54 research outputs found
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Understanding mobile traffic patterns of large scale cellular towers in urban
environment is extremely valuable for Internet service providers, mobile users,
and government managers of modern metropolis. This paper aims at extracting and
modeling the traffic patterns of large scale towers deployed in a metropolitan
city. To achieve this goal, we need to address several challenges, including
lack of appropriate tools for processing large scale traffic measurement data,
unknown traffic patterns, as well as handling complicated factors of urban
ecology and human behaviors that affect traffic patterns. Our core contribution
is a powerful model which combines three dimensional information (time,
locations of towers, and traffic frequency spectrum) to extract and model the
traffic patterns of thousands of cellular towers. Our empirical analysis
reveals the following important observations. First, only five basic
time-domain traffic patterns exist among the 9,600 cellular towers. Second,
each of the extracted traffic pattern maps to one type of geographical
locations related to urban ecology, including residential area, business
district, transport, entertainment, and comprehensive area. Third, our
frequency-domain traffic spectrum analysis suggests that the traffic of any
tower among the 9,600 can be constructed using a linear combination of four
primary components corresponding to human activity behaviors. We believe that
the proposed traffic patterns extraction and modeling methodology, combined
with the empirical analysis on the mobile traffic, pave the way toward a deep
understanding of the traffic patterns of large scale cellular towers in modern
metropolis.Comment: To appear at IMC 201
Origin-Destination Network Generation via Gravity-Guided GAN
Origin-destination (OD) flow, which contains valuable population mobility
information including direction and volume, is critical in many urban
applications, such as urban planning, transportation management, etc. However,
OD data is not always easy to access due to high costs or privacy concerns.
Therefore, we must consider generating OD through mathematical models. Existing
works utilize physics laws or machine learning (ML) models to build the
association between urban structures and OD flows while these two kinds of
methods suffer from the limitation of over-simplicity and poor generalization
ability, respectively. In this paper, we propose to adopt physics-informed ML
paradigm, which couple the physics scientific knowledge and data-driven ML
methods, to construct a model named Origin-Destination Generation Networks
(ODGN) for better population mobility modeling by leveraging the complementary
strengths of combining physics and ML methods. Specifically, we first build a
Multi-view Graph Attention Networks (MGAT) to capture the urban features of
every region and then use a gravity-guided predictor to obtain OD flow between
every two regions. Furthermore, we use a conditional GAN training strategy and
design a sequence-based discriminator to consider the overall topological
features of OD as a network. Extensive experiments on real-world datasets have
been done to demonstrate the superiority of our proposed method compared with
baselines.Comment: 10 pages, 8 figure
IoT vs. Human: A Comparison of Mobility
Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected.Peer reviewe
Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning
Accurately predicting individual-level infection state is of great value
since its essential role in reducing the damage of the epidemic. However, there
exists an inescapable risk of privacy leakage in the fine-grained user mobility
trajectories required by individual-level infection prediction. In this paper,
we focus on developing a framework of privacy-preserving individual-level
infection prediction based on federated learning (FL) and graph neural networks
(GNN). We propose Falcon, a Federated grAph Learning method for
privacy-preserving individual-level infeCtion predictiON. It utilizes a novel
hypergraph structure with spatio-temporal hyperedges to describe the complex
interactions between individuals and locations in the contagion process. By
organically combining the FL framework with hypergraph neural networks, the
information propagation process of the graph machine learning is able to be
divided into two stages distributed on the server and the clients,
respectively, so as to effectively protect user privacy while transmitting
high-level information. Furthermore, it elaborately designs a differential
privacy perturbation mechanism as well as a plausible pseudo location
generation approach to preserve user privacy in the graph structure. Besides,
it introduces a cooperative coupling mechanism between the individual-level
prediction model and an additional region-level model to mitigate the
detrimental impacts caused by the injected obfuscation mechanisms. Extensive
experimental results show that our methodology outperforms state-of-the-art
algorithms and is able to protect user privacy against actual privacy attacks.
Our code and datasets are available at the link:
https://github.com/wjfu99/FL-epidemic.Comment: accepted by TOI
Smartphone App Usage Analysis : Datasets, Methods, and Applications
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-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.Peer reviewe
S3: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various
challenges within social science. It offers extensive applications such as
state prediction, phenomena explanation, and policy-making support, among
others. In this work, we harness the formidable human-like capabilities
exhibited by large language models (LLMs) in sensing, reasoning, and behaving,
and utilize these qualities to construct the S system (short for
ocial network imulation ystem). Adhering to
the widely employed agent-based simulation paradigm, we employ prompt
engineering and prompt tuning techniques to ensure that the agent's behavior
closely emulates that of a genuine human within the social network.
Specifically, we simulate three pivotal aspects: emotion, attitude, and
interaction behaviors. By endowing the agent in the system with the ability to
perceive the informational environment and emulate human actions, we observe
the emergence of population-level phenomena, including the propagation of
information, attitudes, and emotions. We conduct an evaluation encompassing two
levels of simulation, employing real-world social network data. Encouragingly,
the results demonstrate promising accuracy. This work represents an initial
step in the realm of social network simulation empowered by LLM-based agents.
We anticipate that our endeavors will serve as a source of inspiration for the
development of simulation systems within, but not limited to, social science
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
Complex system simulation has been playing an irreplaceable role in
understanding, predicting, and controlling diverse complex systems. In the past
few decades, the multi-scale simulation technique has drawn increasing
attention for its remarkable ability to overcome the challenges of complex
system simulation with unknown mechanisms and expensive computational costs. In
this survey, we will systematically review the literature on multi-scale
simulation of complex systems from the perspective of knowledge and data.
Firstly, we will present background knowledge about simulating complex system
simulation and the scales in complex systems. Then, we divide the main
objectives of multi-scale modeling and simulation into five categories by
considering scenarios with clear scale and scenarios with unclear scale,
respectively. After summarizing the general methods for multi-scale simulation
based on the clues of knowledge and data, we introduce the adopted methods to
achieve different objectives. Finally, we introduce the applications of
multi-scale simulation in typical matter systems and social systems
Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
Decentralized clustering of modern information technology is widely adopted in various fields these years. One of the main reason is the features of high availability and the failure-tolerance which can prevent the entire system form broking down by a failure of a single point. Recently, toolkits such as Akka are used by the public commonly to easily build such kind of cluster. However, clusters of such kind that use Gossip as their membership managing protocol and use link failure detecting mechanism to detect link failures cannot deal with the scenario that a node stochastically drops packets and corrupts the member status of the cluster. In this paper, we formulate the problem to be evaluating the link quality and finding a max clique (NP-Complete) in the connectivity graph. We then proposed an algorithm that consists of two models driven by data from application layer to respectively solving these two problems. Through simulations with statistical data and a real-world product, we demonstrate that our algorithm has a good performance
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