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In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.

In the recent years, wireless sensor networks (WSNs) are treated as one of the emerging fields in the network communication. In WSNs, routing protocols are considered as the major issue and it is framed as the NP hard problem. Though, many routing mechanism are proposed, still there are some research issues to be addressed. In this paper, we are considering the reduction of consumption as the objective function for the proposed routing protocol. The proposed routing protocol considers the Quality value (Q-Value) of the nodes for packet forwarding. The Q-value is calculated based on the battery life and cost of the node. Based on 1058 T. Sunil Kumar Reddy et al. the Q-value, the cluster head (CH) is selected automatically. The proposed protocol is evaluated based on the objectives such as network lifetime, packet delivery and energy consumption.

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