In large-scale Wireless Sensor Networks (WSNs) the amount of data gathered require energy efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensed data are sent to a sensor node whose name is associated with sensed data. Due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. In this paper we propose a Secure Data Centric Sensor (SDCS) Networks that includes security and privacy support to DCS networks. In addition, we propose a multi-query optimization technique that aggregates similar queries and reduces the number of messages. Simulation and experimental results show that our work provides a secure data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works.
Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone.
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