EMP: Exploiting Mobility Patterns for Collaborative Localization in Sparse Mobile Networks

2012 ◽  
Author(s):  
Zhenzhen Tian ◽  
Yanmin Zhu
Keyword(s):  
Mobile Networks ◽  
Mobility Patterns ◽  
Collaborative Localization

scholarly journals A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks

Telecom ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 199-212
Author(s):  
Nasrin Bahra ◽  
Samuel Pierre
Keyword(s):  
Mobile Networks ◽  
Network Capacity ◽  
Vertical Handover ◽  
Mobility Model ◽  
Mobility Prediction ◽  
User Mobility ◽  
Context Modeling ◽  
Handover Management ◽  
Mobility Patterns ◽  
Prediction Approach

Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

Using Personal Cell Phones for Ecological Momentary Assessment

2013 ◽  
Vol 18 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Emmanuel Kuntsche ◽  
Florian Labhart
Keyword(s):  
Data Collection ◽  
Ecological Momentary Assessment ◽  
Communication Systems ◽  
Cell Phones ◽  
Personal Digital Assistants ◽  
Natural Environments ◽  
Mobility Patterns ◽  
Standard Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Ecological Momentary Assessment (EMA) is a way of collecting data in people’s natural environments in real time and has become very popular in social and health sciences. The emergence of personal digital assistants has led to more complex and sophisticated EMA protocols but has also highlighted some important drawbacks. Modern cell phones combine the functionalities of advanced communication systems with those of a handheld computer and offer various additional features to capture and record sound, pictures, locations, and movements. Moreover, most people own a cell phone, are familiar with the different functions, and always carry it with them. This paper describes ways in which cell phones have been used for data collection purposes in the field of social sciences. This includes automated data capture techniques, for example, geolocation for the study of mobility patterns and the use of external sensors for remote health-monitoring research. The paper also describes cell phones as efficient and user-friendly tools for prompt manual data collection, that is, by asking participants to produce or to provide data. This can either be done by means of dedicated applications or by simply using the web browser. We conclude that cell phones offer a variety of advantages and have a great deal of potential for innovative research designs, suggesting they will be among the standard data collection devices for EMA in the coming years.

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