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The enforcement of the Movement Control Order to curtail the spread of COVID-19 has affected home energy consumption, especially HVAC systems. Occupancy detection and estimation have been recognized as key contributors to improving building energy efficiency. Several solutions have been proposed for the past decade to improve the precision performance of occupancy detection and estimation in the building. Environmental sensing is one of the practical solutions to detect and estimate occupants in the building during uncertain behavior. However, the literature reveals that the performance of environmental sensing is relatively poor due to the poor quality of the training dataset used in the model. This study proposed a smart sensing framework that combined camera-based and environmental sensing approaches using supervised learning to gather standard and robust datasets related to indoor occupancy that can be used for cross-validation of different machine learning algorithms in formal research. The proposed solution is tested in the living room with a prototype system integrated with various sensors using a random forest regressor, although other techniques could be easily integrated within the proposed framework. The primary implication of this study is to predict the room occupation through the use of sensors providing inputs into a model to lower energy consumption. The results indicate that the proposed solution can obtain data, process, and predict occupant presence and number with 99.3% accuracy. Additionally, to demonstrate the impact of occupant number in energy saving, one room with two zones is modeled each zone with air condition with different thermostat controller. The first zone uses IoFClime and the second zone uses modified IoFClime using a design-builder. The simulation is conducted using EnergyPlus software with the random simulation of 10 occupants and local climate data under three scenarios. The Fanger model's thermal comfort analysis shows that up to 50% and 25% energy can be saved under the first and third scenarios. [ABSTRACT FROM AUTHOR]

Currently building structure sector takes lead in energy consumption across the globe and is expected to increase by 2% every year. Consequently, being a major energy consumer, it's required to make effort for energy conservation as well. However, this energy conservation has to be in line with occupant thermal comfort. Recent advancement in the Internet of Things concepts attempts to transform residential areas into an energy-aware environment with aimed to reduce energy waste and automate home activities. To identify research trends and challenges in this area the study adopted the Kincham approach to performing review analysis. Our study reveals despite the benefit of this innovation, only resident with two or more unit building structure is mostly benefiting from this opportunity. Out of 94 studies considered 67 are proposed solutions. To find the trend of the research we classified the proposed solution into the topic domain (architecture, algorithm, platform, framework, and model) and it shows that trends moving toward proposing an algorithm to optimize energy consumption in the smart building structure. The study also performed an analytical evaluation on a proposed solution to assess their quality attributes and our evaluation reveals none of the studies considered security in their solution. Finally, the study identifies challenges that bottleneck the development and acceptance of this innovation into societies. [ABSTRACT FROM AUTHOR]

SQL injection attack (SQLIA) is one of the most severe attacks that can be used against web database driving applications. Attackers use SQLIA to get unauthorized access and perform unauthorized data modification. To combat problem of SQLIA, different researchers proposed variety of tools and methods that can be used as defense barrier between client application and database server. However, these tools and methods failed to address the whole problem of SQL injection attack, because most of the approaches are vulnerable in nature, cannot resist sophisticated attack or limited to scope of subset of SQLIA type. With regard to this different researcher proposed different approach (experimental and analytical evaluation) to evaluate the effectiveness of these existing tools based on type SQLIAs they can detect or prevent. However, none of the researcher considers evaluating these existing tool or method based on their ability to be deployed in various injection parameters or development requirements therefore, in this study Kitchenham's guidelines of performing systematic review of software for conducting our study. In this paper, we reviewed the tools and methods that are commonly used in detection and prevention of SQLIA, Finally, we analytically evaluated the reviewed tools and methods based on our experience with respect to SQIAs types and injection parameters. The evaluation result showed that most researchers focused on proposing approaches to detect and prevent SQLIAs, rather than evaluating the efficiency and effectiveness of the existing SQLIA detection and prevention tools/methods. The study also revealed that more emphasis was given by the previous studies on prevention measures than detection measures in combating problem of SQLIAs. An analysis showed that these tools and methods are developed to prevent subset of SQLIAs type and only few of them can be deployed to various injection parameters to be considered in examining SQLIAs. It further revealed that none of the tools or methods can be deployed to prevent attacks that can take advantage of second order (server side SQLIA) SQLI vulnerability. Finally, the study highlights the major challenges that require immediate response by developers and researchers in order to prevent the risk of being hacked through SQLIAs. [ABSTRACT FROM AUTHOR]