skip to main content


Title: Notification Control and Reminders with UWB Indoor Localization
Localization based context awareness in mobile phones can enable several conveniences for users. This demonstration explores a way to smartly control notification and "Do not disturb" settings for the mobile phones based on the user's indoor location. Furthermore, users can setup location-based reminders which pop-up on the mobile phone when the user visits a specific indoor location. While enabling full-scale indoor localization might be challenging, we use just a few UWB beacons placed strategically, say embedded inside home-assistant devices, Wi-Fi routers, etc. and a UWB enabled phone to provide the required context awareness. Video: https://www.youtube.com/shorts/MbBwPw0BIJU  more » « less
Award ID(s):
2145278
PAR ID:
10409069
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
HotMobile '23: Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications
Page Range / eLocation ID:
146 to 146
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework calledFedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy.FedHILintegrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show thatFedHILoutperforms state-of-the-art FL and non-FL indoor localization frameworks.FedHILis able to achieve 1.62 × better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.

     
    more » « less
  2. There are a wide variety of mobile phone emergency response applications exist for both indoor and outdoor environments. However, outdoor applications mostly provide accident and navigation information to users, and indoor applications are limited to the unavailability of GPS positioning and WiFi access problems. This paper describes the proposed mobile augmented reality system (MARS) that allows both outdoor and indoor users to retrieve and manage information for emergency response and navigation that is spatially registered with the real world. The proposed MARS utilizes feature extraction for location sensing in indoor environments as during emergencies GPS and WiFi systems might not work. This paper describes the implementation of this MARS deployed on tablets and smartphones for building evacuation purposes. The MARS delivers critical evacuation information to smartphone users in the indoor environment and navigation information in the outdoor environments. A limited user study was conducted to test the effectiveness of the proposed MARS using the mobile phone usability questionnaire (MPUQ) framework. The results show that AR features were well integrated into the MARS and it will help identify the nearest exit in the building during the emergency evacuation. 
    more » « less
  3. One of the biggest challenges that Universities face today is the safety of its people on campus from crimes like mugging, battery and even shooting in or around the campus area. Using SJSU campus as an example, over 50 alert cases of burglaries, thefts, batteries, sexual assaults and other incidents have been reported in and around the SJSU campus over the last year. We have Bluelight emergency telephones placed all over the campus, in all buildings, elevators and on the campus grounds. These phones can be used to report emergency situations, suspicious activities, request escorts etc. However, there is a huge delay between the occurrence of incidents and the arrival of the policeman at the site. There is a critical need for a system that would allow the authorities to locate victims and respond faster to these incidents. To reduce the delay in reporting incidents and their occurrence time, we have developed a mobile application that will let users send alerts along with their real-time location to the UPD directly from their mobile phones. However, finding the position of a victim in a building is the most important challenge we are facing. Many existing systems do not work in indoor environment, and the state-of-the-art localization systems are either inconvenience to use or inaccurate enough to pin-point user's locations inside the building. In this paper, we propose a fine-grained location-aware smart campus security systems that leverages hybrid localization approaches with minimum deployment cost. Specifically, we effectively combines the Wi-Fi fingerprinting localization approach with the Bluetooth beacon based trilateration approach, and improves the location accuracy to the meter-level with low cost. 
    more » « less
  4. Location-based services have the potentials to change how we interact with the places and things around us. UWB indoor localization is one of the most successful enabling technologies that has achieved decimeter accuracy with robustness against complex indoor multipath environments. In this work, we demonstrate a system that not only achieves high localization accuracy, but also supports infinite scalability, full user privacy, and plug-and-play infrastructure deployment, which brings localization closer to a universal and pervasive technology. 
    more » « less
  5. null (Ed.)
    In this paper, we propose that the theory of planned behavior (TPB) with the additional factors of awareness and context-based information can be used to positively influence users' cybersecurity behavior. A research model based on TPB is developed and validated using a user study. As a proof-of-concept, we developed a mobile cybersecurity news app that incorporates context-based information such as location, search history, and usage information of other mobile apps into its article recommendations and warning notifications to address user awareness better. Through a survey of 100 participants, the proposed research model was validated, and it was confirmed that context-based information positively influences users' awareness in cybersecurity. 
    more » « less