The modern day advancement is increasingly digitizing our lives which has led to a rapid growth of data. Such multidimensional datasets are precious due to the potential of unearthing new knowledge and developing decision-making insights from them. Analyzing this huge amount of data from multiple sources can help organizations to plan for the future and anticipate changing market trends and customer requirements. While the Hadoop framework is a popular platform for processing larger datasets, there are a number of other computing infrastructures, available to use in various application domains. The primary focus of the study is how to classify major big data resource management systems in the context of cloud computing environment. We identify some key features which characterize big data frameworks as well as their associated challenges and issues. We use various evaluation metrics from different aspects to identify usage scenarios of these platforms. The study came up with some interesting findings which are in contradiction with the available literature on the Internet.

This paper mainly focuses on the controlling of home appliances remotely and providing security when the user is away from the place. The system is SMS based and uses wireless technology to revolutionize the standards of living. This system provides ideal solution to the problems faced by home owners in daily life. The system is wireless therefore more adaptable and cost-effective. The HACS system provides security against intrusion as well as automates various home appliances using SMS. The system uses GSM technology thus providing ubiquitous access to the system for security and automated appliance control.

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Wireless Body Area Network (WBAN) is a special purpose wireless sensors network designed to connect various self-autonomous medical sensors and appliances located inside and outside of human body. Interests in human Healthcare Monitoring System (HMS) are based on WBAN due to the increasing aging population and chronically ill patients at home. HMS is expected to reduce healthcare expenses by enabling the continuous monitoring of patient's health remotely in daily life activities. This research focuses on routing protocols in WBAN. The major problems in routing protocols are maximum energy consumption, path loss ratio, packet delivery ratio and maintaining stable signal to noise ratio. Real time analysis is required in HMS to support the patients through doctors, caregivers and hospital systems. Collected data is relayed by using existing wireless communication schemes towards the access point for further retransmission and processing. In this research, an Improved Quality of Service aware Routing Protocol (IM-QRP) is proposed for WBAN based HMS to remotely monitor the elderly people or chronically ill patients in hospitals and residential environments. The proposed protocol is capable to improve 10% residual energy, 30% reduction in path loss ratio, 10% improvement in packet transmission (link reliability) and 7% improvement in SNR as compared to existing CO-LEEBA and QPRD routing protocols. Convolutional Neural Network is used outside the WBAN environment to analyze the medical health records for healthcare diagnosis and intelligent decision-making.