scholarly journals Towards a Bidirectional Coupling of Pedestrian Dynamics and Mobile Communication Simulation

10.29007/nnfj ◽  
2019 ◽  
Author(s):  
Stefan Schuhbäck ◽  
Nico Daßler ◽  
Lars Wischhof ◽  
Gerta Köster
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Traffic Control ◽  
State Of The Art ◽  
Use Cases ◽  
Mobility Models ◽  
Simulation Framework ◽  
Mobility Patterns ◽  
Pedestrian Dynamics ◽  
Bidirectional Coupling

In order to accurately evaluate new concepts and protocols for mobile communication networks, realistic mobility models are needed. Furthermore, for use cases which have a bidirectional dependency between communication and mobility, changes in communication lead to changes in mobility and vice versa, thus requiring an online coupling between models. Therefore, bidirectional coupling to incorporate realistic mobility patterns is state of the art in the analysis of Vehicular Ad-Hoc Network (VANET) applications.However, the same need exist for use cases where the mobile users are pedestrians rather than vehicles. Therefore, this paper introduces our current, on-going work on connecting OMNeT++ and Vadere, an open source simulation framework for microscopic pedestrian dynamics, to benefit from state of the art pedestrian mobility models in mobile communication use cases. The presented coupling is based on the existing Traffic Control Interface (TraCI) protocol used in the Veins (Vehicles in Network Simulation) framework to connect OMNeT++ with SUMO.

Human Mobility Patterns

2014 ◽  
pp. 245-273
Author(s):  
Ali Diab ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Information System ◽  
Communication Networks ◽  
Mobile Communication ◽  
Collective Intelligence ◽  
Human Mobility ◽  
Physical World ◽  
Mobility Models ◽  
Mobility Patterns ◽  
Community Based ◽  
Insight Into

It is well accepted that the physical world itself, including communication networks, humans, and objects, is becoming a type of information system. Thus, to improve the experience of individuals, communities, organizations, and societies within such systems, a thorough comprehension of collective intelligence processes responsible for generating, handling, and controlling data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have myriad uses in crucial fields (e.g. mobile communication, urban planning, etc.). The chapter highlights the state of the art and provides a comprehensive investigation of current research efforts in this field. It classifies mobility models into synthetic, trace-based, and community-based models, and also provides insight into each category. That is, well-known approaches are presented, discussed, and qualitatively compared with each other.

Mover

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Wenjun Lyu ◽  
Guang Wang ◽  
Yu Yang ◽  
Desheng Zhang
Keyword(s):  
Tracking System ◽  
Human Mobility ◽  
Theoretical Models ◽  
Autonomous Driving ◽  
Empirical Models ◽  
Lessons Learned ◽  
Use Cases ◽  
Mobility Models ◽  
Mobility Patterns ◽  
Temporal Continuity

Human mobility models typically produce mobility data to capture human mobility patterns individually or collectively based on real-world observations or assumptions, which are essential for many use cases in research and practice, e.g., mobile networking, autonomous driving, urban planning, and epidemic control. However, most existing mobility models suffer from practical issues like unknown accuracy and uncertain parameters in new use cases because they are normally designed and verified based on a particular use case (e.g., mobile phones, taxis, or mobile payments). This causes significant challenges for researchers when they try to select a representative human mobility model with appropriate parameters for new use cases. In this paper, we introduce a MObility VERification framework called MOVER to systematically measure the performance of a set of representative mobility models including both theoretical and empirical models based on a diverse set of use cases with various measures. Based on a taxonomy built upon spatial granularity and temporal continuity, we selected four representative mobility use cases (e.g., the vehicle tracking system, the camera-based system, the mobile payment system, and the cellular network system) to verify the generalizability of the state-of-the-art human mobility models. MOVER methodically characterizes the accuracy of five different mobility models in these four use cases based on a comprehensive set of mobility measures and provide two key lessons learned: (i) For the collective level measures, the finer spatial granularity of the user cases, the better generalization of the theoretical models; (ii) For the individual-level measures, the lower periodic temporal continuity of the user cases, the theoretical models typically generalize better than the empirical models. The verification results can help the research community to select appropriate mobility models and parameters in different use cases.

Trace- and Social-Based Modeling of Human Mobility Patterns

2016 ◽  
pp. 318-354
Author(s):  
Ali Diab ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Human Mobility ◽  
Movement Patterns ◽  
Human Movement ◽  
Mobility Patterns ◽  
Community Based ◽  
Human Movements ◽  
Mobile Communication Networks ◽  
Synthetic Models

The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-based models. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable of generating realistic mobility models. The key idea of trace-based models is the exploitation of available measurements and traces achieved in deployed systems to reproduce synthetic traces characterized by the same statistical properties of real traces. A main drawback of trace-based modeling of human patterns is the tight coupling between the trace-based model and the traces collected, the network topology deployed and even the geographic location, where the traces were collected. This is why the results of various trace-based models deviate clearly from each other. Sure, this prohibits the generalization of trace-based models. When one also considers that the traces themselves are rarely available, one can understand why synthetic models are preferred over trace-based ones. Community-based modeling of human movements depends on the fact stating that mobile devices are usually carried by humans, which implies that movement patterns of such devices are necessarily related to human decisions and socialization behaviors. So, human movement routines heavily affect the overall movement patterns resulting. One of the major contributions in this context is the application of social networks theory to generate more realistic human movement patterns. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of trace- and social-based modeling of human mobility patterns. It reviews well-known approaches going through their pros and cons. In addition, the chapter studies an aspect that heavily relates to human mobility patterns, namely the prediction of future locations of users.

State-dependent stochastic mobility model in mobile communication networks

2010 ◽  
Vol 18 (3) ◽  
pp. 348-365 ◽  
Author(s):  
F.R.B. Cruz ◽  
P.C. Oliveira ◽  
L. Duczmal
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Mobility Model ◽  
State Dependent ◽  
Mobile Communication Networks

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

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