A TIME EFFICIENT APPROACH FOR DETECTING ERRORS IN BIG SENSOR DATA ON CLOUD

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A TIME EFFICIENT APPROACH FOR DETECTING ERRORS
IN BIG SENSOR DATA ON CLOUD
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 alable 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 ramatically 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

This document describes a proposed approach for detecting errors in large sensor data sets on the cloud. It aims to leverage the computation power and scalability of cloud platforms to more efficiently detect and locate errors compared to traditional methods. Specifically, the approach uses the network topology of sensor networks to distribute error detection tasks across cloud resources. An experiment on a cloud platform showed this approach can significantly reduce error detection time while maintaining accuracy compared to processing large sensor data sets locally.

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A time efficient approach for detecting errors in big sensor data on cloudieeeprojectsbangalore

Big sensor data generated by networked sensors poses challenges for processing due to its volume and velocity. Cloud computing provides scalable computing resources to address this. Existing techniques don't efficiently detect and locate errors in large sensor datasets. The paper proposes an approach exploiting cloud platforms and sensor network topology. It classifies error types and analyzes network features to detect errors in temporal or spatial data blocks rather than whole datasets. Detection tasks are distributed across cloud resources to speed up the error detection process while maintaining accuracy. Experiments on a cloud platform demonstrate the approach significantly reduces error detection time in large sensor datasets.

A time efficient approach for detecting errors in big sensor data on cloudieeeprojectsbangalore

Big sensor data generated by networked sensors poses challenges for processing due to its volume and velocity. Cloud computing provides scalable computing resources to address this. Existing techniques don't efficiently detect and locate errors in large sensor datasets. The paper develops a novel approach exploiting cloud platforms and sensor network topology. It classifies error types and analyzes network features to detect errors through limited temporal or spatial data blocks rather than whole datasets. Detection and location tasks are distributed across cloud resources to fully utilize computation power and storage. Experiments show the approach significantly reduces error detection and location time in large sensor datasets while maintaining accuracy.

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