Phd in COMPUTER SCIENCE

Thesis title: Pervasive Presence: Passive Detection and Active Guidance of People

Privacy-preserving pervasive computing requires context-awareness mechanisms that operate without dedicated infrastructure, intrusive user interaction, or the collection of personally identifiable information. This dissertation addresses this challenge \textit{by proposing a multi-layer framework} based on passive Bluetooth Low Energy (BLE) sensing on commodity smartphones, enabling privacy-conscious presence monitoring, indoor context awareness, and intelligent mobility support. The first contribution investigates Indoor Occupancy Estimation through passive BLE sensing. The proposed framework addresses three fundamental challenges that limit practical deployment: the multi-device phenomenon, cross-room signal propagation, and persistent non-human wireless emitters. A processing pipeline combining temporal aggregation, Received Signal Strength Indicator (RSSI)-based filtering, automatic environmental baseline estimation, and context-aware semantic calibration enables accurate occupancy estimation without requiring dedicated sensing infrastructure or active user enrollment. Extensive multi-week deployments across heterogeneous indoor environments demonstrate a significant improvement over naïve counting approaches while preserving user privacy through a zero Media Access Control (MAC)-address storage policy. The second contribution extends the same passive sensing paradigm to Intelligent Transportation Systems (ITS) by exploiting variations in the surrounding wireless environment to infer transportation modes. High-level statistical features extracted from opportunistic BLE observations are processed by an on-device machine learning pipeline that reliably distinguishes major urban mobility modalities, including walking, private vehicles, buses, trams, and subway travel. Experimental validation on real-world commuting data demonstrates the effectiveness of the proposed approach while maintaining infrastructure independence and edge-based processing. The third contribution expands the research from passive context perception to Active Spatial Guidance in Global Positioning System (GPS)-denied environments. This work investigates the integration of generative AI, memory-grounded reasoning, and structured human-computer interaction to support indoor navigation through technical building layouts. The proposed approach combines standardized user input derived from evacuation-map semantics with interactive spatial reasoning, enabling reliable zero-shot visual localization and familiarity-aware route planning. This research represents a natural extension of the dissertation's overarching objective of transforming passive environmental awareness into intelligent user assistance. Beyond these core contributions, the dissertation presents several complementary collaborative studies that further explore the broader design space of pervasive and ubiquitous computing. Overall, this dissertation demonstrates how passive wireless sensing, privacy-preserving software engineering, and AI-driven interaction techniques can be combined into a coherent framework for context-aware systems operating without specialized infrastructure. The proposed methods contribute practical and scalable solutions that advance the state of the art in pervasive computing, indoor intelligence, and intelligent mobility while preserving user privacy by design.