International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
An Energy Efficient Data Transmission And Aggregation of WSN Using Data Processing In MapReduce M.Mahalakshmi1, A. Mohamoodha bi2, B. Thamarai Selvi3, M. J. T. Vasantha Priya4 1eswarimrugan@gmail.com, 2mohamoodhabi@gmail.com, 3thamaraibupalan@gmail.com,
4M.
4vasanthapriyamjt@gmail.com
J. T. Vasantha Priya, Assistant Professor, Dept. Of Computer Science and Engineering, Velammal Institute of Technology, TamilNadu, India
---------------------------------------------------------------------***--------------------------------------------------------------------Station involves Broadcast Based (Infrastructure network using software defined network(SDN) framework. Oriented).This Hybrid Architecture is to reduce the The sensors are grouped as three different clusters and cluster Energy Consumption of Sensor nodes as it will be head elected between each cluster based on their distance, depleted soon when each sensor broadcasts sensed memory and battery to reduce the Energy Consumption of data to Base station as and when it senses. Hence a Sensor nodes .The sensed data is sent to the cluster head of each cluster by the other sensor nodes in encrypted form. Now, Cluster Head will be elected for each cluster by the data received in the head of cluster are aggregated and considering the battery, Memory and processing signature is appended to the data by Privacy Homomorphism ability. All the Sensors will be sending their sensed data Encryption Scheme using Ecc-Elgamal Signature in a Binary transmission for three completely different Network Clusters to the Cluster Head in a p2p manner.
Abstract : In this Paper, We construct a wireless sensor
and sent to the base station. Here, again the signature is verified and stored in hadoop distributed file system and query processing using Mapreduce.
Bigdata is a term for data sets that are so large or complex that traditional data processing application softwares are inadequate to deal with them. Challenges include capture, storage, analysis,datacuration,search, s haring, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Hadoop MapReduce (Hadoop Map/Reduce) is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks. The process of moving map outputs to the reducers is known as shuffling. Sort: Each reduce task is responsible for reducing the values associated with several intermediate keys. The set of intermediate keys on a single node is automatically sorted by Hadoop before they are presented to the Reducer.
Key Words: Wireless sensor network, software defined network, big data , MapReduce, Elgamal signature.
1.INTRODUCTION The basic architecture of Wireless Sensor Networks is usually a Hybrid type where it is a combination of Infrastructure Oriented and Infrastructure less Networks. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes (see Figure 1). The wireless protocol you select depends on your application requirements. Some of the available standards include 2.4 GHz radios based on either IEEE 802.15.4 or IEEE 802.11 (Wi-Fi) standards or proprietary radios, which are usually 900 MHz. The Communication from sensor to sensor head takes place through p2p Architecture (Infrastructure less) and the communication from Cluster Head to Base Š 2017, IRJET
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Impact Factor value: 5.181
Today, big data is strongly associated with MapReduce [6]. Its simplicity along with its scaling capabilities have rendered MapReduce the de facto |
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