A hotspot attraction driven user mobility model and direction deciding algorithm

2010 ◽  
Author(s):  
Hao Guo ◽  
Zhipeng Gao ◽  
Heng Zhang ◽  
Zhili Guan ◽  
Xingyu Chen ◽  
...  
Keyword(s):  
Mobility Model ◽  
User Mobility

scholarly journals Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 148 ◽  
Author(s):  
Qiong Wu ◽  
Hanxu Liu ◽  
Cui Zhang ◽  
Qiang Fan ◽  
Zhengquan Li ◽  
...  
Keyword(s):  
Mobility Model ◽  
Location Based Services ◽  
The Internet ◽  
User Mobility ◽  
Continuous Location ◽  
Trajectory Data ◽  
Location Selection ◽  
Geographic Feature ◽  
Feature Information ◽  
The Internet Of Things

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

scholarly journals Effect of User Mobility upon Trust Building among Autonomous Content Routers in an Information-Centric Network

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jerzy Konorski ◽  
Jakub Grochowski
Keyword(s):  
Autonomous Agents ◽  
High Mobility ◽  
Mobility Model ◽  
Trust Building ◽  
User Mobility ◽  
Negative Effect ◽  
Current Content ◽  
Content Availability ◽  
The One ◽  
Willingness To Cooperate

The capability of proactive in-network caching and sharing of content is one of the most important features of an information-centric network (ICN). We describe an ICN model featuring autonomous agents controlling the content routers. Such agents are unlikely to share cached content with other agents without an incentive to do so. To stimulate cooperation between agents, we adopt a reputation and trust building scheme that is able to explicitly account for both objective current content availability and subjective willingness to cooperate. The scheme is further complemented with a so-called one-time goodwill mechanism introduced to avoid penalizing agents failures to provide temporarily unavailable content. In a simulated ICN environment under a modified Random Waypoint user mobility model, we investigate the resiliency of the reputation and trust building scheme to subversion, that is, strategic (selfish or malicious) agents acquiring higher trust values than honest ones, for varying user mobility scenarios. The scheme proves resilient in low-mobility scenarios, while increased user mobility is shown to have a negative effect. The one-time goodwill mechanism partly remedies this for high-mobility scenarios. We validate the results by comparison with an existing reputation and trust building scheme and with an alternative user mobility model.

A Purpose-Driven User Mobility Model for Opportunistic Networks

2010 ◽  
Vol 171-172 ◽  
pp. 804-809
Author(s):  
Jian Bo Xu ◽  
Guang Yang
Keyword(s):  
Human Movement ◽  
Mobility Model ◽  
Opportunistic Networks ◽  
User Mobility ◽  
Node Mobility ◽  
Opportunistic Network ◽  
Data Forwarding ◽  
Challenging Tasks ◽  
Efficient Data ◽  
Two Sides

An opportunistic Network is a network consisting exclusively of users’ mobile devices, with mobility being one of its essential features. Under the circumstances that a path may never exist between the two sides of communication, an opportunistic network exploits node mobility to realize delayed data delivery by capturing the opportunities of node meeting to relay messages. Designing efficient data forwarding strategies is one of the most challenging tasks in opportunistic network research, while currently the validation of any protocol for data forwarding almost absolutely relies on simulations of which node mobility models are one of the fundamental components. In this paper, we suggest a purpose-driven user mobility model for opportunistic networks which, to our best knowledge, is the first work considering the factor of purposes behind users’ movement. On the basis of location functionalization, our model can gain a better approximation of human movement patterns.

scholarly journals Advanced Resources Reservation in Mobile Cellular Networks: Static vs. Dynamic Approaches under Vehicular Mobility Model

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 302-327
Author(s):  
Peppino Fazio ◽  
Mauro Tropea
Keyword(s):  
Bandwidth Allocation ◽  
Dynamic Networks ◽  
Mobility Model ◽  
User Mobility ◽  
Prediction Errors ◽  
Mobile Nodes ◽  
System Utilization ◽  
Mobile Cellular ◽  
Average System ◽  
Probability Number

Many studies in literature have shown that the bandwidth of an ongoing flow can dynamically change during multimedia sessions and an efficient bandwidth allocation scheme must be employed. This paper focuses its attention on the management of predictive services in Wireless Infrastructure Dynamic Networks. In particular, two classes of service are considered: NSIS-Mobility Independent Predictive and NSIS-Mobility Dependent Predictive, where NSIS is the Next Steps in Signaling protocol, employed for resources reservation in Integrated Services architectures. A general prediction technique is proposed, based both on the analysis of time spent into a cell by mobile nodes and on the probabilities of hand-in and hand-out events of mobile nodes from wireless cells. User mobility needs to be firstly analyzed and a novel realistic mobility model has been considered, differently from some existing works in which synthetic mobility is generated. The analysis of user mobility is mandatory when the reduction of passive resource reservations for NSIS-MIP users is desired, with a good enhancement in system utilization. Moreover, predictive reservation and admission control schemes have been integrated. The performance of the 2D wireless system is evaluated in terms of average system utilization, system outage probability, number of admitted flows and reservation prediction errors. We provided to carry out an extensive simulation campaign, in order to assess the goodness of the proposed idea: we verified that good results (in terms of perceived utility, bandwidth and admitted flows) can be obtained, outperforming also some existing works.

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%.

Sign in / Sign up

Export Citation Format

Share Document