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Journal articles on the topic 'User mobility'

To see the other types of publications on this topic, follow the link: User mobility.

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Secci, Stefano, Patrick Raad, and Pascal Gallard. "Linking Virtual Machine Mobility to User Mobility." IEEE Transactions on Network and Service Management 13, no. 4 (December 2016): 927–40. http://dx.doi.org/10.1109/tnsm.2016.2592241.

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2

Zonoozi, M. M., and P. Dassanayake. "User mobility modeling and characterization of mobility patterns." IEEE Journal on Selected Areas in Communications 15, no. 7 (1997): 1239–52. http://dx.doi.org/10.1109/49.622908.

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3

Wang, Guowei, Dongliang Liao, and Jing Li. "Complete User Mobility via User and Trajectory Embeddings." IEEE Access 6 (2018): 72125–36. http://dx.doi.org/10.1109/access.2018.2881457.

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4

Qi, Mengjun, Zhongyuan Wang, Zheng He, and Zhenfeng Shao. "User Identification across Asynchronous Mobility Trajectories." Sensors 19, no. 9 (May 7, 2019): 2102. http://dx.doi.org/10.3390/s19092102.

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With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.

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5

Bell, Daniel. "Intermodal Mobility Hubs and User Needs." Social Sciences 8, no. 2 (February 20, 2019): 65. http://dx.doi.org/10.3390/socsci8020065.

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Technological innovation in the mobility and transport sphere is still strongly aimed at vehicle- or infrastructure-based systems. Actual user needs in regard to accessibility and usability of the links between different transportation modes are usually addressed by standardized planning processes. In a row of four consecutive Austrian research projects, every element in the chain of intermodal routes was addressed, starting from the user-centered features of public transport stops in both urban and rural transport systems, to the planning tasks involved in providing demand-driven public transport. The current iteration focuses on establishing a typology of intermodal mobility hubs, including a differentiated view of the potential users of a multimodal transport system and their respective needs at the station.

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6

Lai, Kwong Yuen, Zahir Tari, and Peter Bertok. "Supporting User Mobility through Cache Relocation." Mobile Information Systems 1, no. 4 (2005): 275–307. http://dx.doi.org/10.1155/2005/513531.

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Traffic and access delay can be reduced in a mobile network by caching data objects at network nodes near the clients. Traditional caching techniques, however, are unsuitable in this environment because they do not account for the changing location of the users. To deal with this problem, cache relocation techniques can be applied to dynamically relocate data objects so they remain close to the moving clients. Existing relocation techniques rely heavily on path prediction. Unfortunately, the inaccuracy of path prediction can lead to high relocation overhead and poor response time. This paper presents an analytical study of the effects of client mobility on network cache performance. Two new cache relocation techniques are proposed to deal with the issue of poor path prediction and reduce the overhead of existing cache relocation schemes. The first technique, 2PR, compensates for poor path prediction by temporarily moving data objects to a common parent node prior to a handover. Objects are moved to the correct destination once the client's new location has been confirmed. The second technique, ROLP, reduces the traffic overhead associated with cache relocation by ensuring duplicate objects are not relocated and relocation of objects are performed only from the nearest node to the destination. Test results show that 2PR reduces the query delay experienced by mobile clients by 60 to 83% after handovers, while ROLP reduces the overhead of cache relocation by between 33 to 65% compared to existing schemes.

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7

Chen, Xihui, Jun Pang, and Ran Xue. "Constructing and Comparing User Mobility Profiles." ACM Transactions on the Web 8, no. 4 (November 6, 2014): 1–25. http://dx.doi.org/10.1145/2637483.

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8

Wressle, Ewa, and Kersti Samuelsson. "User Satisfaction with Mobility Assistive Devices." Scandinavian Journal of Occupational Therapy 11, no. 3 (April 2004): 143–50. http://dx.doi.org/10.1080/11038120410020728.

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9

Kim, Sehun, and Ki-Dong Lee. "Modelling user Mobility in Microcellular Systems." International Journal of Modelling and Simulation 21, no. 2 (January 2001): 132–37. http://dx.doi.org/10.1080/02286203.2001.11442195.

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10

Kravets, Robin, Casey Carter, and Luiz Magalhães. "A cooperative approach to user mobility." ACM SIGCOMM Computer Communication Review 31, no. 5 (October 2001): 57–69. http://dx.doi.org/10.1145/1037107.1037115.

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11

Ma, Wenchao, Yuguang Fang, and Phone Lin. "Mobility Management Strategy Based on User Mobility Patterns in Wireless Networks." IEEE Transactions on Vehicular Technology 56, no. 1 (January 2007): 322–30. http://dx.doi.org/10.1109/tvt.2006.883743.

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12

Satoh, Ichiro. "SpatialAgents: integrating user mobility and program mobility in ubiquitous computing environments." Wireless Communications and Mobile Computing 3, no. 4 (2003): 411–23. http://dx.doi.org/10.1002/wcm.126.

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13

Sandeep Kumar, V., Tarun Kumar Juluru, P. Ramchandar Rao, and K. Ravi Kiran. "Cognitive User Mobility Using Cooperative Spectrum Sensing." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 032047. http://dx.doi.org/10.1088/1757-899x/981/3/032047.

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14

Avendi, M. R., and Ha H. Nguyen. "Differential dual-hop relaying under user mobility." IET Communications 8, no. 17 (November 27, 2014): 3161–69. http://dx.doi.org/10.1049/iet-com.2014.0278.

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15

Cadar, Rodica Dorina, Rozalia Melania Boitor, and Marco Petrelli. "Urban Mobility and Road User Behavior Assessment." Procedia Engineering 181 (2017): 116–22. http://dx.doi.org/10.1016/j.proeng.2017.02.378.

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16

Faye, Sébastien, Walter Bronzi, Ibrahim Tahirou, and Thomas Engel. "Characterizing user mobility using mobile sensing systems." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772631. http://dx.doi.org/10.1177/1550147717726310.

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Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.

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17

Daras, Petros, Oscar Mayora-Ibarra, and Isidro-Laso Ballesteros. "Editorial for Mobility and User-Centric Media." Mobile Networks and Applications 16, no. 3 (November 3, 2010): 350. http://dx.doi.org/10.1007/s11036-010-0271-5.

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18

Shad, Shafqat Ali, and Enhong Chen. "Unsupervised User Similarity Mining in GSM Sensor Networks." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/589610.

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Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user’s actual movement prediction, and context awareness. However, significant places extraction and user’s actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.

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19

Wu, Laiyun, Samiul Hasan, Younshik Chung, and Jee Eun Kang. "Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences." Sustainability 13, no. 24 (December 16, 2021): 13921. http://dx.doi.org/10.3390/su132413921.

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Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

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20

Morris, Leah, Mary Cramp, and Ailie Turton. "User perspectives on the future of mobility assistive devices: Understanding users’ assistive device experiences and needs." Journal of Rehabilitation and Assistive Technologies Engineering 9 (January 2022): 205566832211147. http://dx.doi.org/10.1177/20556683221114790.

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Introduction Current assistive devices are inadequate in addressing the needs of some people living with impaired mobility. This study explored the experiences of living with impaired mobility in relation to how wearable assistive adaptive and rehabilitative technologies may improve their quality of life. Methods A cross-case study approach was adopted; the case being defined as the experience of impaired mobility. Semi-structured interviews were utilised. The sample ( n = 8) was purposefully selected to have impaired mobility due to stroke, age-related frailty, or lower limb amputation. From the interview transcripts, in-depth case illustrations were written to provide personal stories and thematic analysis was carried out to provide a cross-case analysis. Results There were two overarching themes: lifestyle changes; and wishes and desires for assistive devices. There were shared experiences across participant groups, such as falls and fear of falling. All participants identified a wish for increased speed of walking. However, the reasons for their difficulties differed depending on personal factors and their condition. Participants wanted devices to be adjustable to their perceived ability on a day-to-day basis. Conclusions Although common concerns and impacts of living with impaired mobility were apparent, individuals have unique requirements that should inform the design of assistive technology devices.

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21

Quintero, A. "A user pattern learning strategy for managing users' mobility in UMTS networks." IEEE Transactions on Mobile Computing 4, no. 6 (November 2005): 552–66. http://dx.doi.org/10.1109/tmc.2005.75.

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22

Ge, Xiaohu, Junliang Ye, Yang Yang, and Qiang Li. "User Mobility Evaluation for 5G Small Cell Networks Based on Individual Mobility Model." IEEE Journal on Selected Areas in Communications 34, no. 3 (March 2016): 528–41. http://dx.doi.org/10.1109/jsac.2016.2525439.

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23

Koumpouros, Yiannis. "User Needs and Limitations of Existing Mobility Devices." International Journal of Reliable and Quality E-Healthcare 7, no. 4 (October 2018): 1–19. http://dx.doi.org/10.4018/ijrqeh.2018100101.

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The use of gait assistive devices, although can provide precious help can also have limitations or adverse consequences. The performance of daily activities is partially restrained by a series of factors having to do with the assistive device limitations or the limitations emerged from environmental causes. Valid and reliable assessment tools to classify and clarify their limitations or users' satisfaction do not exist. An extended literature search was performed for some of the most commonly mentioned assistive device limitations or dissatisfaction reasons and identified the most appropriate tools to be able to study such limitations and conclude in valid outcomes. A study was conducted in a rehabilitation hospital using the IPA, EQ5D and SF-12 tools. The size of the sample combined with the uncertainty related to the satisfaction of normal distribution prerequisite, led to use several statistical analysis tools and methodologies. A summary of the results from both the literature review and the real case study on a mixed group of mobility aid users are presented in the paper.

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24

Arshad, Rabe, Hesham Elsawy, Sameh Sorour, Mohamed-Slim Alouini, and Tareq Y. Al-Naffouri. "Mobility-Aware User Association in Uplink Cellular Networks." IEEE Communications Letters 21, no. 11 (November 2017): 2452–55. http://dx.doi.org/10.1109/lcomm.2017.2729560.

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25

Chen, Changshan, and Xinsheng Zhao. "Mobility-Aware Access Strategy in Multi-User HetNets." IEEE Wireless Communications Letters 9, no. 7 (July 2020): 1004–8. http://dx.doi.org/10.1109/lwc.2020.2978062.

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26

Blom, Jan, Jan Chipchase, and Jaakko Lehikoinen. "Contextual and cultural challenges for user mobility research." Communications of the ACM 48, no. 7 (July 2005): 37–41. http://dx.doi.org/10.1145/1070838.1070863.

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27

Lee, Ki-Dong, and Sehun Kim. "Modeling variable user mobility with stochastic correlation concept." Computer Networks 38, no. 5 (April 2002): 603–12. http://dx.doi.org/10.1016/s1389-1286(01)00274-2.

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28

Rezaeibagha, Fatemeh, and Yi Mu. "Practical and secure telemedicine systems for user mobility." Journal of Biomedical Informatics 78 (February 2018): 24–32. http://dx.doi.org/10.1016/j.jbi.2017.12.011.

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29

Yi-Bing Lin. "Per-user checkpointing for mobility database failure restoration." IEEE Transactions on Mobile Computing 4, no. 2 (March 2005): 189–94. http://dx.doi.org/10.1109/tmc.2005.28.

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30

Cacciapuoti, Angela Sara. "Mobility-Aware User Association for 5G mmWave Networks." IEEE Access 5 (2017): 21497–507. http://dx.doi.org/10.1109/access.2017.2751422.

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31

Bolla, Raffaele, Riccardo Rapuzzi, and Matteo Repetto. "User-centric mobility management for multimedia content access." Multimedia Tools and Applications 70, no. 1 (June 14, 2011): 267–95. http://dx.doi.org/10.1007/s11042-011-0827-9.

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32

Rodriguez-Carrion, Alicia, David Rebollo-Monedero, Jordi Forné, Celeste Campo, Carlos Garcia-Rubio, Javier Parra-Arnau, and Sajal Das. "Entropy-Based Privacy against Profiling of User Mobility." Entropy 17, no. 6 (June 10, 2015): 3913–46. http://dx.doi.org/10.3390/e17063913.

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33

Hong, Jinpyo, and Hwangnam Kim. "An empirical framework for user mobility models: Refining and modeling user registration patterns." Journal of Computer and System Sciences 77, no. 5 (September 2011): 869–83. http://dx.doi.org/10.1016/j.jcss.2010.08.005.

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34

Cheng, Zhipeng, Ning Chen, Bang Liu, Zhibin Gao, Lianfen Huang, Xiaojiang Du, and Mohsen Guizani. "Joint user association and resource allocation in HetNets based on user mobility prediction." Computer Networks 177 (August 2020): 107312. http://dx.doi.org/10.1016/j.comnet.2020.107312.

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35

Gul-E-Laraib, Sardar Khaliq uz Zaman, Tahir Maqsood, Faisal Rehman, Saad Mustafa, Muhammad Amir Khan, Neelam Gohar, Abeer D. Algarni, and Hela Elmannai. "Content Caching in Mobile Edge Computing Based on User Location and Preferences Using Cosine Similarity and Collaborative Filtering." Electronics 12, no. 2 (January 5, 2023): 284. http://dx.doi.org/10.3390/electronics12020284.

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High-speed internet has boosted clients’ traffic needs. Content caching on mobile edge computing (MEC) servers reduces traffic and latency. Caching with MEC faces difficulties such as user mobility, limited storage, varying user preferences, and rising video streaming needs. The current content caching techniques consider user mobility and content popularity to improve the experience. However, no present solution addresses user preferences and mobility, affecting caching decisions. We propose mobility- and user-preferences-aware caching for MEC. Using time series, the proposed system finds mobility patterns and groups nearby trajectories. Using cosine similarity and CF, we predict and cache user-requested content. CF predicts the popularity of grouped-based content to improve the cache hit ratio and reduce delay compared to baseline techniques.

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36

Xu, Jian Bo, and Guang Yang. "A Purpose-Driven User Mobility Model for Opportunistic Networks." Advanced Materials Research 171-172 (December 2010): 804–9. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.804.

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An opportunistic Network is a network consisting exclusively of users’ mobile devices, with mobility being one of its essential features. Under the circumstances that a path may never exist between the two sides of communication, an opportunistic network exploits node mobility to realize delayed data delivery by capturing the opportunities of node meeting to relay messages. Designing efficient data forwarding strategies is one of the most challenging tasks in opportunistic network research, while currently the validation of any protocol for data forwarding almost absolutely relies on simulations of which node mobility models are one of the fundamental components. In this paper, we suggest a purpose-driven user mobility model for opportunistic networks which, to our best knowledge, is the first work considering the factor of purposes behind users’ movement. On the basis of location functionalization, our model can gain a better approximation of human movement patterns.

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37

Bahra, Nasrin, and Samuel Pierre. "A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks." Telecom 2, no. 2 (May 6, 2021): 199–212. http://dx.doi.org/10.3390/telecom2020013.

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Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

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38

Ferreira, Joao. "Mining Users Mobility at Public Transportation." Inteligencia Artificial 20, no. 59 (February 6, 2017): 32. http://dx.doi.org/10.4114/intartif.vol20iss59pp32-41.

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In this research work we propose a new approach to estimate the number of passengers in a public transportation and determinate the users’ route path based on a passive approach without user intervention. The method is based on the probe requests of users mobile device through the collected data in wireless access point. This data is manipulated to extract the information about the numbers of users with mobile devices and track their route path and time. This data can be manipulated to extract useful knowledge related with users’ habits at public transportation and extract user mobility patterns.

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39

Ni, Jun Hong, Yi Zhou, Zhen Dong Zhao, Peng Peng, and Rui Ju Xiao. "Scheduling Algorithm for High-Mobility User in LTE Downlink System." Applied Mechanics and Materials 644-650 (September 2014): 2143–46. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2143.

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Frequency diversity scheduling allocates whole system bandwidth to a user in order to conquer deep fading of the sub-band when user with high-mobility. Though OFDMA technology mitigates interference of users in same cell, it increases interference of cell edge user as base stations use same frequency. Thus, we developed a frequency diversity fairness scheduling algorithm to balance fairness of cell center user and cell edge user. It is demonstrated by computer simulation that the proposed algorithm increases user fairness of the system with same system throughput and fairness between cell edge user and cell center user. It also points out that the algorithm proposed has the same complexity with the frequency diversity scheduling algorithm.

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40

Abitbol, Jacob Levy, and Alfredo J. Morales. "Socioeconomic Patterns of Twitter User Activity." Entropy 23, no. 6 (June 19, 2021): 780. http://dx.doi.org/10.3390/e23060780.

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Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.

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41

Degbelo, Auriol, Jan Kruse, and Max Pfeiffer. "Interactive maps, productivity and user experience: A user study in the e‐mobility domain." Transactions in GIS 23, no. 6 (September 6, 2019): 1352–73. http://dx.doi.org/10.1111/tgis.12576.

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42

Hong, J., and H. Kim. "A Dual Mobility Model with User Profiling: Decoupling User Mobile Patterns from Association Patterns." Computer Journal 56, no. 6 (October 31, 2012): 771–84. http://dx.doi.org/10.1093/comjnl/bxs142.

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43

Fomiatti, Ryan, Lois Moir, Janet Richmond, and Jeannine Millsteed. "The experience of being a motorised mobility scooter user." Disability and Rehabilitation: Assistive Technology 9, no. 3 (June 26, 2013): 183–87. http://dx.doi.org/10.3109/17483107.2013.814171.

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44

Chang, Ya-Fen, Wei-Liang Tai, and Min-How Hsu. "A Secure Mobility Network Authentication Scheme Ensuring User Anonymity." Symmetry 9, no. 12 (December 8, 2017): 307. http://dx.doi.org/10.3390/sym9120307.

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45

Hong, Yang, and Lian-Kuan Chen. "Toward user mobility for OFDM-based visible light communications." Optics Letters 41, no. 16 (August 4, 2016): 3763. http://dx.doi.org/10.1364/ol.41.003763.

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46

Kondor, Daniel, Behrooz Hashemian, Yves-Alexandre de Montjoye, and Carlo Ratti. "Towards Matching User Mobility Traces in Large-Scale Datasets." IEEE Transactions on Big Data 6, no. 4 (December 1, 2020): 714–26. http://dx.doi.org/10.1109/tbdata.2018.2871693.

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47

Jain, Ravi, Anupama Shivaprasad, Dan Lelescu, and Xiaoning He. "Towards a model of user mobility and registration patterns." ACM SIGMOBILE Mobile Computing and Communications Review 8, no. 4 (October 2004): 59–62. http://dx.doi.org/10.1145/1052871.1052877.

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48

Li, Xujie, Ying Sun, Siyuan Zhou, and Yanli Xu. "User pairing scheme in mobility-aware D2D communication system." IET Communications 14, no. 4 (March 3, 2020): 655–64. http://dx.doi.org/10.1049/iet-com.2019.0472.

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49

Cantelmo, Guido, Piergiorgio Vitello, Bogdan Toader, Antoniou, and Francesco Viti. "Inferring Urban Mobility and Habits from User Location History." Transportation Research Procedia 47 (2020): 283–90. http://dx.doi.org/10.1016/j.trpro.2020.03.100.

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50

Taufiq, Muhammad, Mohd Fikri Azli bin Abdullah, and Deokjai Choi. "Wireless LAN Access Point Placement Based on User Mobility." Wireless Personal Communications 60, no. 3 (April 9, 2011): 431–40. http://dx.doi.org/10.1007/s11277-011-0300-0.

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