Internet of Things

GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants

Website privacy policies are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user-friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants …

Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 40% to 50% of energy usage in commercial buildings. Thus, innovative ways to control and manage HVAC systems while preserving occupants' comfort are required. State-of-the-art …

DEMSA: a DT-enabled Middleware for Self-adaptive Smart Spaces

Heating, Ventilation, and Air Conditioning (HVAC) systems account for a significant portion of energy consumption within buildings. In order to balance the effect of thermal comfort vis-a-vis energy savings, HVAC control strategies have been …

SmartSPEC: A framework to generate customizable, semantics-based smart space datasets

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 …

PrivacyLens

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.

Sentaur: Sensor Observable Data Model for Smart Spaces

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

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:

The SemIoTic Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces

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 …

SmartSPEC: Customizable Smart Space Datasets via Event-Driven Simulations

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 …

JENNER: Just-in-time Enrichment in Query Processing

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 …