Design and Implementation of Image Capture for Cluster Housing Security System Based on IoT

The performance of IoT platforms to security systems has been implemented by some researchers in various scopes such as door, garage, and house gates. Implementing an IoT platform to the gate residential cluster is performed for entering and exiting the gate. Having an interactive system, sending an image of the visitor to the resident, and operating an automatic gate are three main features developed in this work. Using Arduino board to MATLAB and Arduino to Blynk interconnections is implemented to perform those three features. This work describes the entire process of its creation from hardware requirements, through the system's design, up to the simulation test from the running process. From the simulation test, the device can interact with the incoming visitor within 1.33 seconds on average, with the accuracy of the played voice being 100% correct, and the image sent to the 100% proper corresponding resident is done within the time taken to respond to permission granted is 1.56 seconds, while the permission denied takes 1.39 seconds.  

Occupant Privacy Perception, Awareness, and Preferences in Smart Office Environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants' perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people's privacy preferences. The features of the collected modality define data modality features -- spatial, security, and temporal context. In contrast, personal features consist of one's awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people's privacy preferences in smart office buildings helps design more effective measures to improve people's privacy

Predictive analytics in facilities management: A pilot study for exploring environmental comfort using wireless sensors

Purpose: Advancements in wireless sensor technology and building modelling techniques have enabled facilities managers to understand the environmental performance of the workplace in more depth than ever before. However, it is unclear to what extent this data can be used to predict subjective environmental comfort. Thus, the aim of this study was to pilot test a methodological framework for integrating real-time environmental data with subjective ratings of environmental comfort. Design/Methodology/Approach: An open-plan office was fitted with environmental sensors to measure key indoor environmental quality parameters (carbon dioxide, temperature, humidity, illumination, and sound pressure level). Additionally, building modelling techniques were used to calculate two spatial metrics (‘workspace integration’ and workspace density) for each workspace within the study area. 15 employees were repeatedly sampled across an 11-day study period, providing 78 momentary assessments of environmental comfort. Multilevel models were used to explore the extent to which the objective environmental data predicted subjective environmental comfort. Findings: Higher carbon dioxide levels were associated with more negative ratings of air quality, higher ‘workspace integration’ was associated with higher levels of distractions, and higher workspace density was associated with lower levels of social interactions. Originality/Value: To our knowledge, this is the first field study to directly explore the relationship between physical environment data collected using wireless sensors and subjective ratings of environmental comfort. The study provides proof-of-concept for a methodological framework for the integration of building analytics and human analytics

Ambient Intelligence for Next-Generation AR

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the Springer Handbook of the Metavers