Behavioral Mobility Model with Geographic Constraints

Beuran, Răzvan; Miwa, Shinsuke; Shinoda, Yoichi

doi:10.1109/waina.2013.140

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…Finally, rules that determine the behavior of a node in the presence of other nodes include Mutual Avoidance, avoiding collisions between nodes, and Group Centering and Velocity Matching, forcing nodes to stay close to one another in the space vs. speed domains, and are the behavioral equivalents of RPGM vs. RVGM. An extension of [26], referred to as Behavioral Mobility Model with Geographic Constraints (BMM-GC) [41], provides a more accurate modeling of the interaction with obstacles. Another model adopting a behavioral approach for the interaction between nodes quite similar to [26] is the Group Force Mobility Model (GFMM) [20].…”

Section: ) Behavioral Modelsmentioning

confidence: 99%

“…Overall citations Citations in 2017-2021 RPGM [18] 1999 General purpose 989 149 RVGM [19] 2002 General purpose 155 17 GFMM [20] 2009 General purpose 17 5 MGCM [21] 2006 Military pedestrian 5 2 RRGM [22] 2005 Security / Search & rescue 26 4 VTGM [23] 2004 Military vehicular 107 15 CMM [24] 2006 Human social interactions 254 20 ECMM [25] 2012 Human social interactions 32 19 SGMM [40] 2004 Human social interactions 52 9 BMM [26] 2006 General purpose 20 3 BMM-GC [41] 2013…”

Section: Model Year Mobility Scenariosmentioning

confidence: 99%

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Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Nardis

Benedetto²

2022

IEEE Access

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Mobility modeling in 5G and beyond 5G must address typical features such as time-varying correlation between mobility patterns of different nodes, and their variation ranging from macro-mobility (kilometer range) to micro-mobility (sub-meter range). Current models have strong limitations in doing so: the widely used reference-based models, such as the Reference Point Group Mobility (RPGM), lack flexibility and accuracy, while the more sophisticated rule-based (i.e. behavioral) models are complex to set-up and tune. This paper introduces a new rule-based Modular Mobility Model, named Mo 3 , that provides accuracy and flexibility on par with behavioral models, while preserving the intuitiveness of the reference-based approach, and is based on five rules: 1) Individual Mobility, 2) Correlated Mobility, 3) Collision Avoidance, 4) Obstacle Avoidance and 5) Upper Bounds Enforcement. Mo 3 avoids introducing acceleration vectors to define rules, as behavioral models do, and this significantly reduces complexity. Rules are mapped one-to-one onto five modules, that can be independently enabled or replaced. Comparison of time-correlation features obtained with Mo 3 vs. reference-based models, and in particular RPGM, in pure micro-mobility and mixed macro-mobility / micro-mobility scenarios, shows that Mo 3 and RPGM generate mobility patterns with similar topological properties (intra-group and inter-group distances), but that Mo 3 preserves a spatial correlation that is lost in RPGM -at no price in terms of complexity -making it suitable for adoption in 5G and beyond 5G.

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Section: ) Behavioral Modelsmentioning

confidence: 99%

Section: Model Year Mobility Scenariosmentioning

confidence: 99%

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Nardis

Benedetto²

2022

IEEE Access

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“…The classification of mobility models is done according to the existing research work in D2D communication. We focus on mobility models and traces with regards to human and vehicle behavior according to their movement patterns [73], [210], [211], speed, geographic location [104], [212], social characteristics [53], [85], [213], stochastic data [214] and frequent visiting places [82]. Mobility models include random mobility model [61], [72], human mobility model [74], vehicular mobility model, dynamic graph model [97], social group based mobility model [5], [215] and geographic based mobility model.…”

Section: A Overview Of Mobility Assisted D2d Communicationmentioning

confidence: 99%

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

Waqas

Niu

et al. 2020

IEEE Commun. Surv. Tutorials

112

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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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“…Finally, rules that determine the behavior of a node in presence of other nodes include Mutual Avoidance, that avoids collisions between nodes, and the Group Centering and Velocity Matching rules, that force nodes to stay close in the space or speed domain, and can be considered as the behavioral equivalents to the RPGM and RVGM mobility models, respectively. An extension of the behavioral model in [24] was recently proposed in [8], focusing on a more accurate modeling of the interaction with obstacles. Behavioral mobility modeling was also investigated in [29], where a modular approach is proposed, in which basic rules are combined in order to generate complex behaviors such as group mobility.…”

Section: Behavioral Modelsmentioning

confidence: 99%

Mo3: a Modular Mobility Model for future generation mobile wireless networks

De Nardis,

Di Benedetto

2017

Preprint

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Existing group mobility models were not designed to meet the requirements for accurate simulation of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date information on the position of each node in the network, combined with a simple and flexible approach to group mobility modeling. A new model for group mobility in wireless networks, named MoMo, is proposed in this paper, based on the combination of a memory-based individual mobility model with a flexible group behavior model. MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move independently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated. A new set of intrinsic properties for a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models. Next, MoMo is compared with existing group mobility models in a typical 5G network scenario, in which a set of mobile nodes cooperate in the realization of a distributed MIMO link. Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes in discrete event simulators, making it suitable for the performance evaluation of networking protocols and resource allocation algorithms in the wide range of network scenarios expected to characterize 5G networks.

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Behavioral Mobility Model with Geographic Constraints

Cited by 7 publications

References 14 publications

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

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

Mo3: a Modular Mobility Model for future generation mobile wireless networks

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Behavioral Mobility Model with Geographic Constraints

Cited by 7 publications

References 14 publications

Mo3: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Mo3: A Modular Mobility Model for Future Generation Mobile Wireless Networks

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

Mo3: a Modular Mobility Model for future generation mobile wireless networks

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Product

Resources

About

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks

Mo³: A Modular Mobility Model for Future Generation Mobile Wireless Networks