arXiv subjects

Amee Trivedi

Publications and source records attributed to Amee Trivedi.

10 recordsLinked to original sources

arXiv2023

Path Planning Under Uncertainty to Localize mmWave Sources

In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (~300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

cs.RO↗

arXiv2022

WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing

Sleep deprivation is a public health concern that significantly impacts one's well-being and performance. Sleep is an intimate experience, and state-of-the-art sleep monitoring solutions are highly-personalized to individual users. With a motivation to expand sleep monitoring capabilities at a large scale and contribute sleep data to public health understanding, we present Wisleep, a system for inferring sleep duration using smartphone network connections that are passively sensed from WiFi infrastructure. We propose an unsupervised ensemble model of Bayesian change point detection, validating it over a user study among 20 students living in campus dormitories and a private home. Our results find Wisleep outperforming prior techniques for users with irregular sleep patterns while yielding an average 88.50% accuracy within 60 minutes sleep time error and 39 minutes wake-up time error. This is comparable to client-side methods, albeit utilizing coarse-grained information. Additionally, we utilize our approach to predict sleep and wake-up times from a user study of more than 1000 student users, demonstrating results similar to prior findings on students' sleep patterns. Finally, we show that Wisleep can process data from twenty thousand users on a single commodity server, allowing it to scale to large campus populations with low server requirements.

eess.SP↗

arXiv2022

Analyzing the Impact of Covid-19 Control Policies on Campus Occupancy and Mobility via Passive WiFi Sensing

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies. These solutions include, among other efforts, enforcing social distancing and monitoring crowd movements in indoor spaces. However, such solutions may not be effective without mass adoption. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. This paper conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, our analysis demonstrates how coarse-grained WiFi data can sufficiently reflect indoor occupancy spectrum when different COVID-19 policies were enacted. Our work analyzes staff and students' mobility data from three different university campuses. Two of these campuses are in Singapore, and the third is in the Northeastern United States. Our results show that online learning, split-team, and other space management policies effectively lower occupancy. However, they do not change the mobility for individuals transitioning between spaces. We demonstrate how this data source can be put to practical application for institutional crowd control and discuss the implications of our findings for policy-making.

cs.CY↗

arXiv2022

Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification

The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolution measurements. This paper explores mmWave-based positioning for a target localization problem where a fixed target broadcasts mmWave signals and a mobile robotic agent attempts to capture the signals to locate and navigate to the target. A three-stage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the multipath channel components and estimate their parameters. Second, a machine-learning trained classifier is then used to predict the link state, meaning if the strongest path is line-of-sight (LOS) or non-LOS (NLOS). For the NLOS case, the link state predictor also determines if the strongest path arrived via one or more reflections. Third, based on the link state, the agent either follows the estimated angles or uses computer vision or other sensor to explore and map the environment. The method is demonstrated on a large dataset of indoor environments supplemented with ray tracing to simulate the wireless propagation. The path estimation and link state classification are also integrated into a state-of-the-art neural simultaneous localization and mapping (SLAM) module to augment camera and LIDAR-based navigation. It is shown that the link state classifier can successfully generalize to completely new environments outside the training set. In addition, the neural-SLAM module with the wireless path estimation and link state classifier provides rapid navigation to the target, close to a baseline that knows the target location.

cs.RO↗

arXiv2021

WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing

Modeling human mobility has a wide range of applications from urban planning to simulations of disease spread. It is well known that humans spend 80% of their time indoors but modeling indoor human mobility is challenging due to three main reasons: (i) the absence of easily acquirable, reliable, low-cost indoor mobility datasets, (ii) high prediction space in modeling the frequent indoor mobility, and (iii) multi-scalar periodicity and correlations in mobility. To deal with all these challenges, we propose WiFiMod, a Transformer-based, data-driven approach that models indoor human mobility at multiple spatial scales using WiFi system logs. WiFiMod takes as input enterprise WiFi system logs to extract human mobility trajectories from smartphone digital traces. Next, for each extracted trajectory, we identify the mobility features at multiple spatial scales, macro, and micro, to design a multi-modal embedding Transformer that predicts user mobility for several hours to an entire day across multiple spatial granularities. Multi-modal embedding captures the mobility periodicity and correlations across various scales while Transformers capture long-term mobility dependencies boosting model prediction performance. This approach significantly reduces the prediction space by first predicting macro mobility, then modeling indoor scale mobility, micro-mobility, conditioned on the estimated macro mobility distribution, thereby using the topological constraint of the macro-scale. Experimental results show that WiFiMod achieves a prediction accuracy of at least 10% points higher than the current state-of-art models. Additionally, we present 3 real-world applications of WiFiMod - (i) predict high-density hot pockets for policy-making decisions for COVID19 or ILI, (ii) generate a realistic simulation of indoor mobility, (iii) design personal assistants.

cs.NI↗

arXiv2021

WiFiTrace: Network-based Contact Tracing for Infectious Diseases Using Passive WiFi Sensing

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional methods, rather than to supplant them with new technology. We designed an efficient graph algorithm to scale our approach to large networks with tens of thousands of users. The graph-based approach outperforms an indexed PostgresSQL in memory by at least 4.5X without any index update overheads or blocking. We have implemented a full prototype of our system and deployed it on two large university campuses. We validated our approach and demonstrate its efficacy using case studies and detailed experiments using real-world WiFi datasets.

cs.NI↗

arXiv2020

Digital Contact Tracing: Technologies, Shortcomings, and the Path Forward

Since the start of the COVID-19 pandemic, technology enthusiasts have pushed for digital contact tracing as a critical tool for breaking the COVID-19 transmission chains. Motivated by this push, many countries and companies have created apps that enable digital contact tracing with the goal to identify the chain of transmission from an infected individual to others and enable early quarantine. Digital contact tracing applications like AarogyaSetu in India, TraceTogether in Singapore, SwissCovid in Switzerland, and others have been downloaded hundreds of millions of times. Yet, this technology hasn't seen the impact that we envisioned at the start of the pandemic. Some countries have rolled back their apps, while others have seen low adoption. Therefore, it is prudent to ask what the technology landscape of contact-tracing looks like and what are the missing pieces. We attempt to undertake this task in this paper. We present a high-level review of technologies underlying digital contact tracing, a set of metrics that are important while evaluating different contact tracing technologies, and evaluate where the different technologies stand today on this set of metrics. Our hope is two-fold: (a) Future designers of contact tracing applications can use this review paper to understand the technology landscape, and (b) Researchers can identify and solve the missing pieces of this puzzle so that we are ready to face the rest of the COVID-19 pandemic and any future pandemics. A majority of this discussion is focused on the ability to identify contact between individuals. The questions of ethics, privacy, and security of such contact tracing are briefly mentioned but not discussed in detail.

cs.CY↗

arXiv2020

Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids

Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11-32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.

eess.SP↗

arXiv2020

Empirical Characterization of Mobility of Multi-Device Internet Users

Understanding the mobility of humans and their devices is a fundamental problem in mobile computing. While there has been much work on empirical analysis of human mobility using mobile device data, prior work has largely assumed devices to be independent and has not considered the implications of modern Internet users owning multiple mobile devices that exhibit correlated mobility patterns. Also, prior work has analyzed mobility at the spatial scale of the underlying mobile dataset and has not analyzed mobility characteristics at different spatial scales and its implications on system design. In this paper, we empirically analyze the mobility of modern Internet users owning multiple devices at multiple spatial scales using a large campus WiFi dataset. First, our results show that mobility of multiple devices belonging to a user needs to be analyzed and modeled as a group, rather than independently, and that there are substantial differences in the correlations exhibited by device trajectories across users that also need to be considered. Second, our analysis shows that the mobility of users shows different characteristics at different spatial scales such as within and across buildings. Third, we demonstrate the implications of these results by presenting generative models that highlight the importance of considering the spatial scale of mobility as well as multi-device mobility. More broadly, our empirical results point to the need for new modeling research to fully capture the nuances of mobility of modern multi-device users.

cs.CY↗

arXiv2018

Persistent Memory Transactions

This paper presents a comprehensive analysis of performance trade offs between implementation choices for transaction runtime systems on persistent memory. We compare three implementations of transaction runtimes: undo logging, redo logging, and copy-on-write. We also present a memory allocator that plugs into these runtimes. Our microbenchmark based evaluation focuses on understanding the interplay between various factors that contribute to performance differences between the three runtimes -- read/write access patterns of workloads, size of the persistence domain (portion of the memory hierarchy where the data is effectively persistent), cache locality, and transaction runtime bookkeeping overheads. No single runtime emerges as a clear winner. We confirm our analysis in more realistic settings of three "real world" applications we developed with our transactional API: (i) a key-value store we implemented from scratch, (ii) a SQLite port, and (iii) a persistified version of memcached, a popular key-value store. These findings are not only consistent with our microbenchmark analysis, but also provide additional interesting insights into other factors (e.g. effects of multithreading and synchronization) that affect application performance.

cs.DC↗