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Abstract-Developmentof location estimation algorithms with improvement in location precision with lower cost, less energy consumption and less hardware support has become more important for many applications in Wireless Sensor Networks (WSNs). This paper addresses the problem of secure location determination, known as secure localization in WSNs using voting based technique which gives a search region in presence of anchor nodes. From the obtained search region trilateration is applied to know the position of sensor nodes. To avoid the involvement of sensor nodes in further location estimation process, bilateration is applied. Experimental analysis shows that the maximum number of nodes can be localized and accurate location of a node can be determined efficiently with low estimation error. To avoid the attacks and involvement of malicious nodes in the localization process, we implement an improved authentication and security algorithm. Using few location reference points in the localization process reduces the communication cost. The proposed scheme also provides very good localization accuracy.

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Power quality disturbances (PQD) degrades the quality of power. Detection of these PQDs in real time using smart systems connected to the power grid is a challenge due to the integration of energy generation units and electronic devices. Deep learning methods have shown advantages for PQD classification accurately. PQD events are non-stationary and occur at discrete events. Pre-processing of power signal using dual tree complex wavelet transform in localizing the disturbances according to time-frequency-phase information improves classification accuracy.Phase space reconstruction of complex wavelet sub bands to 2D data and use of fully connected feed forward neural network improves classification accuracy. In this work, a combination of DTCWT-PSR and FC-FFNN is used to classify different complex PSDs accurately.The proposed algorithm is evaluated for its performance considering different network configurations and the most optimum structure is developed. The classification accuracy is demonstrated to be 99.71% for complex PQDs and is suitable for real time activity with reduced complexity.