Group mobility: Detection, tracking and characterization

Nunes, Ivan O.; Melo, Pedro O. S. Vaz de; Loureiro, Antônio A. F.

doi:10.1109/icc.2016.7511488

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…This section presents the main properties of group meetings that could be used to leverage D2D Routing. Further characterization of other group mobility properties may be found in [31]. Figure 5(a) presents the frequency of group reencounters for the MIT Reality Mining, i.e., given the fact that a group first met at time t = 0, how group re-meetings are distributed along the next hours (t hours after the first meeting).…”

Section: Social Group Meetings Propertiesmentioning

confidence: 99%

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GROUPS-NET: Group meetings aware routing in multi-hop D2D networks

et al. 2017

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Abstract-Device-to-device (D2D) communication will allow direct transmission between nearby mobile devices in the next generation cellular networks. A fundamental problem in multi-hop D2D networks is the design of forwarding algorithms that achieve, at the same time, high delivery ratio and low network overhead. In this work, we study group meetings' properties by looking at their structure and regularity with the goal of applying such knowledge in the design of a forwarding algorithm for D2D multi-hop networks. We introduce a forwarding protocol, namely GROUPS-NET, which is aware of social group meetings and their evolution over time. Our algorithm is parameter-calibration free and does not require any knowledge about the social network structure of the system. In particular, different from the state of the art algorithms, GROUPS-NET does not need communities' detection, which is a complex and expensive task. We validate our algorithm by using different publicly available data sources. In real-world large scale scenarios, our algorithm achieves approximately the same delivery ratio of the state-of-the-art solution with 40% less network overhead.

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Section: Social Group Meetings Propertiesmentioning

confidence: 99%

“…This section presents the main properties of group meetings that could be used to leverage D2D Routing. Further characterization of other group mobility properties may be found in [31].…”

Section: Social Group Meetings Propertiesmentioning

confidence: 99%

GROUPS-NET: Group meetings aware routing in multi-hop D2D networks

et al. 2017

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“…Therefore, such models are not representative of the statistical regularity of human interactions, i.e., groups of people that meet regularly. Recent studies [13,14,15,16] have shown that the regularity of group meetings, present in real-world traces, play an important role for content forwarding in mobile opportunistic networks.…”

Section: Doi: 101145/1235mentioning

confidence: 99%

GRM: Group Regularity Mobility Model

Nunes¹,

Celes²,

Silva³

et al. 2017

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In this work we propose, implement, and evaluate GRM, a novel mobility model that accounts for the role of group meeting dynamics and regularity in human mobility. Specifically, we show that existing mobility models for humans do not capture the regularity of human group meetings which is present in real mobility traces. Next, we characterize the statistical properties of such group meetings in real mobility traces and design GRM accordingly. We show that GRM maintains the typical pairwise contact properties of real traces, such as contact duration and inter-contact time distributions. In addition, GRM accounts for the role of group mobility, presenting group meetings regularity and social communities' structure. Finally, we evaluate state-ofart social-aware protocols for opportunistic routing using a synthetic contact trace generated by our model. The results show that the behavior of such protocols in our model is similar to their behavior in real mobility traces.1 GRM synthetic mobility traces containing 100, 1000, and 2000 mobile nodes are available together with a demo video of GRM working on top of The ONE Simulator [21] at: https://www.dropbox.com/sh/792mi849nf3dvam/ AAAR4RofaLBfoFaxmeONe-H4a?dl=0

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“…Dynamic base station switching schemes monitor and model the movements of the group by understanding the utilization of the network. Big data analytics can be used for developing and validating the detection methods to monitor group mobility, validate connected/idle duration of models and simulations based on dynamic switching of users between base stations [20].…”

Section: Introductionmentioning

confidence: 99%

GeoHash tag based mobility detection and prediction for traffic management

2020

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User mobility detection and prediction can help in many ways for planning and monitoring a population distribution and displacement in a specific area or location. It also helps in planning for resource distribution and allocation. In this paper, we present a study on the mobility of the population which can be monitored by sensors, GPS devices through location detection. All the public transports like buses, taxis, etc. have GPS devices posted that can provide movement of a vehicle in city traffic. For experimental analysis, the T-Drive Taxi Trajectories dataset was selected, in which taxi location data are collected with the information of taxi ID, timestamp, latitude, and longitude. GeoHash tags were generated for the location of a taxi based on latitude and longitude. A graph was built based on vertices as GeoHash tag and edges as a direct link between the GeoHash tags. Graph-Based data analysis was applied to identify the importance of GeoHash tag based on the in-degree and PageRank of the vertices. The mobility path and movement of traffic can be predicted that can be used for disaster management and urban development for city planning.

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Group mobility: Detection, tracking and characterization

Cited by 9 publications

References 20 publications

GROUPS-NET: Group meetings aware routing in multi-hop D2D networks

GROUPS-NET: Group meetings aware routing in multi-hop D2D networks

GRM: Group Regularity Mobility Model

GeoHash tag based mobility detection and prediction for traffic management

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