Abstract

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

Existing System

Big data is a collection of data sets so large and complex that it becomes difficult to process with on hand database management systems or traditional data processing applications. It represents the progress of the human cognitive processes, usually includes data sets with sizes beyond the ability of current technology, method and theory to capture, manage, and process the data within a tolerable elapsed time WSN with cloud can be categorized as a kind of complex network systems. In these complex network systems such as WSN and social network, data abnormality and error become an annoying issue for the real network applications

Some work has been done for big data analysis and error detection in complex networks including intelligence sensors networks. There are also some works related to complex network systems data error detection and debugging with online data processing techniques. Since these techniques were not designed and developed to deal with big data on cloud, they were unable to cope with current dramatic increase of data size. For example, when big data sets are encountered, previous offline methods for error detection and debugging on a single computer may take a long time and lose real time feedback. Because those offline methods are normally based on learning or mining, they often introduce high time cost during the process of data set training and pattern matching.

Disadvantages:

1. No big data analysis and error detection

 2. Increase packet loss ratio.

 3. Network Failure

Proposed System

Advantages:

Our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy. We aim to develop a novel error detection approach by exploiting the massive storage, scalability and computation power of cloud to detect errors in big data sets from sensor networks. Fast detection of data errors in big data with cloud remains challenging. Especially, how to use the computation power of cloud to quickly find and locate errors of nodes in WSN needs to be explored.

1. NodeSide/EdgeSide error detection.
2. Improve Network Performance.
3. Big data analysis and error detection

SYSTEM REQUIREMENTS

SOFTWARE REQUIREMENTS:

•           Web Technologies                             :           HTML, CSS, JS. JSP

•           Programming Language                  :           Java and J2EE

•           Database Connectivity                      :           JDBC

•           Backend Database                            :           MySQL

•           Operating System                              :           Windows 08/10

HARDWARE REQUIREMENTS:

• Processor                                :           Core I3
• RAM Capacity                      :           Java and J2EE
• Hard Disk                              :           JDBC
• Monitor                                 :           MySQL
• Mouse                                   :           Two or Three Button Mouse
• Key Board                   :           Windows 08/10