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In a distributed Wireless Communication Technology, the Wireless Sensor Network (WSN) is a technology developing for sensing and performing different monitoring operations. The proposed algorithm dynamically partitions the Heterogeneous Wireless Sensor Network (HWSN) in to clusters. On the basis of initial energy, the cluster head (CH) is selected in the first round and residual energy with low draining rate protocol (RELDR) is used in the next round for selecting CH. The CH senses and aggregates the data, these summarized data is processed between the clusters and the link is maintained with the base station. Cluster Authority (CA) is a member node that acts as a supervising node which contains remove list and maintains the attacker information. The Technology Multiple Input and Multiple Output(MIMO) is used in the proposed system which reduces the noise in the signal and improves the network performance. During transmission, the unauthenticated nodes which are responsible for data leakage or any malicious activities are detected by the algorithm and information of these nodes are updated in the remove list of CA. The listed unauthenticated nodes or the black hole attack nodes in CA are removed from the network. The proposed algorithm removes the malicious nodes which are affecting the network performance and reconstructs the network by considering only the legitimate nodes. Experimental results will be analyzed for the network parameters like throughput, delay, energy and Packet delivery ratio and compared with the existing systems.

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The fact that the signature is widely used as a means of personal verification emphasizes the need for a signature verification system. In this paper, Standard Scores Correlation based Off-line Signature Verification (SSCOSV) System is presented. The comparison is made on the basis of Pixel density and geometric feature points. Before extracting the features, preprocessing of a scanned signature image is performed to isolate the signature part and to remove any spurious noise present. The concept of Correlation is used to compare the genuine signature with the test signature. If the value of Correlation Coefficient is greater than the predefined threshold (corresponding to minimum acceptable degree of similarity), the test signature is verified to be that of the claimed subject else detected as a forgery. It is found that the values of FAR, FRR and EER for optimal threshold correlation are better compared to that of existing systems.

Offline signature verification system is widely used as a behavioral biometric for identifying a person. This behavioral biometric trait is a challenge in designing the system that has to counter intrapersonal and interpersonal variations. In this paper, we propose a novel technique PCVOS: Principal Component Variances based Off-line Signature Verification on two critical parameters viz., the Pixel Density (PD) and the Centre of Gravity (CoG) distance. It consists of two parallel processes, namely Signature training which involves extraction of features from the samples of database and Test signature analysis which performs extraction of features from the test samples. The trained values from the database are compared with the features of the test signature using Principal Component Analysis (PCA). The PCVOS algorithm shows a notable improvement over the algorithms in [21], [22] and [23].

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