دانلود رایگان مقاله استفاده از data mules برای شبکه های حسگر بازیابی اطلاعات

Abstract

In this paper, we study the problem of efficient data recovery using the data mules approach, where a set of mobile sensors with advanced mobility capabilities re-acquire lost data by visiting the neighbors of failed sensors, thereby avoiding permanent data loss in the network. Our approach involves defining the optimal communication graph and mules’ placements such that the overall traveling time and distance is minimized regardless to which sensors crashed. We explore this problem under different practical network topologies such as arbitrary graphs, grids and random linear networks and provide approximation algorithms based on multiple combinatorial techniques. Simulation experiments demonstrate that our algorithms outperform various competitive solutions for different network models, and that they are applicable for practical scenarios.

7. Conclusions and future work This work address the topological design of data mules usage for improving resiliency to data loss caused by net- work disasters. Our solutions involve constructing the op- timal data gathering tree and finding the optimal node placement under multiple network structures, such as gen- eral graph, linear and grids. We use the topology charac- teristic to produce multiple approximation algorithms and validate their performance using simulations. This paper emphasizes on the problem of minimizing the sum of distances the mule travels. Instead, we can ex- plore minimizing the maximum traveling distance or time of the mule. Formally, we can define the (1, 1)-Mule prob- lem as follows: minT,m maxv∈V |t(m, δ(v, T ))|. Interestingly, the given objective completely changes the complexity and algorithms of the problem. For example, while the opti- mal topology in the min-sum version was a star, in the min-max version it is a line that traverse all the nodes in the graph. In addition, we can find the optimal solution by carefully selecting the location of the mule, which means the problem is not NP-hard. It will be interesting to fur- ther explore this objective under different network criteria and to compare the solutions to the ones proposed in this paper.