arXiv subjects

Vaishnavi Ranganathan

Publications and source records attributed to Vaishnavi Ranganathan.

5 recordsLinked to original sources

arXiv2026

Listening Alone, Understanding Together: Collaborative Context Recovery for Privacy-Aware AI

We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI. As agents evolve from reactive to always-listening assistants, they face a core privacy risk (of capturing non-consenting speakers), which makes their social deployment a challenge. To overcome this, we implement CONCORD, which enforces owner-only speech capture via real-time speaker verification, producing a one-sided transcript that incurs missing context but preserves privacy. We demonstrate that CONCORD can safely recover necessary context through (1) spatio-temporal context resolution, (2) information gap detection, and (3) minimal A2A queries governed by a relationship-aware disclosure. Instead of hallucination-prone inferring, CONCORD treats context recovery as a negotiated safe exchange between assistants. Across a multi-domain dialogue dataset, CONCORD achieves 91.4% recall in gap detection, 96% relationship classification accuracy, and 97% true negative rate in privacy-sensitive disclosure decisions. By reframing always-listening AI as a coordination problem between privacy-preserving agents, CONCORD offers a practical path toward socially deployable proactive conversational agents.

cs.AI↗

arXiv2025

A Single-Chain Backscatter Tag for Multi-Sensor Multiplexing

Many real-world sensing tasks require co-located, multi-modal measurements at a single site, typically a bundle of two to five sensors, for example, in plant stress sensing and blood pressure estimation. RF-backscatter devices have emerged as a low-power solution for sensing, yet existing backscatter tags support a single sensor. Placing several single-sensor tags at one site increases attachment footprint and induces mutual coupling between nearby tag antennas, thereby limiting practical deployment. We present MATRIX, a single-chain multi-sensor backscatter tag that concurrently supports multiple onboard sensors and multiplexes them as a composite voltage, then backscatters it through one analog modulation chain. Rather than time-division polling, which introduces inter-sensor sampling offsets, or frequency-division, which requires independent per-sensor modulation chains, MATRIX introduces a voltage-division multiplexing architecture in which each sensor value is encoded as a PWM waveform, carrying the measurement in its duty cycle and reserving the amplitude for multiplexing. To support reliable demultiplexing, MATRIX selects the voltage-division weights in a binary-weighted geometric progression so that every active-sensor set maps to a uniquely invertible, well-spaced composite voltage. The composite voltage is then converted into backscatter frequency shifts through a single modulation chain. At the receiver, MATRIX formulates demultiplexing as a Hidden Markov Model to recover per-sensor readings while tolerating analog hardware imperfections and multipath. MATRIX's ASIC design consumes 25.56uW. Detailed evaluation shows that the prototype, multiplexing five sensors, achieves 20 dB average signal reconstruction SNR at a 30 kHz sampling frequency; we further validate MATRIX with case studies in plant sensing, health monitoring, and microphone-based direction finding.

cs.NI↗

arXiv2025

TerraTrace: Temporal Signature Land Use Mapping System

Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards from forests. We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on plant photosynthesis, have unique temporal signatures that reflect agricultural practices and seasonal cycles. We analyze yearly NDVI changes on 20 farms for 10 unique crops. Initial results show that NDVI curves are coherent with agricultural practices, are unique to each crop, consistent globally, and can differentiate farms from forests. We develop a novel longitudinal NDVI dataset for the state of California from 2020-2023 with 500~m resolution and over 70 million points. We use this to develop the TerraTrace platform, an end-to-end analytic tool that classifies land use using NDVI signatures and allows users to query the system through an LLM chatbot and graphical interface.

eess.IV↗

arXiv2023

Cellular LTE and Solar Energy Harvesting for Long-Term, Reliable Urban Sensor Networks: Challenges and Opportunities

In a world driven by data, cities are increasingly interested in deploying networks of smart city devices for urban and environmental monitoring. To be successful, these networks must be reliable, scalable, real-time, low-cost, and easy to install and maintain -- criteria that are all significantly affected by the design choices around connectivity and power. LTE networks and solar energy can seemingly both satisfy the necessary criteria and are often used in real-world sensor network deployments. However, there have not been extensive real-world studies to examine how well such networks perform and the challenges they encounter in urban settings over long periods. In this work, we analyze the performance of a stationary 118-node LTE-connected, solar-powered sensor network over one year in Chicago. Results show the promise of LTE networks and solar panels for city-wide IoT deployments, but also reveal areas for improvement. Notably, we find 11 sites with inadequate RSS to support sensing nodes and over 33,000 hours of data loss due to solar energy availability issues between October and March. Furthermore, we discover that the neighborhoods most affected by connectivity and charging issues are socioeconomically disadvantaged areas with a majority Black and Latine residents. This work presents observations from a networking and powering perspective of the urban sensor network to help drive reliable, scalable future smart city deployments. The work also analyzes the impact of land use, adaptive energy harvesting management strategies, and shortcomings of open data, to support the need for increased real-world deployments that ensure the design of equitable smart city networks.

cs.NI↗

arXiv2021

Towards a Cost vs. Quality Sweet Spot for Monitoring Networks

Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor -- in terms of collection, storage and analysis -- by judiciously controlling how much and which measurements we collect. By positing that we can treat almost all measured signals as sampled time-series, we show that we can use signal processing techniques such as the Nyquist-Shannon theorem to avoid wasteful data collection. We show that large savings appear possible by analyzing tens of popular measurements from a production datacenter network. We also discuss the technical challenges that must be solved when applying these techniques in practice.

cs.NI↗