Publications

2022

1. Percom

   Prototyping Opportunistic Learning in Resource Constrained Mobile Devices

   Yu, Haoxiang, Chen, Hsiao-Yuan, Lee, Sangsu, Zheng, Xi, and Julien, Christine

   In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) 2022
2. Percom

   Swarm: Playground for Large-scale Decentralized Learning Simulations

   Lee, Sangsu, Yu, Haoxiang, Zheng, Xi, and Julien, Christine

   In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) 2022

2021

1. COMPSAC

   A Transfer Learning Approach to Surface Detection for Accessible Routing for Wheelchair Users

   Mokrenko, Valeria, Yu, Haoxiang, Raychoudhury, Vaskar, Edinger, Janick, Smith, Roger O, and Gani, Md Osman

   In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) 2021
2. MobiQuitous

   Dynamic Taxi Ride-Sharing through Adaptive Request Propagation using Regional Taxi Demand and Supply,

   Yu, Haoxiang, Raychoudhury, Vaskar, and Saha, Snehanshu

   In 18th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (MobiQuitous) 2021
3. Master Thesis

   A Comprehensive System for Dynamic and Distributed Taxi Ride-Sharing via Localized Communication

   Yu, Haoxiang

   2021
4. BuildSys

   Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices

   Yu, Haoxiang, Hua, Jie, and Julien, Christine

   2021

   Abs

   With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the "smartness" of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: "What kinds of behaviors do humans expect from their IoT devices?" The dataset we collected contains the basic information of the IFTTT rules, trigger and action events, and how many people are using each rule.

2020

1. ICDCN

   Dynamic taxi ride sharing using localized communication

   Yu, Haoxiang, Raychoudhury, Vaskar, and Silwal, Shrawani

   In Proceedings of the 21st International Conference on Distributed Computing and Networking 2020