Informatics Publishing Limited

J. Sybi Cynthia ¹, D. Shalini Punithavathani ²

Affiliations
1 C.S.I. Institute of Technology, Thovalai, Tamilnadu, IN
2 Government College of Engineering, Tirunelveli, Tamilnadu, IN

Abstract

Sensor nodes set out in malicious surroundings and are susceptible to pickup and pact. An intruder may reach confidential hidden particulars from these sensors, clone and cleverly spread out them in the network to put up a variety of insider attacks. This attack mode is mostly defined as a clone attack. We propose a new layout for clone attack detection that comes up with successful and well-organized technique called PAWS (Pair Access Witness Selection) technique to detect such clone attacks. Selecting common nodes in between the pairmate as a witness node is the key idea of PAWS to detect clones in the network. The witness selection plays a vital role to overcome the redundancy and reachability problem. The proposed framework results in detecting clones and detection performance depends on the proper selection of witness nodes. Performance analysis and simulations also reveal that our new scheme is more proficient than extant schemes from communication cost and energy consumption.

Keywords

Clone Attacks, Wireless Sensor Networks, Network Security, Node Replication Detection.

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J. Sybi Cynthia ¹, D. Shalini Punithavathani ²

Affiliations
1 Department of Computer Science and Engineering, C.S.I. Institute of Technology, IN
2 Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, IN

Abstract

Wireless Sensor Networks (WSNs) deployed in the destructive atmosphere are susceptible to clone attacks. Clone attack in wireless sensor network is a complicated problem because it deployed in hostile environments, and also the nodes could be physically compromised by an adversary. For valuable clone attack detection, the selection criteria play an important role in the proposed work. In this paper, it has been classified the existing detection schemes regarding device type, detection methodologies, deployment strategies and detection ranges and far explore various proposals in deployment based selection criteria category. And also this paper provides a review of detection methodology based on various clone attack detection techniques. It is also widely agreed that clones should be detected quickly as possible with the best optional. Our work is exploratory in that the proposed algorithm concern with table assisted random walk with horizontal and vertical line, frequent level key change and revokes the duplicate node.Our simulation results show that it is more efficient than the detection criteria in terms of security feature, and in detection rate with high resiliency. Specifically, it concentrates on deployment strategy which includes grid based deployment technique. These all come under the selection criteria for better security performance. Our protocol analytically provides effective and clone attack detection capability of robustness.

Keywords

Clone Attacks, Wireless Sensor Networks, Node Replication Detection.

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C. Jehan ¹, D. Shalini Punithavathani ²

Affiliations
1 Department of Computer Science and Engineering, Tamizhan College of Engineering and Technology, IN
2 Government College of Engineering, Tirunelveli, IN

Abstract

In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN). The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM) and gravitational search algorithm (GSA) are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA) is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

Keywords

Clustering, Dynamic, Target Tracking, Static, Oppositional, Gravitational Search.

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E. Anandha Banu ¹, D. Shalini Punithavathani ²

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, IN

Abstract

High quality power is very much critical and essential for medical, research and industrial applications to bring good quality results with accurate evaluation. Hence all the sensitive equipments and critical loads need to be provided with high quality and reliable power, where Uninterruptible Power Supplies (UPS) are mainly used to supply reliable power to these loads. Inverter is the main component of a UPS. In the recent times, by the advanced usage of semiconductor devices and non linear loads, harmonics are unavoidable. So the actual challenge for UPS is, under a non linear condition of load, it has to maintain a high quality sinusoidal output voltage. In this paper, the inverter of UPS is replaced by a nine level cascaded H bridge multilevel inverter with equal DC sources and harmonics can be eliminated by the optimal selection of switching angle by using Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique along with a hybrid technique to optimize and minimize the Total Harmonic Distortion(THD). The proposed hybrid technique utilizes the Genetic Algorithm (GA) and Neural Network (NN). The switching angles are calculated offline using Genetic Algorithm. Then the NN is trained by these switching angles and the real time switching angles are found out by Neural Network. The proposed technique is tested over a nine level cascaded H-bridge inverter and the resultant fundamental and harmonic voltages are analysed. Then, simulation is carried out in Matlab/Simulink environment and the results indicate that the switching angles obtained using this method results in efficient harmonic minimization.

Keywords

Genetic Algorithm, Neural Network, Harmonics, Switching Angle, UPS.

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