In smart spaces, data flows from sensors through data processing pipelines that interpret and enrich it to realize the needs of diverse applications. Smart space data may also be stored for future analysis and processing to implement new …
Buildings account for a significant share of global energy consumption, with Heating, Ventilation, and Air Conditioning (HVAC) systems being responsible for up to 60% of a building's energy usage. For this purpose, existing scheduling and control …
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 …
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 …
With the proliferation of digital transactions, trust is becoming increasingly important, as exemplified by the World Economic Forum’s Data Free Flow with Trust. Digital signatures are utilized to establish trust to prevent spoofing and unauthorized …
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on a privacy-invasive collection of …
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 …
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 …