Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9739707/ |
| _version_ | 1818353986363719680 |
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| author | Luca De Nardis Maria-Gabriella Di Benedetto |
| author_facet | Luca De Nardis Maria-Gabriella Di Benedetto |
| author_sort | Luca De Nardis |
| collection | DOAJ |
| description | 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<sup>3</sup>, 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<sup>3</sup> 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<sup>3</sup> vs. reference-based models, and in particular RPGM, in pure micro-mobility and mixed macro-mobility / micro-mobility scenarios, shows that Mo<sup>3</sup> and RPGM generate mobility patterns with similar topological properties (intra-group and inter-group distances), but that Mo<sup>3</sup> 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. |
| first_indexed | 2024-12-13T19:18:14Z |
| format | Article |
| id | doaj.art-abf60dfc30ca4f9186d7bec07f12343a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T19:18:14Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-abf60dfc30ca4f9186d7bec07f12343a2022-12-21T23:34:14ZengIEEEIEEE Access2169-35362022-01-0110340853411510.1109/ACCESS.2022.31615419739707Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless NetworksLuca De Nardis0https://orcid.org/0000-0001-9286-8744Maria-Gabriella Di Benedetto1https://orcid.org/0000-0003-1523-5083Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, ItalyDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, ItalyMobility 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<sup>3</sup>, 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<sup>3</sup> 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<sup>3</sup> vs. reference-based models, and in particular RPGM, in pure micro-mobility and mixed macro-mobility / micro-mobility scenarios, shows that Mo<sup>3</sup> and RPGM generate mobility patterns with similar topological properties (intra-group and inter-group distances), but that Mo<sup>3</sup> 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.https://ieeexplore.ieee.org/document/9739707/Beyond 5G networksgroup mobility modelingmobile wireless networks simulation |
| spellingShingle | Luca De Nardis Maria-Gabriella Di Benedetto Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks IEEE Access Beyond 5G networks group mobility modeling mobile wireless networks simulation |
| title | Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks |
| title_full | Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks |
| title_fullStr | Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks |
| title_full_unstemmed | Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks |
| title_short | Mo<sup>3</sup>: A Modular Mobility Model for Future Generation Mobile Wireless Networks |
| title_sort | mo sup 3 sup a modular mobility model for future generation mobile wireless networks |
| topic | Beyond 5G networks group mobility modeling mobile wireless networks simulation |
| url | https://ieeexplore.ieee.org/document/9739707/ |
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