Dustin Maas

Anirudh Kamath, Dustin Maas, Jacobus Van der Merwe et al.

Data wrangling continues to be the most time-consuming task in the data science pipeline and wireless network data is no exception. Prior approaches for automatic or assisted data-wrangling primarily target unordered, single-table data. However, unlike traditional datasets where rows in a table are unordered and assumed to be independent of each other, wireless network datasets are often collected across multiple measurement devices, producing multiple, temporally ordered tables that must be integrated for obtaining the complete dataset. For instance, to create a dataset of the signal quality of 5G cell towers within a geographic region, GPS data collected by cellphones must be joined with radio frequency measurements of the corresponding cell towers. However, the join key timestamp typically exhibits mismatched sampling periods, causing a misalignment. Data wrangling techniques for generic time-series datasets also fail here, since they lack knowledge of domain-specific data semantics, which are often defined by network protocols and system configurations. To aid in wrangling wireless network datasets, we demonstrate WN-Wrangle, an interactive wrangling assistant, tailored to the wireless network domain that suggests the top-k next-best wrangling operations, along with rich, domain-specific explanations. Under the hood, WN-Wrangle enforces temporal constraints- and a wireless network semantics-aware mechanism to score and rank an extended set of wrangling operators to improve the data quality. We demonstrate how WN-Wrangle identifies elusive data-quality issues specific to the wireless network domain and suggests accurate wrangling steps over datasets obtained from the widely used POWDER city-scale wireless testbed.

Arijit Banerjee, Dustin Maas, Maurizio Bocca et al.

We introduce and investigate the ability of an attacker to surreptitiously use an otherwise secure wireless network to detect moving people through walls, in an area in which people expect their location to be private. We call this attack on location privacy of people an "exploiting radio windows" (ERW) attack. We design and implement the ERW attack methodology for through wall people localization that relies on reliably detecting when people cross the link lines by using physical layer measurements between the legitimate transmitters and the attack receivers. We also develop a method to estimate the direction of movement of a person from the sequence of link lines crossed during a short time interval. Additionally, we describe how an attacker may estimate any artificial changes in transmit power (used as a countermeasure), compensate for these power changes using measurements from sufficient number of links, and still detect line crossings. We implement our methodology on WiFi and ZigBee nodes and experimentally evaluate the ERW attack by monitoring people movements through walls in two real-world settings. We find that our methods achieve very high accuracy in detecting line crossings and determining direction of motion.