TRAILS - A Trace-Based Probabilistic Mobility Model

Förster, Anna; Muslim, Anas Bin; Udugama, Asanga

doi:10.1145/3242102.3242134

Cited by 14 publications

(12 citation statements)

References 7 publications

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“…In addition, in TRAILS, a user only travels to a point of interest (POI) that has a direct path with its current POI. 16…”

Section: Related Workmentioning

confidence: 99%

“…Section 3.1.1 defines how popularity indicators are estimated, and in section 3.2, we describe how TRAILS uses those indicators in a simulator. 16…”

Section: Model Descriptionmentioning

confidence: 99%

“…15 Another example is TRAce-based ProbabILiStic (TRAILS), which captures all three characteristics by extracting information from real traces. The TRAILS model presented in this paper builds upon the paper “TRAILS-A Trace-Based Probabilistic Mobility Model.” 16 Our work enhances the previous algorithms by considering the changes of human routines over time, as explained in section 3.1.…”

Section: Introductionmentioning

confidence: 99%

“…The TRAILS model presented in this paper builds upon the paper ''TRAILS-A Trace-Based Probabilistic Mobility Model.'' 16 Our work enhances the previous algorithms by considering the changes of human routines over time, as explained in section 3.1.…”

Section: Introductionmentioning

confidence: 99%

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TRAILS mobility model

Sarmiento

Förster

2022

SIMULATION

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According to state-of-the-art research, mobile network simulation is preferred over real testbeds, especially to evaluate communication protocols used in Opportunistic Networks (OppNet) or Mobile Ad hoc NETworks (MANET). The main reason behind it is the difficulty of performing experiments in real scenarios. However, in a simulation, a mobility model is required to define users’ mobility patterns. Trace-based models can be used for this purpose, but they are difficult to obtain, and they are not flexible or scalable. Another option is TRAce-based ProbabILiStic (TRAILS). TRAILS mimics the spatial dependency, geographic restrictions, and temporal dependency from real scenarios. In addition, with TRAILS, it is possible to scale the number of mobile users and simulation time. In this paper, we dive into the algorithms used by TRAILS to generate mobility graphs from real scenarios and simulate human mobility. In addition, we compare mobility metrics of TRAILS simulations, real traces, and another synthetic mobility model such as Small Worlds in Motion (SWIM). Finally, we analyze the performance of an implementation of the TRAILS model in computation time and memory consumption. We observed that TRAILS simulations represent the interaction among users of real scenarios with higher accuracy than SWIM simulations. Furthermore, we found that a simulation with TRAILS requires less computation time than a simulation with real traces and that a TRAILS graph consumes less memory than traces.

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“…In addition, in TRAILS, a user only travels to a point of interest (POI) that has a direct path with its current POI. 16…”

Section: Related Workmentioning

confidence: 99%

“…Section 3.1.1 defines how popularity indicators are estimated, and in section 3.2, we describe how TRAILS uses those indicators in a simulator. 16…”

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TRAILS mobility model

Sarmiento

Förster

2022

SIMULATION

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“…This issue is in common among the synthetic mobility models such as SWIM. Hence, a hybrid mobility model (Trace-based Probabilistic Mobility Model), has been recently proposed to resolve this issue and further to make the pause and contact times shorter compared with the SWIM model [195]. The social routine work of the human operators in a service-oriented MANET composed of service providers and service requesters has been also modeled via the SWIM mobility model in [196].…”

Section: ) Small World In Motion Mobility Model (Swim) [72]mentioning

confidence: 99%

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

et al. 2020

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With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.

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