Heating, ventilation and air conditioning accounts for roughly 40–60% of a building’s energy use, yet the reason to run it at all is the comfort of the people inside. Pervasive and mobile computing makes it possible to sense individual thermal sensations and manage HVAC around them, but evaluating such systems is hard: real-world experiments are expensive, slow, privacy-sensitive and rarely cover the diversity of occupants and climates, while simulations have to be shown to reflect reality.
This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and …
Privacy policies are the main channel through which organizations tell people what happens to their data, yet they are long, intricate, and routinely skipped or misunderstood — and for smart devices they are scattered across manufacturers and e-commerce platforms, so even finding the right policy is hard.
As the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, …
This paper presents Sentaur, a middleware designed, built, and deployed to support sensor-based smart space analytical applications. Sentaur supports a powerful data model that decouples semantic data (about the application domain) from sensor data …
Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are …
This paper presents SmartSPEC, an approach to generate customizable smart space datasets with information about sensorized spaces in which people and events are em- bedded. Smart space datasets are critical to design, deploy and evaluate robust …
Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at …
This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and …