Browsing by Author "Li, Tao"

Now showing 1 - 5 of 5
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    IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System
    (IEEE, 2020-05) Li, Tao; Han, Dianqi; Chen, Yimin; Zhang, Rui; Zhang, Yanchao; Hedgpeth, Terri; Computer Information and Graphics Technology, School of Engineering and Technology
    Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.
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    Is the relationship between alexithymia and aggression context-dependent? Impact of group membership and belief similarity
    (2012-08) Konrath, Sara H.; Novin, Sheida; Li, Tao
    Previous research finds positive relationships between alexithymia and aggression. This study examined potential interpersonal factors that might elicit aggressiveness among people with high levels of alexithymia. College student participants completed the Toronto Alexithymia Scale online prior to interacting with their partners in the laboratory. Participants interacted with a partner who (i) was from their in-group versus out-group, and (ii) held similar versus different beliefs on an important topic. Results show that compared to low-alexithymic individuals, individuals with high levels of alexithymia reported increased anger after interacting with out-group members. This corresponded to increased trait aggressiveness when interacting with out-group members. No differences emerged regarding behavioral aggression. Implications for the association between alexithymia and aggression are discussed.
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    RCID: Fingerprinting Passive RFID Tags via Wideband Backscatter
    (IEEE, 2022) Li, Jiawei; Li, Ang; Han, Dianqi; Zhang, Yan; Li, Tao; Zang, Yanchao; Computer and Information Science, Purdue School of Science
    Tag cloning and spoofing pose great challenges to RFID applications. This paper presents the design and evaluation of RCID, a novel system to fingerprint RFID tags based on the unique reflection coefficient of each tag circuit. Based on a novel OFDM-based fingerprint collector, our system can quickly acquire and verify each tag’s RCID fingerprint which are independent of the RFID reader and measurement environment. Our system applies to COTS RFID tags and readers after a firmware update at the reader. Extensive prototyped experiments on 600 tags confirm that RCID is highly secure with the authentication accuracy up to 97.15% and the median authentication error rate equal to 1.49%. RCID is also highly usable because it only takes about 8 s to enroll a tag and 2 ms to verify an RCID fingerprint with a fully connected multi-class neural network. Finally, empirical studies demonstrate that the entropy of an RCID fingerprint is about 202 bits over a bandwidth of 20 MHz in contrast to the best prior result of 17 bits, thus offering strong theoretical resilience to RFID cloning and spoofing.
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    User Leaving Detection Via MMwave Imaging
    (2023-08) Xu, Jiawei; King, Brian; Li, Tao; Zhang, Qingxue
    The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user’s 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.
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    WearRF-CLA: Continuous Location Authentication with Wrist Wearables and UHF RFID
    (National Science Foundation, 2022) Li, Ang; Li, Jiawei; Han, Dianqi; Zhang, Yan; Li, Tao; Zhang, Yanchao; Computer and Information Science, Purdue School of Science
    Continuous location authentication (CLA) seeks to continuously and automatically verify the physical presence of legitimate users in a protected indoor area. CLA can play an important role in contexts where access to electrical or physical resources must be limited to physically present legitimate users. In this paper, we present WearRF-CLA, a novel CLA scheme built upon increasingly popular wrist wearables and UHF RFID systems. WearRF-CLA explores the observation that human daily routines in a protected indoor area comprise a sequence of human-states (e.g., walking and sitting) that follow predictable state transitions. Each legitimate WearRF-CLA user registers his/her RFID tag and also wrist wearable during system enrollment. After the user enters a protected area, WearRF-CLA continuously collects and processes the gyroscope data of the wrist wearable and the phase data of the RFID tag signals to verify three factors to determine the user's physical presence/absence without explicit user involvement: (1) the tag ID as in a traditional RFID authentication system, (2) the validity of the human-state chain, and (3) the continuous coexistence of the paired wrist wearable and RFID tag with the user. The user passes CLA if and only if all three factors can be validated. Extensive user experiments on commodity smartwatches and UHF RFID devices confirm the very high security and low authentication latency of WearRF-CLA.