Human mobility models for opportunistic networks

2011 ◽  
Vol 49 (12) ◽  
pp. 157-165 ◽  
Author(s):  
Dmytro Karamshuk ◽  
Chiara Boldrini ◽  
Marco Conti ◽  
Andrea Passarella
Keyword(s):  
Human Mobility ◽  
Opportunistic Networks ◽  
Mobility Models

scholarly journals How Human Mobility Models Can Help to Deal with COVID-19

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Enrique Hernández-Orallo ◽  
Antonio Armero-Martínez
Keyword(s):  
Human Mobility ◽  
Opportunistic Networks ◽  
Mobility Models ◽  
Key Factors ◽  
Mobile Computer ◽  
Heat Map ◽  
Health Authorities ◽  
Spatial Risk ◽  
Management Plans ◽  
Human Infections

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.

Human mobility models reveal the underlying mechanism of seasonal movements across China

2021 ◽  
Author(s):  
Bing Song ◽  
Xiao-Yong Yan ◽  
Suoyi Tan ◽  
Bin Sai ◽  
Shengjie Lai ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Human Mobility ◽  
Underlying Mechanism ◽  
Gravity Models ◽  
Mobility Models ◽  
Temporal And Spatial Distribution ◽  
Population Movements ◽  
Different Types ◽  
Before And After ◽  
Time Periods

Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow” effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

scholarly journals On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

2017 ◽  
Vol 38 ◽  
pp. 215-232 ◽  
Author(s):  
Marcello Tomasini ◽  
Basim Mahmood ◽  
Franco Zambonelli ◽  
Angelo Brayner ◽  
Ronaldo Menezes
Keyword(s):  
Human Mobility ◽  
Opportunistic Networks ◽  
Mobile Opportunistic Networks

Survey on Node Mobility Model for Opportunistic Network

2011 ◽  
Vol 52-54 ◽  
pp. 1253-1257 ◽  
Author(s):  
Ming Xia Yang ◽  
Shuang Xia Han ◽  
Cai Yun Yang ◽  
Lu Zhang ◽  
Dong Fen Ye
Keyword(s):  
Wireless Sensor Networks ◽  
Ad Hoc ◽  
Mobility Model ◽  
Opportunistic Networks ◽  
Wireless Sensor ◽  
Node Mobility ◽  
Mobility Models ◽  
Opportunistic Network ◽  
Research Focus ◽  
Mobile Ad Hoc

Opportunistic networks is one of the newest hot research spots in wireless networks after mobile ad hoc net-works(MANET) and wireless sensor networks(WSN). Mobility model describes mobility manners of nodes. It has been widely used in research on wireless network. This paper firstly introduced, classifies, and compares the current familiar mobility models. Secondly, it classifies, and compares the current familiar mobility models. Next, it was discussed that current research focus on new mobility models, analysis of nodes mobility features, trace strategy, and evaluation of mobility model. Finally, this paper involved what calls for further study.

scholarly journals Community-based Mobility Model and Probabilistic ORBIT Mobility Model Implementations in OMNeT++

10.29007/4tv9 ◽  
2018 ◽  
Author(s):  
Vishnupriya Kuppusamy ◽  
Leonardo Sarmiento ◽  
Asanga Udugama ◽  
Anna Förster
Keyword(s):  
Real World ◽  
Human Mobility ◽  
Mobility Model ◽  
Mobility Models ◽  
Community Based ◽  
Opportunistic Networking

Simulations of Opportunistic Networking (OppNet) protocols require the use of suitable synthetic mobility models or real world traces. Many synthetic mobility models have been proposed based on the study of human mobility individually and in groups. Opportunistic Protocol Simulator (OPS) is a budding simulator which is based on OMNeT++ to simulate OppNets. However, compared to other OppNet simulators in the literature, only very few synthetic mobility models exist in OMNeT++ currently, restricting the simulation of OppNets to using the existing mobility models or traces. In this paper, we develop two more synthetic mobility models in OMNeT++ namely community-based mobility model and probabilistic ORBIT based mobility, which can enhance the simulating environment available for OppNets in OMNeT++.

scholarly journals Deriving fine-scale models of human mobility from aggregated origin-destination flow data

2021 ◽  
Vol 17 (2) ◽  
pp. e1008588
Author(s):  
Constanze Ciavarella ◽  
Neil M. Ferguson
Keyword(s):  
Spatial Scale ◽  
Gravity Model ◽  
Spatial Resolution ◽  
Human Mobility ◽  
Parameter Estimates ◽  
Mobility Models ◽  
Fine Scale ◽  
Flow Data ◽  
Mobility Data ◽  
Movement Data

The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource-poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to ∼ 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit.

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.

Sign in / Sign up

Export Citation Format

Share Document