Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment
Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment
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DOI：
10.26599/tst.2019.9010061
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发表时间：
2021-04
影响因子：
6.6
通讯作者：
Huan Yang;Feng Li;Dongxiao Yu;Yifei Zou;Jiguo Yu
中科院分区：
文献类型：
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作者：
Huan Yang;Feng Li;Dongxiao Yu;Yifei Zou;Jiguo Yu
在大数据时代，传感器网络已经被广泛部署，产生了大量的数据用于各种应用。然而，由于传感器网络通常被放置在恶劣的环境中，管理大量的数据是一个非常具有挑战性的问题。在这项研究中，我们主要集中在异构无线传感器网络中的数据存储可靠性问题，强大的存储节点部署在传感器网络中，并通过编码技术利用数据冗余。为了最小化数据传输和数据存储成本，我们设计了一种算法来联合优化数据路由和存储节点部署。该问题可以归结为一个二元非线性组合优化问题，由于其NP-困难性，设计近似算法是非常困难的。通过利用马尔可夫近似框架，我们精心设计了一个有效的算法驱动的连续时间马尔可夫链调度部署的存储节点和相应的路由策略。我们还进行了大量的模拟，以验证我们的算法的有效性。
In the era of big data, sensor networks have been pervasively deployed, producing a large amount of data for various applications. However, because sensor networks are usually placed in hostile environments, managing the huge volume of data is a very challenging issue. In this study, we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques. To minimize data delivery and data storage costs, we design an algorithm to jointly optimize data routing and storage node deployment. The problem can be formulated as a binary nonlinear combinatorial optimization problem, and dueto its NP-hardness, designing approximation algorithms is highly nontrivial. By leveraging the Markov approximation framework, we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy. We also perform extensive simulations to verify the efficacy of our algorithm.