CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks

Fang, Zhihan; Wang, Guang; Wang, Shuai; Zuo, Chaoji; Zhang, Fan; Stankovic, John A.

doi:10.1145/3366423.3380141

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Cao et al [13] conduct measurement studies to predict human movement in a college campus using WLAN measurements. Additionally, recent studies have also investigated the impact of geographic location on cellular network usage [14], [15].…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [32] develop a real-time model for human mobility using multi-view learning and Zhu et al [34] develop spherical hidden Markov model for understanding human mobility. There is also work on analyzing data in relationship to the geographic locations [11,25]. Cao et al [6] conduct measurement studies on the predictability of human movement in a college campus using WLAN measurements.…”

Section: Related Workmentioning

confidence: 99%

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Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

Ayan

Damasceno

Chaskar

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic. To this end, we collect and analyze cellular network connections from 1400 antennas for all users in the city of Rio de Janeiro and its suburbs from March 1, 2020 to July 1, 2020. Our analysis reveals that the total number of cellular connections decreases to 78% during the lockdown phase and then increases to 85% of the pre-COVID era as the lockdown eases. We observe that user mobility starts increasing around 3 weeks before the end of lockdown, with the trend continuing into the post-lockdown period. We also design an interactive tool that showcases mobility patterns in different granularities and can help government officials take informed actions to control the spread of the disease.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

Ayan

Damasceno

Chaskar

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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show abstract

CellSense

Fang

Yang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatial-temporal observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction.

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Mover

Lyu

Wang

Yang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Human mobility models typically produce mobility data to capture human mobility patterns individually or collectively based on real-world observations or assumptions, which are essential for many use cases in research and practice, e.g., mobile networking, autonomous driving, urban planning, and epidemic control. However, most existing mobility models suffer from practical issues like unknown accuracy and uncertain parameters in new use cases because they are normally designed and verified based on a particular use case (e.g., mobile phones, taxis, or mobile payments). This causes significant challenges for researchers when they try to select a representative human mobility model with appropriate parameters for new use cases. In this paper, we introduce a MObility VERification framework called MOVER to systematically measure the performance of a set of representative mobility models including both theoretical and empirical models based on a diverse set of use cases with various measures. Based on a taxonomy built upon spatial granularity and temporal continuity, we selected four representative mobility use cases (e.g., the vehicle tracking system, the camera-based system, the mobile payment system, and the cellular network system) to verify the generalizability of the state-of-the-art human mobility models. MOVER methodically characterizes the accuracy of five different mobility models in these four use cases based on a comprehensive set of mobility measures and provide two key lessons learned: (i) For the collective level measures, the finer spatial granularity of the user cases, the better generalization of the theoretical models; (ii) For the individual-level measures, the lower periodic temporal continuity of the user cases, the theoretical models typically generalize better than the empirical models. The verification results can help the research community to select appropriate mobility models and parameters in different use cases.

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CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks

Cited by 3 publications

References 44 publications

Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

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