MOBILITY PREDICTION METHOD FOR VEHICULAR NETWORK USING MARKOV CHAIN

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Arfah Hasbollah ◽  
Sharifah H. S. Ariffin ◽  
N. Fisal
Keyword(s):  
Markov Chain ◽  
Prediction Method ◽  
Vehicular Network ◽  
Mobile User ◽  
Mobility Prediction ◽  
Moving Vehicle ◽  
Handover Procedure ◽  
User Data ◽  
Short Time ◽  
Location Zone

This paper proposes mobility prediction technique via Markov Chains with an input of user’s mobile data traces to predict the user’s movement in wireless network. The main advantage of this method is prediction will give knowledge of user’s movement in advance even in fast moving vehicle. Furthermore, the information from prediction result will be use to assist handover procedure by reserve resource allocation in advance in vehicular network. This algorithm is simple and can be computed within short time; thus the implementation of this technique will give the significant impact especially on higher speed vehicle. Finally, an experiment is performed using real mobile user data traces as input for Markov chain to predict next user movement. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out with several users under same location zone.  The results show that the proposed method predicts have good performance which is 30% of mobile users achieved 100% of prediction accuracy.

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.

Mobile User Data Mining and Its Applications

2011 ◽  
pp. 216-232
Author(s):  
J. Goh
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

scholarly journals On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

2020 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
Siddharth Siddharth ◽  
Mohan M. Trivedi
Keyword(s):  
New Technologies ◽  
Vision System ◽  
Driver Assistance Systems ◽  
Sensing Systems ◽  
Autonomous Mode ◽  
Skin Response ◽  
User Data ◽  
Multiple Sensor ◽  
Short Time ◽  
Awareness Context

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

scholarly journals Method of Resource Estimation Based on QoS in Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guangshun Li ◽  
Jianrong Song ◽  
Junhua Wu ◽  
Jiping Wang
Keyword(s):  
Markov Chain ◽  
Prediction Method ◽  
Edge Computing ◽  
Resource Estimation ◽  
Matching Method ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Delay Sensitive ◽  
Penalty Factor

With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy.

scholarly journals A Prediction Method of Mobile User Preference Based on the Influence between Users

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yancui Shi ◽  
Jianhua Cao ◽  
Congcong Xiong ◽  
Xiankun Zhang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Prediction Method ◽  
Mobile User ◽  
User Preferences ◽  
User Preference ◽  
Context Information ◽  
Mobile Social Network ◽  
Data Set ◽  
Interaction Behavior

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

scholarly journals A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yajun Zhou ◽  
Lilei Wang ◽  
Rong Zhong ◽  
Yulong Tan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Prediction Method ◽  
Chain Model ◽  
Daily Production ◽  
Forecasting Accuracy ◽  
Traffic Demand ◽  
Demand Prediction ◽  
Proposed Model ◽  
Bike Sharing

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

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