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Author: search.filters.author.B, Rosiline JeethaStart date: 2010

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    A NOVEL HYBRID APPROACH ON SECURE DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS
    (International Journal of Future Generation Communication and Networking, 2020) R, Kowsalya; B, Rosiline Jeetha
    n recent year wireless sensor network plays an important role in day to day life, to achieve the security, cryptography techniques are used. As wireless sensor has the limited memory space and energy consumption to provide security is vital problem. The main aim of this research work is to analysing different cryptographic techniques such as symmetric key cryptography and asymmetric key cryptography and comparing AES, DES, 3DES, RC5 and IDEA encryption techniques. In this paper, a new security symmetric algorithm was proposed to provide high security. It provides cryptographic primary key integrity, confidentiality and authentication. The results show that the proposed hybrid algorithm HSR19 gives efficient performance for communication devices with the parameters in computation time with different file sizes, encryption and decryption speed and energy
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    CCHS: AN IMPROVED CENTRALIZED CLUSTER HEAD SELECTION IN WIRELESS SENSOR NETWORKS
    (Journal of Advanced Research in Dynamical and Control Systems, 2019) R, Kowsalya; B, Rosiline Jeetha
    In wireless sensor network, devices or nodes are normally battery powered devices. These nodes have imperfect quantity of primary energy that an enthusiastic at dissimilar rates, depending on the power or energy level. A modified centralized cluster based cluster-head selection is a primary issue in existing representative clustering methods such as M-LEACH and SEDC, cluster heads are selected with a optional likelihood in a distributed manner. So, there are huge deviations of the amount of clusters and size for each cluster at each round throughout network lifetime. In order to conquer issues of difficult cluster-head selection and great energy consumption in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), in this paper presents a Modified Centralized Cluster-Head Selection (MCCHS) algorithm based on Simple Energy-efficient Data Collecting (SEDC) protocol was proposed. The proposed system presents a MCCHS algorithm in static manner selection algorithm for cluster head selection technique using NS (Network Simulator) 2.34 Framework. This technique leads to improved CH strength, reduction in the amount of clusters in the network, and improving the energy efficiency.
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    MISSING VALUE AWARE OPTIMAL FEATURE SELECTION METHOD FOR EFFICIENT BIG DATA MINING PROCESS
    (International Journal of Recent Technology and Engineering (IJRTE), 2019-09) S, Meera; B, Rosiline Jeetha
    Big mining plays a more critical role in the real world environment due to presence of large volume of data with different varieties and type. Handling these data values and predicting the information would be the more difficult task which needs to be concerned more to obtain the useful knowledge. This is achieved in our previous research work by introducing the Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA -MANN) which can select the optimal features from the big volume of data. However this research work might be reduced in its performance due to presence of missing values in the dataset. And also this method is more complex to perform due to increased computational overhead of ANN algorithm. This is resolved in the proposed research method by introducing the method namely Missing Value concerned Optimal Feature Selection Method (MV-OFSM). In this research method Improved KNN imputation algorithm is introduced to handle the missing values. And then Dynamic clustering method is introduced to cluster the dataset based on closeness measure. Then Anarchies Society Optimization (ASO) based feature selection approach is applied for performing feature selection in the given dataset. Finally a Hybrid ANN-GA classification technique is applied for implementing the classification. The overall performance evaluation of the research method is performed in the matlab simulation environment from which it is proved that the proposed research method leads to provide the better performance than the existing research technique
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    SURVEY ON SWARM SEARCH FEATURE SELECTION FOR BIG DATA STREAM MINING.
    (International Journal of Computational Intelligence Research, 2017-01) S, Meera; B, Rosiline Jeetha
    Big data is the slightly abstract phase which describes the relationship between the data size and data processing speed in the system. The many new information technologies the big data deliver dramatic cost reduction, substantial improvements in the required time to perform the computing task or new product and service offerings. The several complicated specific and engineering problems can be transformed in to optimization problems. Swarm intelligence is a new subfield of computational intelligence (CI) which studies the collective intelligence in a group of simple intelligence. In the swarm intelligence, useful information can be obtained from the competition and cooperation of individuals. In this paper discussed about some of the optimization algorithms based on swarm intelligence such as Ant Colony optimization (ACO), Particle Swarm Algorithm (PSO), Social Spider Optimization (SSO) Algorithm and Parallel Social Spider Optimization (P-SSO) Algorithm. These optimization techniques are based on their merits, demerits and metrics accuracy, sum of intra cluster distance, Recovery Error Etc.