scholarly journals A Distributed Fault/Intrusion-Tolerant Sensor Data Storage Scheme Based on Network Coding and Homomorphic Fingerprinting

2012 ◽  
Vol 23 (10) ◽  
pp. 1819-1830 ◽  
Author(s):  
Rongfei Zeng ◽  
Yixin Jiang ◽  
Chuang Lin ◽  
Yanfei Fan ◽  
Xuemin Shen
Keyword(s):  
Network Coding ◽  
Data Storage ◽  
Sensor Data ◽  
Storage Scheme ◽  
Intrusion Tolerant

A scalable and robust key pre-distribution scheme with network coding for sensor data storage

2011 ◽  
Vol 55 (10) ◽  
pp. 2534-2544 ◽  
Author(s):  
Rongfei Zeng ◽  
Yixin Jiang ◽  
Chuang Lin ◽  
Yanfei Fan ◽  
Xuemin (Sherman) Shen
Keyword(s):  
Network Coding ◽  
Data Storage ◽  
Sensor Data ◽  
Distribution Scheme

A Framework for Storage Security in Cloud Computing

2013 ◽  
Vol 278-280 ◽  
pp. 1767-1770 ◽  
Author(s):  
Guo You Chen ◽  
Jia Jia Miao ◽  
Feng Xie ◽  
Han Dong Mao
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Computer Network ◽  
Large Data ◽  
Storage Security ◽  
Insertion And Deletion ◽  
Security Challenges ◽  
Storage Scheme ◽  
Short Time

Cloud computing has been a hot researching area of computer network technology, since it was proposed in 2007. Cloud computing also has been envisioned as the next-generation architecture of IT Enterprise [1]. Cloud computing infrastructures enable companies to cut costs by outsourcing computations on-demand. It moves the application software and database to the large data centers, where the management of the data and services may not be fully trustworthy [2]. This poses many new security challenges. In this paper, we just focus on data storage security in the cloud, which has been the most important aspect of quality of service. To ensure the confidentiality and integrity of user’s data in the cloud, and support of data dynamic operations, such as modification, insertion and deletion, we propose a framework for storage security, which includes cryptographic storage scheme and data structure. With our framework, the untrusted server cannot learn anything about the plaintext, and the dynamic operation can be finished in short time. The encryption algorithm and data storage structure is simple, and it’s easy to maintain. Hence, our framework is practical to use today.

scholarly journals A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

2018 ◽  
Vol 8 (10) ◽  
pp. 1992 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
SangSik Lee ◽  
ByungKwan Lee
Keyword(s):  
Deep Learning ◽  
Data Storage ◽  
Good Condition ◽  
Sensor Data ◽  
Weather Data ◽  
Learning Models ◽  
Weather Information ◽  
Nutrient Environment ◽  
Total Crop ◽  
Crop Diagnosis

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.

Data Storage Scheme of Sensor Network Based on HDFS

2014 ◽  
Vol 926-930 ◽  
pp. 2462-2465 ◽  
Author(s):  
Hui Xiang Zhou ◽  
Qiao Yan Wen
Keyword(s):  
Sensor Network ◽  
Data Storage ◽  
Cloud Storage ◽  
High Reliability ◽  
Storage System ◽  
Security Analysis ◽  
Storage Security ◽  
Mass Data ◽  
Storage Scheme ◽  
Multiple Copies

In order to solve the problem of growing massive of data in sensor network, we propose a new scheme of data storage for sensor network based on HDFS which is a cloud-based storage platform, it effectively alleviate the pressure of mass data storage on sensor network, and improved the scalability of storage system and part of the enhanced the data storage security on sensor network. And this scheme is based on cloud storage platform, storage the data which collected by sensors to each data node using a distributed architecture solution, and keep multiple copies of data in order to maintain its high reliability of data. As reducing the pressure of data storage, but also protects the security of stored data as shown by security analysis.

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