SLAW: A New Mobility Model for Human Walks

Lee, K.; Hong, Soojung; Kim, S. J.; Rhee, Injong; Chong, Song

doi:10.1109/infcom.2009.5061995

Cited by 448 publications

(333 citation statements)

References 29 publications

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“…Accordingly, for building a trajectory the following steps are required: i) we randomly select the starting point from 1022 PoIs extracted from Wikipedia, ii) we identify the starting point neighbors from the set of PoIs closer than d, iii) we rank the neighboring points by means of a function that minimizes the distance between the candidate PoI and the one currently analyzed; we select the next step in the trajectory according to the score associated to each candidate PoI -the higher the score, the higher the probability of selection, and iv) we terminate the trajectory building when the desired length is reached. The decision to model the interest for a PoI by the distance was influenced by the good results shown in [7]. The resulting synthetic dataset contains 20000 trajectories.…”

Section: Discussionmentioning

confidence: 99%

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A Trajectory-Based Recommender System for Tourism

Baraglia

Frattari

Muntean

et al. 2012

Active Media Technology

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Abstract. Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a priori. We propose several metrics to evaluate both the spatial coverage of the dataset and the quality of recommendations produced. We assess our system on two datasets: a real and a synthetic one. Results show that our solution is a viable one.

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Section: Discussionmentioning

confidence: 99%

“…-Modified Accuracy (MA) refines Accuracy. In [7] is shown that people tend to minimize the distance between locations. Accordingly, a tourist in a region may move to another PoI in the same region or in a different one.…”

Section: Evaluating the Effectiveness And Efficiencymentioning

confidence: 99%

A Trajectory-Based Recommender System for Tourism

Baraglia

Frattari

Muntean

et al. 2012

Active Media Technology

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“…We then averaged the contact rates of users in the same community as well as across communities to implement simulations. Additionally, we also implemented the simulation system using SLAW [15] and INFOCOM05 dataset [16]. In each simulation scenario, there were 35 realistic human mobility traces, among which 12 UEs were randomly selected as helpers.…”

Section: A Evaluation System Setupmentioning

confidence: 99%

“…All the results were averaged over hundreds of simulation trials. Further details about the user traces and simulated region can be founded in [9], [13], [15], [16].…”

Section: A Evaluation System Setupmentioning

confidence: 99%

Impact of Selfishness in Device-to-Device Communication Underlying Cellular Networks

Gao

Zhang

Chen

et al. 2017

IEEE Trans. Veh. Technol.

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Abstract-In a device-to-device (D2D) communication underlaying cellular network, user equipment are required to operate cooperatively and unselfishly to transmit data as relays. However, most users more or less behave in a selfish way, which makes user selfishness a key factor that affects the performance of the whole communication system. We focus on the impact of user selfishness on D2D communications. By separating the user selfishness into two types in accordance with two D2D transmission modes, which are connected D2D transmission and opportunistic D2D transmission, we propose a time-varying graph model that characterizes the impacts of both individual and social selfishness on the D2D communications. Simulation results obtained under the realistic networking settings indicate that the interaction between connected and opportunistic selfishness worsens the impairment caused by individual selfishness. Additionally, when concerning social selfishness, inside-community selfishness can be ignored in some occasions, while otherwise its role is heavily influenced by community numbers.

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“…The class of location-based mobility models aims to realistically represent user mobility patterns in space. They are typically concerned with the regular reappearance to a set of preferred locations [9] or with the length of paths travelled by the users [10]. Similarly, there are models mostly focused on the accurate representation of the time-varying behavior of users, often relying on very detailed schedules of human activities [11] [12].…”

Section: Introductionmentioning

confidence: 99%

SPoT: Representing the social, spatial, and temporal dimensions of human mobility with a unifying framework

Karamshuk

Boldrini

Conti

et al. 2014

Pervasive and Mobile Computing

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Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a flexible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility.Our framework, named SPoT, is able to incorporate the three dimensions -spatial, social, and temporal -of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the flexibility of the framework, and we show that SPoT Preprint submitted to Elsevier July 16, 2013 is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge.

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SLAW: A New Mobility Model for Human Walks

Cited by 448 publications

References 29 publications

A Trajectory-Based Recommender System for Tourism

A Trajectory-Based Recommender System for Tourism

Impact of Selfishness in Device-to-Device Communication Underlying Cellular Networks

SPoT: Representing the social, spatial, and temporal dimensions of human mobility with a unifying framework

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