Tong Li

Tsinghua University | TH · Department of Electronic Engineering

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are...

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals' cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app...

The outbreak of Covid-19 changed the world as well as human behavior. In this paper, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on th...

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the peri...

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-...

In the intelligent transportation system, traffic forecasting, which is generally characterized as a graph spatial-temporal prediction task, plays a crucial role. It is challenging to generate reliable forecast results due to the complexity of traffic topological information and the inherent uncertainty of road traffic circumstances. Existing works...

Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consis...

Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based on tensor decomposition or graph embedding suffer from the following two major limitations: 1) ignoring the fine-grained similarities of user preferen...

Urban abnormal events constitute a significant threat to social order and public safety. It is of vital importance for emergency treatment if the location and time of abnormal events could be predicted before they happen. However, forecasting the occurrence of urban abnormal events is extremely challenging due to various influencing factors. First,...

Understanding mobile application usage patterns is significant for producing better services and enriching user experience. The understanding of spatiotemporal patterns of application usage is still limited. In this paper, we aim at finding spatiotemporal mobile app usage patterns and propose a framework to capture who, when, where, and what applic...

With the increasing diversity of mobile apps, users install many apps in their smartphones and often use several apps together to meet a specific requirement. Because of the evolution of user habits and app functions, the set of apps using at the same time, i.e., app usage context, may change over time, which represents the dynamic correlation of d...

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with a limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage ar...

Origin-Destination (OD) flow contains the information of direction and volume of population mobility between different regions in a city, having significant value in public transportation resource allocation. In this paper, we explore population distribution to infer OD flows, which is called pop2flow (population distribution to OD flows) problem....

In this paper, we investigate the feasibility of using mobility patterns and demographic data to predict hospital visits. We collect mobility traces from two thousand users for around two months. We extract 16 mobility features from these passively collected mobility traces and train an XGBoost model to predict users' hospital visits. We demonstrat...

Skeleton designs are widely seen in the robotics industry and multimedia applications such as animated films and computer games. The design of skeletons is mental-labor intensive, especially axes directions of joints are difficult even for the most experienced designers to select. In the existing works, there are auto creation of skeletons from mes...

The increasing amount of urban data enables us to investigate urban dynamics, assist urban planning, and, eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and repr...

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithm...

The current explosion of video traffic compels service providers to deploy caches at edge networks. Nowadays, most caching systems store data with a high programming voltage corresponding to the largest possible ‘expiry date’, typically on the order of years, which maximizes the cache damage. However, popular videos rarely exhibit lifecycles longer...

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recent...

Augmented reality head-worn computers often feature small-sized touch interfaces that complicate interaction with content, provide insufficient space for comfortable text input, and can be awkward to use in social situations. This paper presents a novel one-handed thumb-to-finger text entry solution for augmented reality head-worn computers. We des...

Urban anomalies such as abnormal flow of crowds and traffic accidents could result in loss of life or property if not handled properly. Detecting urban anomalies at the early stage is important to minimize the adverse effects. However, urban anomaly detection is difficult due to two challenges: a) the criteria of urban anomalies varies with differe...

Ever more powerful mobile devices are nowadays capable of collectively carrying out reasonably demanding computational tasks without offloading the processing to an edge server or a distant cloud-computing service. In this work, we explore such distributed computing and study how it is affected by the mobility as well as the number of nodes that co...

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-sc...

For seamless QoS, it is important that all the stakeholders, such as the hosts, applications, access networks, routers, and other middleboxes, follow a single protocol and they trust each other. In this article, we investigate the participation of these entities in providing QoS over wireless networks in light of DiffServ QoS architecture. We initi...

The explosive development of mobile communications and networking has led to the creation of an extremely complex system, which is difficult to manage. Hence, we propose an AI-powered network framework that uses AI technologies to operate the network automatically. However, due to the separation between different mobile network operators, data barr...

The explosive growth of mobile users has brought great challenges to traditional cellular networks. The deployment of various Small-cell Base Stations (SBSs) provides a flexible way to address the problem of covering blind spots in Macro-cell Base Station (MBS) and reduce the traffic loads from MBS. In this paper, we investigate energy-saving based...

In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and a...

Device-to-Device (D2D) communications and small cell networks, as promising technologies to improve spectral efficiency and system throughput for future cellular networks, have received increasing attentions. In this paper, we model D2D communications in the two-tier heterogeneous macro-small cell networks. We propose not only two novel resource sh...

In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted i...

Device-to-Device (D2D) communication underlaying macro-small cell networks, as one of the promising technologies in the era of 5G, is able to improve spectral efficiency and increase system capacity. In this paper, we model the cross- and co-tier D2D communications in two-tier macro-small cell networks. To avoid the complicated interference for cro...

In this paper, we investigate the inbound handover confusion in the two-tier macrocell-small cell networks with help of mobility prediction. Instead of studying the mobile user's (MU) movement, we propose an analytical model for the activity status of small cells, which is to exploit the statistical property of inbound handover events that would ha...