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

Impact of COVID-19 on iot adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.Comment: This is the version accepted at Sensors 202

Human-building interaction towards a sustainable built environment: A review

Human Building Interaction (HBI), a recently introduced emerging area, can be used for various purposes, including the development of better designs, constructions, and operations, as well as the support of building managers and occupants in meeting their goals. The expanding community of HBI researchers seeks to investigate the future of HBI research and design for an interactive built environment. Building managers and owners strive for energy-efficient, sustainable, and more livable buildings to improve and become 'smart.' Diverse buildings and urban spaces are individually designed and outfitted with various systems, components, and accessories. With the advent of the Internet of Things (IoT), these devices form a network of internet-connected 'things' that generate massive amounts of data. We can collect vast volumes of data in unprecedented numbers, providing critical insights that allow buildings to care for us by learning from acquired data and adjusting to our requirements. This paper contributes to HBI by surveying various efforts to interact with buildings using IoT sensors and interconnected things to gain useful insights. Buildings, in our perspective, have distinct personalities and obligations to achieve their objectives. So, we are trying to incorporate them into reality. Considering a building to be a bio-inspired living architecture, we compare human anatomy to building anatomy to understand better the functions and operations that buildings can perform in their built environment. Thinking from this outlook allows us to investigate how sensors can help us achieve such building sustainability standards and what operations they perform to create an interactive built environment. This review paper aims to investigate the role of sensors in particular and to what extent they can provide various useful insights to building occupants and users to meet sustainability standards. We examine the most recent work on how people engage with and interact with buildings via various interfaces to achieve sustainability goals. Finally, some domain-specific challenges that limit human engagement and interactions with the built environment are discussed

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out

ACUTA Journal of Telecommunications in Higher Education

In This Issue Network Security: An Achilles Heel for Organizations of All Sizes Providing Backup in a VolP World Security Concerns Shift lnward Cell Phones, Land Lines, and E911 Security Checklists Higher Ed\u27s Tricky Equation: Directories Help Balance Availability with Security Disaster Recovery Planning Essentials Passing the Test of productivity Interview President\u27s Message From the Executive Director Here\u27s My Advic

Threat modeling in smart firefighting systems: aligning MITRE ATT&CK Matrix and NIST security controls

Industrial automation technologies are envisioned as multi-device systems that are constantly interacting with one another and with enterprise systems. In these industrial systems, the industrial internet of things (IIoT) significantly improves system efficiency, scalability, ease of control, and monitoring. These benefits have been achieved at the cost of greater security risks, thus making the system vulnerable to cyberattacks. Historically, industrial networks and systems lacked security features like authentication and encryption due to intended isolation over the Internet. Lately, remote access to these IIoT systems has made an attempt of holistic security alarmingly critical. In this research paper, a threat modeling framework for smart cyber–physical system (CPS) is proposed to get insight of the potential security risks. To carry out this research, the smart firefighting use case based on the MITRE ATT&CK matrix was investigated. The matrix analysis provided structure for attacks detection and mitigation, while system requirement collection (SRC) was applied to gather generic assets’ information related to hardware, software and network. With the help of SRC and MITRE ATT&CK, a threat list for the smart firefighting system was generated. Conclusively, the generated threat list was mapped on the national institute of standards and technology (NIST) security and privacy controls. The results show that these mapped controls can be well-utilized for protection and mitigation of threats in smart firefighting system. In future, critical cyber–physical systems can be modeled upon use case specific threats and can be secured by utilizing the presented framework

Energize Worcester: Smart Heating Controls in Student HMOs

This project partnered with the University of Worcester to examine the possible social implications and effects of implementing smart heating controls in student-rented houses in multiple occupation (HMOs). Through the use of surveys, our team evaluated student attitudes towards sustainability as well as the potential acceptance and effectiveness that smart heating controls could have in student off-campus housing. Our survey results identified three areas that may complicate the implementation of smart heating controls: insufficient levels of student motivation to improve energy efficiency, the apparent absence of fuel poverty in the population studied, and concerns regarding privacy. We recommend that students be educated on energy expenditure and sustainable practices