On the Universal Generation of Mobility Models

Medina, Alberta; Gürsun, Gonca; Basu, Prithwish; Matta, Ibrahim

doi:10.21236/ada522737

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…In addition, Random Waypoint mobility model, Random Walk mobility model, and Gauss‐Markov mobility model were developed to mimic the random and erratic movement of an intruder and were also used in modeling unexpected node movement in mobile WSNs . Specifically, Medina et al proposed a universal mobility modeling framework that enables the instantiation of an application‐specific mobility model, and Yanmaz et al proposed a coverage‐based mobility model by using the local physical topology information to improve coverage of a WSN domain. Furthermore, Dousse et al considered a range of motion models for intrusion detection, ranging from a straight‐path motion to a random walk motion pattern.…”

Section: Related Workmentioning

confidence: 99%

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Wang

Leow

Yin

2011

Wireless Communications

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Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight‐line intrusion path and a Boolean sensing model. However, a straight‐line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real‐world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel sine‐curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Wang

Leow

Yin

2011

Wireless Communications

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“…Then, a temporal graph can be defined as the sequence of graphs extracted at each time step while the nodes move. We investigate two different mobility models that are implemented using the Universal Mobility Model Framework [38]: Random Waypoint Model (RWP) and Random Waypoint Group Model (RWPG). In RWP each node selects uniformly at random a location towards which it moves with speed uniformly distributed in a fixed range [5,40] mph.…”

Section: Random Temporal Network Modelsmentioning

confidence: 99%

“…Each group leader selects a random target and moves towards it, according to the RWP mobility model. Group members do not select any target; instead, they follow their group leader according to the pursuit force [38] which is set to give a group span of 200 meters.…”

Section: Random Temporal Network Modelsmentioning

confidence: 99%

Applications of Temporal Graph Metrics to Real-World Networks

Tang

Leontiadis

Scellato

et al. 2013

Understanding Complex Systems

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Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

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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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On the Universal Generation of Mobility Models

Cited by 5 publications

References 5 publications

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Applications of Temporal Graph Metrics to Real-World Networks

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

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On the Universal Generation of Mobility Models

Cited by 5 publications

References 5 publications

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Applications of Temporal Graph Metrics to Real-World Networks

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

Contact Info

Product

Resources

About

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