arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1201.4479cs.ITcs.DBmath.IT
收藏

基于LT码的大规模传感器网络分布式数据存储

Distributed Data Storage in Large-Scale Sensor Networks Based on LT Codes

Saber Jafarizadeh, Abbas Jamalipour

摘要:

提出了一种提高大规模传感器网络数据持久性的算法。在这里考虑的情况下,k出n个节点的感觉的现象,并产生?信息包。由于通常危险的环境和有限的资源,例如能源,网络中的传感器是脆弱的。此外,由于网络规模庞大,从少数几个中心希望收集信息是不可行的。由于每个节点的内存有限,洪泛也不是理想的选项。因此,增加数据持久性的最佳方法是通过随机游走在整个网络中传播数据。该算法基于分布式LT(Luby Transform)码,利用LT码编译码复杂度低的优点。在以前的算法中,基本的全局信息(例如,N和K)是基于图统计来估计的,这需要过多的传输。在我们提出的算法中,这些值是在没有额外传输的情况下获得的。提出了一种新的随机游走概率转发表生成方案,提高了随机游走的混合时间。该方法只使用本地信息,它是可扩展的任何网络拓扑结构。通过仿真验证了所提出的算法与以往算法相比性能的提高。

英文摘要:

This paper proposes an algorithm for increasing data persistency in large-scale sensor networks. In the scenario considered here, k out of n nodes sense the phenomenon and produced ? information packets. Due to usually hazardous environment and limited resources, e.g. energy, sensors in the network are vulnerable. Also due to the large size of the network, gathering information from a few central hopes is not feasible. Flooding is not a desired option either due to limited memory of each node. Therefore the best approach to increase data persistency is propagating data throughout the network by random walks. The algorithm proposed here is based on distributed LT (Luby Transform) codes and it benefits from the low complexity of encoding and decoding of LT codes. In previous algorithms the essential global information (e.g., n and k) are estimated based on graph statistics, which requires excessive transmissions. In our proposed algorithm, these values are obtained without additional transmissions. Also the mixing time of random walk is enhanced by proposing a new scheme for generating the probabilistic forwarding table of random walk. The proposed method uses only local information and it is scalable to any network topology. By simulations the improved performance of developed algorithm compared to previous ones has been verified.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑