Indoor airborne transmission poses a significant health risk, yet current monitoring solutions are invasive, costly, or fail to address it directly. My research explores the untapped potential of ambient audio sensing to estimate key transmission risk factors such as ventilation, aerosol emissions, and occupant distribution—non-invasively and in real time. I develop privacy-preserving systems that leverage existing microphones to monitor the whole spectrum of indoor air quality which can have a significant effect on an individual's health. This work lays the foundation for privacy-aware airborne risk monitoring using everyday devices.

Voice-based authentication is widely used for secure access, yet it remains vulnerable to replay attacks. We present a lightweight privacy-aware liveness detection system leveraging acoustic feature engineering to distinguish genuine voices from spoofed attempts. Using the ASVspoof 2017 dataset, our method achieves EER of 16.2% while being faster than existing DNN-based liveness detection models. We show that higher order formant frequencies, reverberation, and group delay play a crucial role in liveness detection. Our approach provides a privacy-conscious method for preventing attacks on voice-based authentication systems, so that security does not come at the cost of privacy.

EEG signals contain highly sensitive information about an individual's mental state, cognitive processes, and health conditions, making privacy preservation crucial. With the rise of commercial headwear capable of capturing EEG signals, developing robust mechanisms for ensuring privacy of such data is imperative. This work aims to protect EEG data privacy in cloud-based processing systems by sending intermediate output after neural network layer splitting to the cloud. We propose a novel holistic Combined Privacy Metric (CPM) that quantifies privacy leakage between raw EEG signals and intermediate outputs. Our study focuses on EEG-based seizure detection using a 1D CNN architecture, achieving accuracy of 96.25%. We evaluate various splitting configurations to optimize the trade-off between privacy preservation and computational efficiency. We find that splitting after the second convolutional layer achieves a CPM of 0.82 with a modest client-side model size of 509kB. This approach significantly enhances EEG data privacy while enabling effective cloud-based analysis, potentially facilitating wider adoption of secure EEG technologies in healthcare and research applications.

Bathymetry mapping of static water bodies like lakes is essential for sustainable ecosystem development strategies. However, bathymetry mapping using (i) traditional manual sampling approaches using single-beam echosounder (SBE) has large mapping errors and (ii) performing multi-beam survey is very expensive. Alternatively, performing lawn-mower SBE surveys is time consuming due to limited field-of-view. In order to address the above issues, in this paper, we present an on-the-way sampling approach with Gaussian Process Upper Confidence Bound (GPUCB) algorithm called as OAS-GPUCB that can adaptively sample the lake to minimize the bathymetry error while reducing the distance travelled to achieve a given mapping accuracy. We validate the proposed approach using simulations on actual lake bathymetry maps and also carry out real-world experiments using an Autonomous Surface Vehicle(ASV) with SBE. Further, we compare OAS-GPUCB to lawn-mower, GPUCB, and GPUCB with fixed radius approaches. The results consistently show that the proposed approach can achieve less than 10% bathymetry error while achieving distance reduction of more than 55%compared to the lawn-mower approach, and more than 90% less distance travelled compared to GPUCB and GPUCB with fixed radius approaches. The results shows the general applicability of OAS-GPUCB for bathymetry mapping of water bodies without any prior information maps.

Drones represent a significant technological shift at the convergence of on-demand cyber-physical systems and edge intelligence. However, realizing their full potential necessitates managing the limited energy resources carefully. Prior work looks at factors such as battery characteristics, intelligent edge sensing considerations, planning, and robustness in isolation. But a global view of energy awareness that considers these factors and looks at various tradeoffs is essential. To this end, we present results from our detailed empirical study of battery charge-discharge characteristics and the impact of altitude and lighting on edge inference accuracy. Our energy models, derived from these observations, predict energy usage while performing various manoeuvres with an error of 5.6%, a 2.5X improvement over the state-of-the-art. Furthermore, we propose a holistic energy-aware multi-drone scheduling system that decreases the energy consumed by 21.14% and the mission times by 46.91% over state-of-the-art baselines. To achieve system robustness in the event of link or drone failure, we observe trends in Packet Delivery Ratio to propose a methodology to establish reliable communication between nodes. We release an open-source implementation of our system. Finally, we tie all of these pieces together using a people-counting case study.