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Providing Occupancy as a Service with Databox

Published: 04 November 2018 Publication History

Abstract

Occupancy modelling for efficient energy management of indoor spaces has gained significant recent attention. Unfortunately, many such models rely on copying sensor data to the cloud for third-party services to process, creating risks of privacy breach. Such matters have become particularly pertinent for companies handling data of EU citizens due to provisions of the General Data Protection Regulation (GDPR). In this paper we present an implementation of "Occupancy-as-a-Service" (OaaS) at the edge, inverting the usual model: rather than ship data to the cloud to be processed, we retain data where it is generated and compute on it locally. This effectively avoids many risks associated with moving personal data to the cloud, and increases the agency of data subjects in managing their personal data. We describe the Databox architecture, its core components, and the OaaS functionality. As well as improving the privacy of the occupants, our approach allows us to offer occupancy data to other applications running on Databox, at a granularity that is not constrained by network usage, storage or processing restrictions imposed by third-party services, but is under data subject control.

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Cited By

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  • (2024)CerberusProceedings of the 7th International Workshop on Edge Systems, Analytics and Networking10.1145/3642968.3654817(25-30)Online publication date: 22-Apr-2024
  • (2023)Personal Data Stores (PDS): A ReviewSensors10.3390/s2303147723:3(1477)Online publication date: 28-Jan-2023
  • (2019)Enforcing accountability in Smart built-in IoT environment using MUDProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3361004(368-369)Online publication date: 13-Nov-2019

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cover image ACM Conferences
CitiFog'18: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing
November 2018
47 pages
ISBN:9781450360517
DOI:10.1145/3277893
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 04 November 2018

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Author Tags

  1. Indoor Occupancy
  2. IoT Middleware
  3. Personal Data Management

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Cited By

View all
  • (2024)CerberusProceedings of the 7th International Workshop on Edge Systems, Analytics and Networking10.1145/3642968.3654817(25-30)Online publication date: 22-Apr-2024
  • (2023)Personal Data Stores (PDS): A ReviewSensors10.3390/s2303147723:3(1477)Online publication date: 28-Jan-2023
  • (2019)Enforcing accountability in Smart built-in IoT environment using MUDProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3361004(368-369)Online publication date: 13-Nov-2019

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