Smart home technology implementation remains an essential aspect of Internet of Things (IoT). It provides needed living support and convenience for elderly people in society. Despite the remarkable achievements in smart home monitoring technology studies, a systematic literature review (SLR) on smart home technology implementation is lacking. There is a limited number of SLR studies on smart home monitoring technology. Therefore, the current study assesses the literature to collect the evidence regarding studies on smart home monitoring technology implantation. An SLR method involving a manual search was applied to review the articles published from January 2010 to December 2019. To carefully classify these articles accordingly, we applied certain quality assessment criteria. Out of 73 relevant primary studies, only 3% were identified to have applied comprehensive SLR guidelines. Seven percent of the relevant studies were identified to have applied the SLR guidelines in a strongly moderately acceptable manner, but they were not completely comprehensive, whereas 8% of the primary studies applied the SLR guideline in a fairly acceptable manner. This manuscript therefore encourages researchers working on SLR studies on smart home technology to apply comprehensive SLR guidelines that takes into account the quality standard. INDEX TERMS Internet of Things, smart home technology, elderly people, systematic literature review, monitoring technologies.

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Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses. This research study draws upon the literature in the areas of information security, incident response, theory of planned behaviour, and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation. The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore's history to-date, the 2018 SingHealth data breach. The single in-depth case study observed information security awareness, policy, experience, attitude, subjective norms, perceived behavioral control, threat appraisal and self-efficacy as emerging prominently in the framework's applicability in incident handling. The data analysis did not support threat severity relationship with conscious care behaviour. The findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.

The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world's population and carries no specific treatment. Early and accurate detection of spread in affected regions can save precious lives. Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams. However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are required. This research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache Hadoop. The method consists of two main parts: the first part collects data from social media using Apache Flume, while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache Hive. To overcome the noisy and unstructured nature of the data, the process of extracting information is characterized by pre and post-filtration phases. As a result, only with the integration of Flume and Hive with filtration and polarity analysis, can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social network. We introduce how the Apache Hadoop ecosystem -Flume and Hivecan provide a sentiment analysis capability by storing and processing large amounts of data. An important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.

SecureCloud is a comprehensive security framework for cloud computing environments consisting of different modules that handle the security and trust issues of key components. One of the modules is responsible for authentication and identity management (IDM). In SecureCloud, the author suggest, to implement the security solution for the Authentication and IDM module, it is important to ensure IDM services in the cloud can be integrated with the existing IDM framework. Existing techniques for use of pseudonyms and accommodating multiple identities to protect users' privacy can help build a desired usercentric federated IDM for clouds. Motivated by attempting to find a solution from the suggestion, this paper proposes the integration of SWIFT identity management framework into the module using the existing trust relationships in SecureCloud to enhance the security of the SecureCloud framework. SWIFT provides a set of advanced security features such as identity aggregation, privacy protection, advanced access control and single sign-on (SSO) to help enhance the security of IDM module in SecureCloud.