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%.

scholarly journals Conceptual mobility model of vertical handover decision in heterogeneous networks

2019 ◽  
Vol 13 (3) ◽  
pp. 1143
Author(s):  
Norakmar Arbain ◽  
Zolidah Kasiran
Keyword(s):  
Heterogeneous Network ◽  
Vertical Handover ◽  
Mobility Model ◽  
Mobile Node ◽  
User Mobility ◽  
Handover Management ◽  
Handover Decision ◽  
Network Availability ◽  
Pattern Movement ◽  
Decision Algorithms

In heterogeneous network, maintaining seamless connectivity needs excessive efforts from various aspects such as network availability and mobile node reliability. Presently, a vertical handover management is a practical approach in facilitating the service continuity for mobile users. Many researches have been conducted in this area by considering performance improvement in delay, latency, and overhead. Preserving the Quality of Services (QoS) based on user mobility and pattern movement during handover decision has become an important aspect in vertical handover management. This paper presents the conceptual mobility model of vertical handover decision in heterogeneous network. Hence, several researches in vertical handover decision management has been reviewed regarding the issues on the vertical handover decision algorithms such as RSS Based Algorithm, MADM Based Algorithm and Intelligence Based Algorithm.  This paper highlights the current decision algorithms that integrate the traditional methods with intelligence algorithm for better optimization. In decision parameters, the user mobility pattern can be importance in terms of direction randomness and mobility speed.  Hence, a conceptual mobility-awareness model for vertical handover are been proposed in targeting some improvement of handover performance.

Cooperation Strategies for P2P Content Distribution in Cellular Mobile Networks

2011 ◽  
pp. 152-165
Author(s):  
Tobias Hoßfeld ◽  
Michael Duelli ◽  
Dirk Staehle ◽  
Phuoc Tran-Gia
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Content Distribution ◽  
Vertical Handover ◽  
Mobility Model ◽  
Computational Time ◽  
Heterogeneous Environments ◽  
Cellular Mobile ◽  
The Impact

The performance of P2P content distribution in cellular networks depends highly on the cooperation and coordination of heterogeneous and often selfish mobile users. The major challenges are the identification of problems arising specifically in cellular mobile networks and the development of new cooperation strategies to overcome these problems. In the coherent previous chapter, the authors focused on the selfishness of users in such heterogeneous environments. This discussion is now extended by emphasizing the impact of mobility and vertical handover between different wireless access technologies. An abstract mobility model is required to allow the performance evaluation in feasible computational time. As a result, the performance in today’s and future cellular networks is predicted and new approaches to master heterogeneity in cellular networks are derived.

scholarly journals A Cooperative Two-Step Vertical Handoff Scheme with Mobility Prediction

2021 ◽  
Vol 1 (3) ◽  
pp. 75-87
Author(s):  
Shih Yu Chang ◽  
Pin-Han Ho
Keyword(s):  
Mobile Station ◽  
Signal Strength ◽  
Access Point ◽  
Video On Demand ◽  
Local Area ◽  
Analytical Models ◽  
Vertical Handoff ◽  
Mobility Prediction ◽  
User Mobility ◽  
Mobility Patterns

As mobile communication evolves into 3G beyond, the interworking of multiple heterogeneous networks serves as the major effort for taking the best advantage of different technologies available in supporting various emerging services, such as VoIP, Video on Demand (VoD), and IP Television (IPTV), etc. Vertical handoff is one of the key mechanisms in achieving Always Best Connected (ABC) for the mobile users by leveraging the benefits of deploying different types of networks for provisioning seamless handoff/roaming services in presence of user mobility. This paper aims to introduce a novel cooperative two-step vertical handoff scheme for the integration of 3G Wireless Wide-Area Networks (WWAN) and the IEEE 802.11 Wireless Local-Area Networks (WLANs). The proposed scheme is based on the cooperation based access point (AP) and mobile station (MS), where the AP manipulates the sensed signal strength to determine whether a pre-handoff action should be initiated. To improve the accuracy of user mobility prediction, a Markov model that incorporates with a novel parameter training process is developed at the AP for acquiring the hotspot geographic arrangement, such as the location of aisles, walls, and entrances/exits, etc., which is considered as the major factor of determining the user mobility patterns in an indoor hotspot. We will justify feasibility and discuss the operation complexity of the proposed cooperative vertical handoff. Moreover, error propagation due to inaccurate signal strength measurement is studied through Maximum Likelihood estimation. Finally, we will clearly demonstrate the merits gained by using the proposed two-step vertical handoff mechanism through extensive simulation, where the derived analytical models are verified.

scholarly journals A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandru Topîrceanu ◽  
Radu-Emil Precup
Keyword(s):  
Computational Models ◽  
Human Mobility ◽  
Discrete Event ◽  
Mobility Model ◽  
Hierarchical Organization ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Fine Grained ◽  
Epidemic Size ◽  
Control Of Epidemics

AbstractComputational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

Sign in / Sign up

Export Citation Format

Share Document