Large commercial buildings are complex cyber-physical systems containing expensive and critical equipment that ensure the safety and comfort of their numerous occupants. Yet occupant and visitor access to spaces and equipment within these buildings are still managed through unsystematic, inefficient, and human-intensive processes. As a standard practice, long-term building occupants are given access privileges to rooms and equipment based on their organizational roles, while visitors have to be escorted by their hosts. This approach is conservative and inflexible. In this paper, we describe a methodology that can flexibly and securely manage building access privileges for long-term occupants and short-term visitors alike, taking into account the risk associated with accessing each space within the building. Our methodology relies on blockchain smart contracts to describe, grant, audit, and revoke fine-grained permissions for building occupants and visitors, in a decentralized fashion. The smart contracts are specified through a process that leverages the information compiled from Brick and BOT models of the building. We illustrate the proposed method through a typical application scenario in the context of a real office building and argue that it can greatly reduce the administration overhead, while, at the same time, providing fine-grained, auditable access control. CCS Concepts: • Security and privacy → Security services; • Computer systems organization → Embedded and cyber-physical systems; Sensors and actuators.
During the COVID-19 pandemic, regulations on building usage and occupancy density were brought to the forefront, as research indicated that transmission was most likely to occur in indoor environments. Public health officials and building managers had to decide how to best use their buildings while curtailing the infection risk for their occupants.
In this article, we present a systematic simulation-based methodology for estimating the infection risk for a building’s occupants under different scenarios of building usage. We have evaluated our simulations against some real-world building usage data from a university campus building; our experiments demonstrate the realism of our simulations. Based on this finding, we have developed a virus transmission model that estimates the potential infection transmission risk given the behaviors of a building’s occupants. Our methodology enables building managers to simulate alternative building usage scenarios and estimate their relative infection transmission risk. We argue that such risk estimate comparisons can be useful in making decision about alternative building usage options.
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