ε - 问题的近似解，其中 $$\varepsilon \rightarrow 0$$ ε → 0 作为传感器网络覆盖更大的区域。该算法需要$$O\left( n^{3}\cdot \varDelta \left( G\right) \right) $$ O n 3 · Δ G 时间来构建采集树并放置MULE，其中$ $\varDelta \left( G\right) $$ Δ G 是图中的最大度数，n 是节点数。