Group vertical handoff management in heterogeneous networks

Abstract

Traditional vertical handover schemes postulate that vertical handovers (VHOs) of users come on an individual basis. This enables users to know previously the decision already made by other users, and then the choice will be accordingly made. However, in case of group mobility, almost all VHO decisions of all users, in a given group (e.g., passengers on board a bus or a train equipped with smart phones or laptops), will be made at the same time. This concept is called group vertical handover (GVHO). When all VHO decisions of a large number of users are made at the same time, the system performance may degrade and network congestion may occur. In this paper, we propose two fully decentralized algorithms for network access selection, and that is based on the concept of congestion game to resolve the problem of network congestion in group mobility scenarios. Two learning algorithms, dubbed Sastry Algorithm and Q-Learning Algorithm, are envisioned. Each one of these algorithms helps mobile users in a group to reach the nash equilibrium in a stochastic environment. The nash equilibrium represents a fair and efficient solution according to which each mobile user is connected to a single network and has no intention to change his decision to improve his throughput. This shall help resolve the problem of network congestion caused by GVHO. Simulation results validate the proposed algorithms and show their efficiency in achieving convergence, even at a slower pace. To achieve fast convergence, we also propose a heuristic method inspired from simulated annealing and incorporated in a hybrid learning algorithm to speed up convergence time and maintain efficient solutions. The simulation results also show the adaptability of our hybrid algorithm with decreasing step size-simulated annealing (DSS-SA) for high mobility group scenario.

Secure Vertical Handover to NEMO Using Hybrid Cryptosystem

“…In host mobility, deciding to select the best network is very smooth in the case of NEMO or group mobility; several users are using different applications, and choosing the right network is very difficult. Walid et al [24] proposed to select a better network selection mechanism called group vertical handover to group mobility-based architecture by considering user preferences and congestion parameters. ey used two algorithms to calculate congestion, i.e., dubbed Sastry and O-Learning algorithms.…”

Two‐sided matching framework for optimal user association in 5G multi‐RAT UDNs

“…The constraints (15) and (16) mean that each   can be affected to at most one BS in the downlink transmission. Each optimization problem (P1, P2) is considered as a nonlinear optimization problem, which is an NP-hard problem, because it contains binary variables (X,Y).…”

“…Based on numerical results, they have deduced the necessity of balancing between these two modes to maximize the network performances. Walid et al deal with the network congestion issues in the context of group mobility scenarios in multi‐RAT networks. The bus passengers on board moving inside a multi‐RAT coverage may selfishly perform vertical handoff decisions that can occur extremely at the same time.…”

User Quality of Experience (QoE) prediction in Heterogeneous Mobile Networks

“…Inevitably, operators have no options but to provide high communication quality under more complex and dynamic networking conditions [1].These developments are evidence of the co-existence of the various network technologies such as (2G/3G/4G) with variable weighting by country and zones around the globe. A heterogeneous network consists of different Radio Access Technologies (RAT), which includes among others High-Speed-DownlinkpacketAccess (HSDPA), LongTermEvolution (LTE), Wireless Local Area -Network (LAN), and Worldwide Interoperability for Microwave Access (WiMAX) networks [2].…”