The real-life mobility model: RLMM

Gorawski, Michał; Grochla, Krzysztof

doi:10.1109/fgct.2013.6767180

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…The real-life mobility model (RLMM) [48] considers the WDMM model as a baseline while it has certain characteristic differences. Distinguishing the days when people work and the weekend days is an example of such differences.…”

Section: Human Mobility Models With Vehicle Usementioning

confidence: 99%

A Survey of Human Mobility Models

Solmaz¹,

Turgut

2019

IEEE Access

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Human mobility models are key components of various research fields including transportation, mobile networks, disaster management, urban planning, and epidemic modeling. Understanding human mobility has a major role in the realistic evaluation of new approaches to challenges in these fields. For the perspective of networked systems, simulations of the networks with human participants such as opportunistic social networks are highly dependent on human mobility. In this article, we summarize the state of the art for scientific research on human mobility and survey the currently used human mobility models. We discuss the commonly used metrics and data collection techniques. Furthermore, we include a taxonomy of the mobility models according to their main characteristics and classify them. We lastly discuss the general trends, applicability, further research directions and open problems of human mobility modeling. INDEX TERMS Human mobility, mobility models, Internet of Things, smart cities.

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Section: Human Mobility Models With Vehicle Usementioning

confidence: 99%

A Survey of Human Mobility Models

Solmaz¹,

Turgut

2019

IEEE Access

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“…(1) Experiment 1: The first experiment analyzed a single user’s mobility during a five-day workweek and a two-day weekend collected on a smartphone. The data show the distinctive characteristics of human mobility, which were applied in [ 34 ] and described in [ 62 ]: repeated cyclic actions during the workweek and longer travels during the weekend. In seven days, the user traveled 27 km during the workweek and 191 km during the weekend.…”

Section: Sample Resultsmentioning

confidence: 99%

“…Since deploying a test version in 2013, the system has tracked over 40,000 km of human movements from over 50 unique user identifiers. The collected data were successfully applied to modeling cellular networks: in [ 34 ], we proposed a novel human mobility model based on real data. Since September 2014, our mobile application has been publicly available on Google Play, under the name “BX Tracker” [ 35 ].…”

Section: Bx Tracker: An Energy-efficient Crowdsensing Platformmentioning

confidence: 99%

“…Such research on human movements requires long-range, accurate and continuous location data of many individuals, which is a scarce resource. Some data are available in the CRAWDAD (Community Resource for Archiving Wireless Data At Dartmouth) database (for example [ 44 , 45 , 46 ]) and through the OpenStreeMap project [ 47 ], but none of the published datasets were suitable for our model presented in [ 34 ]. Moreover, we considered collecting the required data using off-the-shelf GPS trackers as inadequate.…”

Section: Energy-efficient Gps Trackingmentioning

confidence: 99%

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Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

Foremski

Gorawski

Grochla

et al. 2015

Sensors

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The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.

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“…While this decision process may capture some realism in spatial constraints, this does not represent temporal tendencies of human mobility. Moreover, the drawback is the exponential cut off that appears every 12 hours in the SLAW mobility model, which impacts on people's daily routines [84]. b) Heterogeneous Human Walk (HHW): The aforementioned SLAW is suitable for modeling mobility patterns of users having similar 'characteristics'.…”

Section: A Mobility Modelsmentioning

confidence: 99%

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Waqas

Niu

et al. 2020

IEEE Commun. Surv. Tutorials

114

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Device-to-device (D2D) communication proposes a new epitome in mobile networking to avail data exchange between physically proximate devices. The exploitation of D2D communication enables mobile operators to harvest short range communications for improving network performance and corroborating proximity-based services. In this article, we investigate mobility aspects of D2D communication, which are indispensable for the adoption and implementation of D2D communication technology. We present an extensive review of the state-of-theart problems and the corresponding solutions for encouraging the exploitation of mobility to assist D2D communication. Specifically, by identifying the mobility models, traces, problems, requirements, and features of different proposals, we discuss the lessons learned and summarize the advantages of mobility-aware D2D communication. We also present open problems and highlight future research directions concerning D2D communication applications in real-life scenarios. To the best of our knowledge, this is the first comprehensive survey to address mobility-aware D2D communication, which offers insight to the underlying problems and provides the potential solutions. Index Terms-Device-to-device communication, mobility, mobile communication, mobile data traffic. I. INTRODUCTION D EVICE-to-device (D2D) communication takes the advantage of opportunistic encounters by mobile users with each other [1]. These opportunistic encounters' information between users are highly related to their movement. By exploiting users' movement, D2D enabled applications and services visualize highly opportunistic and unpredictable human mobility. Therefore, the challenges of exploiting mobility resides in the inherent complexity of users' mobility. It is mainly concerns with predicting the establishment of communication links among D2D users. For instance, two mobile users can establish a link at the time when they are in close proximity to each other. However, there are two key questions have to M.

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The real-life mobility model: RLMM

Cited by 11 publications

References 16 publications

A Survey of Human Mobility Models

A Survey of Human Mobility Models

Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

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