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 …
Framework aimed at discovering, collecting, and analyzing privacy policies of smart devices using NLP and ML algorithms, to provide insights to users, policy authors, and regulators.
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 …
SmartSPEC is a smart space simulator and data generator that creates customizable smart space datasets using semantic models of spaces, people, events and sensors. We employ ML-based approaches to characterize and learn attributes of the embedded people and events in a sensorized space and apply an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor datasets, etc.).
The SmartSPEC architecture consists of two main components:
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 …
We present our experiences in adapting and deploying TIPPERS, a novel privacy-enabled IoT data collection and management system for smart spaces, to facilitate the monitoring of adherence to COVID-19 regulations in a university campus and a military …