Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people’s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user’s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard naïve Bayesian and some of the state of the art predictors.

Predicting encounter and colocation events / K. Keramat Jahromi, M. Zignani, S. Gaito, G.P. Rossi. - In: AD HOC NETWORKS. - ISSN 1570-8705. - 62(2017 Jul), pp. 11-21. [10.1016/j.adhoc.2017.04.004]

Predicting encounter and colocation events

K. Keramat Jahromi
Primo
;
M. Zignani
Secondo
;
S. Gaito
Penultimo
;
G.P. Rossi
Ultimo
2017

Abstract

Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people’s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user’s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard naïve Bayesian and some of the state of the art predictors.
human mobility; encounter and colocation prediction; weighted features Bayesian predictor
Settore INF/01 - Informatica
lug-2017
6-ott-2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/469839
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