scholarly journals Congestion-Optimal WiFi Offloading with User Mobility Management in Smart Communications

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Bin Liu ◽  
Qi Zhu ◽  
Weiqiang Tan ◽  
Hongbo Zhu
Keyword(s):  
Mobility Management ◽  
Access Point ◽  
Subgradient Method ◽  
Equivalent Transformation ◽  
User Mobility ◽  
Network Congestion ◽  
Performance Simulation ◽  
Dynamical Nature ◽  
Lower Complexity ◽  
Wifi Offloading

We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion-Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion-Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.

A Novel Approach to Detect the Level of Congestion in a Wireless Network

2020 ◽  
Vol 17 (4) ◽  
pp. 1688-1691
Author(s):  
V. Balaji ◽  
T. K. S. Rathish Babu ◽  
A. Anitha Juliette ◽  
V. Anantha Krishna
Keyword(s):  
Wireless Network ◽  
Task Force ◽  
Access Point ◽  
Network Congestion ◽  
Wireless Monitoring ◽  
Novel Approach ◽  
The Status

Congestion in a network is due to the lower bandwidth in the wireless part as compared to the wired one. Extensive planning has to be made on the wireless network side as it is challenging to predict the number of nodes active which are connected to the network over a period of time. In this paper we study the link between the RE-Transmission Timer with respect to network congestion. We are proposing a cluster based methodology to determine the status of the of access point with respect to the channel which will help us to plan better network. We use the traces collected from wireless monitoring at the 62nd Internet Engineering Task Force (IETF) meeting held in Minneapolis, MN, March, 2005.

Periodic properties of user mobility and access-point popularity

2006 ◽  
Vol 11 (6) ◽  
pp. 465-479 ◽  
Author(s):  
Minkyong Kim ◽  
David Kotz
Keyword(s):  
Access Point ◽  
User Mobility

Erratum to: Wireless LAN Access Point Placement Based on User Mobility

2011 ◽  
Vol 60 (3) ◽  
pp. 441-441
Author(s):  
Mohd Fikri Azli bin Abdullah ◽  
Muhammad Taufiq Nuruzzaman ◽  
Deokjai Choi
Keyword(s):  
Wireless Lan ◽  
Access Point ◽  
User Mobility ◽  
Point Placement

scholarly journals AMF Optimal Placement based on Deep Reinforcement Learning in Heterogeneous Radio Access Network

2020 ◽  
Author(s):  
hao jin ◽  
Wenzhe Pang ◽  
Chenglin Zhao
Keyword(s):  
Reinforcement Learning ◽  
Mobility Management ◽  
Network Architecture ◽  
Access Network ◽  
Optimal Placement ◽  
Network Function Virtualization ◽  
User Mobility ◽  
Radio Access Network ◽  
Network Function ◽  
Radio Access

Abstract To support various service requirements such as massive Machine Type Communications, Ultra-Reliable and Low-Latency Communications in 5G scenario, Network Function Virtualization (NFV) plays an important role in the 5G network architecture to manage and orchestrate network services. As the key network function responsible for mobility management, Access and Mobility Management Function (AMF) can be deployed flexibly at the edge of the radio access network to improve the performance of mobility management based on NFV. In this paper, the optimal placement of AMF is addressed based on Deep Reinforcement Learning (DRL) in a heterogeneous radio access network, which aims to minimize the network utility including the average delay of mobility management requests at AMF, the average wired hops to relay the requests and the cost of AMF instances. By considering time-varying features including user mobility and the arrival rate of user mobility management requests, an AMF optimal placement approach is proposed for the long term optimization. Simulation results show that the performance of the proposed DRL based AMF optimal placement approach outperforms that of the baselines.

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 Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Ananthi Jebaseeli Samuelraj ◽  
Sundararajan Jayapal
Keyword(s):  
Mobility Management ◽  
Research Work ◽  
Mobile Network ◽  
Access Point ◽  
Mobile Node ◽  
Support Network ◽  
Node Mobility ◽  
Network Mobility ◽  
Management Protocol ◽  
Mobility Management Protocol

Proxy Mobile IPV6 (PMIPV6) is a network based mobility management protocol which supports node’s mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO) in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node’s mobility should be modified to support group nodes’ mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

scholarly journals A QoS/Mobility-Aware Model for Mobile Internet

2011 ◽  
Vol 7 (2) ◽  
pp. 41
Author(s):  
Nuno Vasco Lopes ◽  
Maria Joao Nicolau ◽  
Alexandre Santos
Keyword(s):  
Resource Utilization ◽  
Mobility Management ◽  
Model Simulation ◽  
Mobile Ip ◽  
Mobile Internet ◽  
Network Congestion ◽  
Bandwidth Utilization ◽  
Micro Mobility ◽  
High Dynamic ◽  
Mobility Solution

This paper proposes a QoS micro-mobility solution capable of providing QoS support for global mobility. The solution comprises enhancements with regards to the mobility management of Mobile IPv6 (MIPv6) and in the resourcemanagement of the Differentiated Services (DiffServ) QoS model. The mobility management of MIPv6 was extended with fast and local handovers in order to improve its efficiency in high dynamic micro-mobility scenarios.The DiffServ resource management was extended with adaptive and dynamic QoS provisioning in order to improve resource utilization in mobile IP networks. Furthermore, to improve resource utilization, the mobility and QoS messages have been coupled so that resource managementable to proactively react to mobility events can be attained.The performance improvement of the proposed solution inaddition to the model parametrization have been evaluated using a simulation model. Simulation results indicate that the solution avoids network congestion as well as the starvation of DiffServ classes of a lesser priority. Moreover, the results also indicate that bandwidth utilization for priority classes increased. The QoS offered to MN’s applications, in each DiffServ class, remained unchanged with MN mobility.

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