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Raja Rajeswari S.,Anna University | Seenivasagam V.,National Engineering College Autonomous
Wireless Personal Communications | Year: 2016

Topology Maintenance Protocols are vital elements that influence Wireless Sensor Networks. These protocols strive to conserve energy and to prevent collisions during communication. In this paper, a Secured Energy Conserving Slot-based Topology Maintenance Protocol, which serves its purpose by overthrowing several existing issues such as energy deterioration and memory overhead, is proposed. Energy conservation is achieved by node behavior based on timeslot. Hence for a particular timeslot, only certain count of nodes remain in work cycle, and the remaining nodes remain in the state of sleep. This conserves energy at its best, which in turn improves the lifespan of the network. Additionally, the issue of memory overhead is resolved by allowing only direct communication between the node and the base station, and hence the base station directly authenticates the constituent nodes. This work widens its scope by focusing on security breaches too. We introduce five attacks to the system; however, the system proves its resilience. The proposed work outperforms the existing system in terms of energy conservation, increase in network lifetime and less memory overhead. © 2015, Springer Science+Business Media New York.

Arumugadevi S.,Sri Krishna College of Engineering And Technology | Seenivasagam V.,National Engineering College Autonomous
International Journal of Automation and Computing | Year: 2016

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features “a” and “b” of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy. © 2016 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg

Anjanadevi S.,National Engineering College Autonomous | Vijayakumar D.,National Engineering College Autonomous | Srinivasagan K.G.,National Engineering College Autonomous
2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014 | Year: 2014

Cloud computing is an emerging, computing model wherein the tasks are allocated to software, combination of connections, and services accessed over a network. This connections and network of servers is collectively known as the cloud. In place of operating their own data centers, users might rent computing power and storage capacity from a service provider and pays only for what they use. Cloud storage is delivering the data storage as service. If the data is stored in cloud, it must provide the data access and heterogeneity. With the advances in cloud computing it allows storing of large number of images and data throughout the world. This paper proposes the indexing and metadata management which helps to access the distributed data with reduced latency. The metadata management can be enhanced for large scale file system applications. When designing the metadata, the storage location of the metadata and attributes is important for the efficient retrieval of the data. Indexes are used to quickly locate data without having to search over every location in storage. Based on these two models, the data can be easily fetched and the search time was reduced to retrieve the appropriate data. © 2014 IEEE.

Sathya N.,National Engineering College Autonomous | Kalaiselvi S.,National Engineering College Autonomous | Gomathi V.,National Engineering College Autonomous | Srinivasagan K.G.,National Engineering College Autonomous
2014 International Conference on Electronics and Communication Systems, ICECS 2014 | Year: 2014

Remote sensing is the most reliable and effective way to monitor the land use and land cover changes (LULC). In the present day world, thisLULC mapping is of great significance in scientific, scholarly research, planning and management. Based on remotely sensed data acquired on two different periods it could be quite possible to detect the changes. This paper proposes a novel unsupervised monitoring approach based on Undecimated Discrete Wavelet Transforms (UDWT). In urban areas, detection of land space destruction is highly essential. In this paper, a novel UDWT transform domain approach to construct change detection binary map is performed initially on two co- registered images. Change Vector Analysis (CVA) is applied next on the two temporal images. As a result, change or no change map is produced. Finally forecasting of the land use trend changes using markov chain is done. © 2014 IEEE.

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