Achieving optimal data storage position in wireless sensor networks
Achieving optimal data storage position in wireless sensor networks
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DOI：
10.1016/j.comcom.2009.08.005
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Zhaochun Yu;Bin Xiao;Shuigeng Zhou
中科院分区：
文献类型：
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作者：
Zhaochun Yu;Bin Xiao;Shuigeng Zhou
无线传感器网络（WSNs）中的数据存储涉及生产者（诸如传感器节点）在存储位置中存储它们已经收集的大量数据，以及消费者（例如，基站、用户和传感器节点），然后检索该数据。在解决这个问题时，以前的工作未能利用多个生产者和消费者的数据速率和位置来确定在网状网络拓扑中通信成本有效的最佳数据存储位置。在本文中，我们首先将数据存储问题形式化为一对一（一个生产者和一个消费者）模型和多对多（m个生产者和n个消费者）模型，目标是最小化总能量成本。基于上述模型，我们提出了最优数据存储（ODS）算法，可以产生全局最优的数据存储位置的线性，网格和网状网络拓扑结构。为了减少ODS在网状网络拓扑中的计算量，提出了一种近似的、能获得局部最优位置的近优数据存储算法。ODS和NDS都是本地感知的，并且能够自适应地调整存储位置以最小化能耗。仿真结果表明，NDS不仅提供了大量的成本效益，集中式数据存储（CDS）和地理哈希表（GHT），但表现以及ODS在超过75%的情况下。
Data storage in wireless sensor networks (WSNs) involves producers (such as sensor nodes) storing in storage positions a large amount of data which they have collected and consumers (e.g., base stations, users, and sensor nodes) then retrieving that data. When addressing this issue, previous work failed to utilize data rates and locations of multiple producers and consumers to determine optimal data storage positions to be communication cost-effective in a mesh network topology. In this paper, we first formalize the data storage problem into a one-to-one (one producer and one consumer) model and a many-to-many (m producers and n consumers) model with the goal of minimizing the total energy cost. Based on above models, we propose optimal data storage (ODS) algorithms that can produce global optimal data storage position in linear, grid, and mesh network topologies. To reduce the computation of ODS in the mesh network topology, we present a near-optimal data storage (NDS) algorithm, which is an approximation algorithm and can obtain a local optimal position. Both ODS and NDS are locality-aware and are able to adjust the storage position adaptively to minimize energy consumption. Simulation results show that NDS not only provides substantial cost benefit over centralized data storage (CDS) and geographic hash table (GHT), but performs as well as ODS in over 75% cases.