T. Shankar ¹, Tony James ¹, R. Mageshvaran ¹, A. Rajesh ¹

Affiliations
1 VIT University, Vellore, IN

Abstract

Objectives: The nodes in a wireless sensor network are extremely energy limited. Often, there occurs transmission of similar sensed data from adjacent nodes of a geographical region with high sensor node density. Network lifetime of the sensor network can be improved by grouping such closely packed nodes using the node locations obtained from a localization algorithm, thereby eliminating redundant data transmission. Methods: This paper employs the Flower Pollination Algorithm (FPA) for node localization and the performance of this method is compared against alternative localization techniques, viz. Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA). Findings: The lifetime improvement of the proposed Proximity Grouping Algorithm (PGA) method is compared with that offered by conventional Low-Energy Adaptive Clustering Hierarchy (LEACH) method. Application/Improvements: The proposed PGA method shows 35% better network lifetime improvement, based on the residual energy of the network.

Keywords

Flower Pollination Algorithm (FPA), Flower Pollination, Localization, Meta Heuristic, Network Lifetime.

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A. Rajesh ¹, G. Pragathi ¹, T. Shankar ¹

Affiliations
1 School of Electronics Engineering, VIT University, Vellore – 14, IN

Abstract

Background/Objectives: Recent advances in IEEE 802.11ac based wireless networks have made them potential enough to be used for Voice over Wireless Local Area Networks (VoWLAN). The main requirement in the design of such network is to minimize the energy consumption to maximize the network lifetime of user equipment’s. Methods/Statistical Analysis: For such energy conservation, the IEEE 802.11ac networks make use of power saving protocols, where the wireless radio is turned on and off periodically. In this paper, a group based adaptive power saving mechanism is suggested that dynamically utilize the contention and transmission opportunity parameters of user equipment. Findings: The design of the power saving is unique as the grouping is based on the current power level of user equipment’s, type of service, channel status and the number of services to a user equipment. Application/Improvements: Simulation results shows that proposed grouping technique provides an improvement of 46.25% and 47.29% in terms of energy consumption and delay, respectively compared the existing power saving protocol for delay sensitive and VoWLAN applications.

Keywords

IEEE 802.11ac, Medium Access Control, VoWLAN, Wireless Networks.

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M. Azhagiri ¹, A. Rajesh ²

Affiliations
1 Department of CSE, St Peters University, Chennai - 600054, Tamil Nadu, IN
2 Department of CSE, C. Abdul Hakeem College of Engineering and Technology, Anna University, Melvisharam, Vellore - 632509, Tamil Nadu, IN

Abstract

Objectives: To develop a novel method of Intrusion Detection System (IDS) by coupled dynamic learning of system with Fuzzy logics for minimizing false alarms and security compromise of a system connected with internet. Method: When Intrusion Detection System (IDS) raise alarm based on assigned rules, there would be a possibility for too many false alarms. The degree of intrusion and subsequent alert are often depending on different situations. These situations are not unique for all systems hence; a global knowledge based filter rules fail to minimize false alarms. In this paper, a concept was proposed to solve this hazy and unclear cutoff rules derived from global knowledge, by self-learning and turning activity of system, towards the security issues from the analytical outcomes of behavioral patterns of network system. Findings: The use of fuzzy logic helps to smooth the sharp separation of normal and abnormal behaviors in network activity which adds further strength in minimizing false alarms and security compromise. This concept is illustrated and demonstrated using some familiar network behaviors for easy understanding of logics and mechanism of the proposed IDS model. Application/Improvements: This intelligence associated with fuzzy logic may be extended with more and more parameters for better efficiency in Intrusion Detection System (IDS).

Keywords

Anomaly Detection, Behavior Analysis, Fuzzy Logic, Fuzzy Score, Fuzzy Decision Module Intrusion-Detection System.

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A. Rajesh ¹, Rakesh Achar ¹

Affiliations
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Background/Objectives: The Long-Term Evolution- Advanced (LTE-A) based heterogeneous networks focus on the Femto based cell deployment. These Femto cells will benefit the customers and service providers in term of network coverage and spectral efficiency. In such networks, optimal means of resource allocations is the major concern. Methods/Statistical Analysis: This paper reviews a detail study of resource allocation in Femto cell based LTE-A networks. In addition, a resource allocation strategy is suggested by means of Heterogeneous Channel Quality Index (CQI) based Scheduling Techniques (HCBST). It makes use of an indexed adaptive modulation and coding technique by referring the various CQI parameters. Findings: The HCBST algorithm is implemented by analyzing various scheduling technique at Femto and Marco base stations. The proposed resource allocation strategy is implemented by means of two scenarios, namely, Femto without node mobility and Femto with node mobility. Application/Improvements: The proposed HCBST system with modified largest weight delay first with CQI at Macro cell and Exponential with CQI at Femto cell (ML-EXP-CQI) based scheduling display 1.1879% and 2.85% better in term of throughput and spectral efficiency, respectively, as compared to existing scheduling algorithm..

Keywords

Femto Cell Networks, HCBST, Long-Term Evolution- Advanced, ML-EXP-CQI, Scheduling.

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Sheik Faritha Begum ¹, K. P. Kaliyamurthie ¹, A. Rajesh ²

Affiliations
1 Department of Computer Science and Engineering, Bharath University, Chennai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, C. Abdul Hakeem College of Engineering and Technology, Vellore – 638052, Tamil Nadu, IN

Abstract

Objective: This paper discusses and compares the various clustering methods over Ill-structured datasets and the primary objective is to find the best clustering method and to fix the optimal number of clusters. Methods: The dataset used in this experiment has derived from the measures of sensors used in an urban waste water treatment plant. In this paper, clustering methods like hierarchical, K means and PAM have been compared and internal cluster validity indices like connectivity, Dunn index, and silhouette index have been used to validate the clusters and the optimization of clustering is expressed in terms of number of clusters. At the end, experiment is done by varying the number of clusters and optimal scores are calculated. Findings: Optimal score and optimal rank list are generated which reveals that the hierarchical clustering is the optimal clustering method. The optimum value of connectivity index should be minimum, silhouette should be maximum, dunn should be maximum. So by interpreting the results, the optimal number of clusters for the experimental dataset have been concluded as K=2 and the optimal method for clustering the given dataset is hierarchical. Applications: The experiment has been done over the dataset derived from the measures of sensors used in a urban waste water treatment plant.

Keywords

Clustering Methods, Ill-Structured Datasets, Optimization,Validity Indices

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J. S. Adeline Johnsana ¹, A. Rajesh ², S. Kishore Verma ³

Affiliations
1 Department of Computer Science and Engineering, St. Peter’s University, Avadi, Chennai - 600054, Tamil Nadu, IN
2 Department of Computer and Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvisharam - 632509, Tamil Nadu, IN
3 Department of Computer Science and Engineering, SCSVMV University, Kanichipuram - 631561, Tamil Nadu, IN

Abstract

Background/Objectives: Time series is a significant type of data, widely used in diverse application such as financial, medical, and weather analyses, which in-turn contain personal privacy to a great extent. Methods/Statistical Analysis: The perquisite to protect privacy of time series data is to bolster the data holder to get involved in the above applications without any privacy threats. The k-anonymization approach of time series data has picked up consideration over late years, a key requirement of such an approach is to guarantee anonymization of time series data while minimizing the information loss caused from that approach. Findings: In this article, we implemented a novel methodology called CATs (Clustered k-Anonymization of Time Series Data) that applies the idea of clustering on time series data and ensure anonymization by gaining minimized information loss within venerable utility. The fundamental perception here is that the time series data tuples that are alike, ought to be a part of one cluster, and de-identification of these tuples is furnished. We thus formulate and proposed this approach as CATs, implemented through mishmash of WEKA and ARX anonymization tool. We have executed the solution on two benchmark time series data set available in UCR, Our experimental result strives that CATs confirms to have minimal information loss ranging from 18% to 24% reduction rate when compared with existing TSA (Time Series Anonymization) approaches. Applications/Improvements: As result of our experimentation, we express that our approach can play a remarkable role in the field of financial management, Online Medical process monitoring and management etc.

Keywords

Clustering, Information Loss, k-Anonymization, Privacy Preserving Data Mining, Re-Identification Risks, Time Series Data Mining.

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