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 Penerapan Wireless Distribution System (WDS) Mesh Untuk Optimasi Cakupan Area Wi-Fi di UM Jember

2021 ◽  
Vol 1 (2) ◽  
pp. 61-69
Author(s):  
Achmad Riyadus Sholikhin ◽  
Taufiq Timur Warisaji ◽  
Triawan Adi Cahyanto
Keyword(s):  
Distribution System ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Signal To Noise Ratio ◽  
Rapid Change ◽  
Signal Strength ◽  
Access Point ◽  
Local Area ◽  
Area Network ◽  
One Step

One very rapid change in computer networks is the use of Wireless Local Area Network (WLAN) to access systems both locally and the internet. One way to add an Access Point (AP) so as not to change the configuration of the running software is use the Wireless Distribution System Mesh (WDS) network. The use of WDS mesh at Universitas Muhammmadiyah Jember (UM Jember) enables a slightly different wireless configuration to reduce the connection loss in the user due to the wireless network area coverage. One step is use AP devices into one unit to handle the case of a connection break by utilizing a network service that is WDS Mesh. WDS Mesh can cover Wi-Fi areas at UM Jember, and the results of testing and analysis of client devices for APs that have been configured WDS Mesh with parameters Signal Strength, CCQ, Signal to Noise Ratio Ratio, Throughput is Excellent.

scholarly journals Performance Characterization of Load Balancing Algorithm

2021 ◽  
Vol 13 (3) ◽  
pp. 915-922
Author(s):  
R. Krishan
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Local Area ◽  
Network Load ◽  
Mobile Stations ◽  
Riverbed Modeler ◽  
Load Balancing Algorithm ◽  
Access Services ◽  
Extensive Performance

The developing interest in mobile services increases the demand for well-planned and cautiously managed wireless local area networks (WLAN) deployment. In WLAN, a station can access services of the network through an access point (AP) after associating with it. Any number of access points can be accessed by the station whose signal strength is available from among the APs. But practically, a WLAN station (STA) always associates with the access point with higher signal strength among the APs. In WLAN, mobile stations continuously change their location, which results in an uneven network load allocation. This uneven load dissemination prompts an extensive performance degradation of WLAN.  This paper presents mathematical modeling to characterize the WLAN performance by balancing the network load and enhancing network throughput. Riverbed Modeler simulator was used to investigate the performance parameters as network load and throughput of the network.

scholarly journals Mining Users Mobility at Public Transportation

2017 ◽  
Vol 20 (59) ◽  
pp. 32 ◽  
Author(s):  
Joao Ferreira
Keyword(s):  
Mobile Device ◽  
Public Transportation ◽  
Research Work ◽  
Access Point ◽  
Wireless Access ◽  
User Mobility ◽  
Mobility Patterns ◽  
New Approach ◽  
Useful Knowledge ◽  
User Intervention

In this research work we propose a new approach to estimate the number of passengers in a public transportation and determinate the users’ route path based on a passive approach without user intervention. The method is based on the probe requests of users mobile device through the collected data in wireless access point. This data is manipulated to extract the information about the numbers of users with mobile devices and track their route path and time. This data can be manipulated to extract useful knowledge related with users’ habits at public transportation and extract user mobility patterns.

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 Security Mechanism for user access to Single SSID WLAN

2020 ◽  
Vol 245 ◽  
pp. 07009
Author(s):  
Li Wang ◽  
Mingshan Xia ◽  
Fazhi Qi
Keyword(s):  
Access Control ◽  
Wireless Network ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Mobile Station ◽  
Access Point ◽  
Local Area ◽  
Area Network ◽  
Office Work ◽  
Network Resources

Wireless local area network (WLAN) technology is widely used in various enterprises and institutions. In order to facilitate the use of users, they often provide a single SSID access point, resulting in different identities of users authenticated and authorized can connect to the wireless network anytime, anywhere as needed and obtain the same accessible network resources such as bandwidth, access control (ACL) and so on. Multiple SSID can solve the problem but it will be confused to users who don’t know which SSID can be connected. Although we could prevent visitors from accessing intranet resources by isolating the wireless network from the internal network, this would make it impossible for users to use the wireless network for internal office work. In this paper, we propose an access control system that grouping users according to the different identities and users authenticated and authorized can access different network resources because a wireless access point dynamically maps an SSID provided by a mobile station to a BSSID based on a VLAN assignment. The deployment experiment of the solution proves that users of different identities accessing the same wireless network can set different access policies, which effectively improves the security of the wireless network and simplifies the management of the wireless network.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

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.

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