Data collection from WSNs to the cloud based on mobile Fog elements
Data collection from WSNs to the cloud based on mobile Fog elements
复制标题
基于移动雾元素从无线传感器网络到云端的数据收集
DOI：
10.1016/j.future.2017.07.031
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发表时间：
2017-07
影响因子：
7.5
通讯作者：
Baowei Wang
中科院分区：
文献类型：
--
作者：
Tian Wang;Ji;ian Zeng;Yongxuan Lai;Yiqiao Cai;Hui Tian;Yonghong Chen;Baowei Wang
云计算强大的计算和存储能力为无线传感器网络注入了新的活力，并催生了一系列新的应用。然而，由于无线传感器网络通信能力差，特别是在时延敏感的应用中，无线传感器网络向云端的数据采集成为其进一步发展和应用的瓶颈。我们提出了一个雾结构组成的多个移动的汇。移动的汇聚点作为雾节点，在无线传感器网络和云之间架起了差距的桥梁。它们相互协作建立多输入多输出（MIMO）网络，旨在最大化吞吐量和最小化传输延迟。我们区收集区的所有汇，然后分配传感器到相应的汇。对于那些分配的传感器，跳数和能量消耗被认为是解决跳点问题。传感器数据通过接收器同步上传到云端。该问题被证明是NP-难的，我们设计了一个近似算法来解决这个问题的几个可证明的性质。我们还设计了一个详细的路由算法的传感器考虑跳数和能量消耗。我们比较我们的方法与几个传统的解决方案。大量的实验结果表明，该方法显着优于传统的解决方案。
The powerful computing and storage capability of cloud computing can inject new vitality into wireless sensor networks (WSNs) and have motivated a series of new applications. However, data collection from WSNs to the Cloud is a bottleneck because the poor communication ability of WSNs, especially in delay-sensitive applications, limits their further development and applications. We propose a fog structure composed of multiple mobile sinks. Mobile sinks act as fog nodes to bridge the gap between WSNs and the Cloud. They cooperate with each other to set up a multi-input multi-output (MIMO) network, aiming to maximize the throughput and minimize the transmission latency. We district collecting zones for all sinks and then assign sensors to the corresponding sinks. For those assigned sensors, hops and energy consumption are considered to solve the hopspot problem. Sensor data are uploaded to the Cloud synchronously through sinks. The problem is proved to be NP-hard, and we design an approximation algorithm to solve this problem with several provable properties. We also designed a detailed routing algorithm for sensors considering hops and energy consumption. We compare our method to several traditional solutions. Extensive experimental results suggest that the proposed method significantly outperforms traditional solutions.
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DOI：
--
发表时间：
2015-08
期刊：
Journal of Tsinghua University
影响因子：
--
作者：
Cai Jie;Gu Ming
通讯作者：
Cai Jie;Gu Ming
DOI：
10.1145/2701126.2701231
发表时间：
2015-01
期刊：
Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication
影响因子：
--
作者：
Dabin Kim;Young-Bae Ko
通讯作者：
Dabin Kim;Young-Bae Ko
影响因子：
4.1
作者：
Yanhua Zhang;Xingming Sun;Baowei Wang
通讯作者：
Yanhua Zhang;Xingming Sun;Baowei Wang
视觉传感器网络快速准确的近似重复图像消除
DOI：
10.1177/1550147717694172
发表时间：
2017-02
影响因子：
2.3
作者：
Zhou Zhili;Wu Q. M. Jonathan;Huang Fang;Sun Xingming
通讯作者：
Sun Xingming
影响因子：
4.1
作者：
Xianggang Zhang;Hao Wang;Yuliang Shi;Changjun Fu;Huiqin Wang;Guoyu Wang
通讯作者：
Xianggang Zhang;Hao Wang;Yuliang Shi;Changjun Fu;Huiqin Wang;Guoyu Wang