Opportunistic WiFi Offloading in a Vehicular Environment: An MDP Approach

In recent years, WiFi offloading provides a potential solution for improving ad hoc network performance along with cellular network. This paper reviews the different offloading techniques that are implemented in various applications. In disaster management applications, the cellular network is not optimal for existing case studies because the lack of infrastructure. MANET Wi-Fi offloading (MWO) is one of the potential solutions for offloading cellular traffic. This word combines the cellular network with mobile ad hoc network by implementing the technique of Wi-Fi offloading. Based on the applications requirements the offloading techniques implemented into mobile-to-mobile (M-M), mobile-to-cellular (M-C), mobile-to-AP (M-AP). It serves more reliability, congestion eliminated, increasing data rate, and high network performance. The authors also identified the issue while implementing the offloading techniques in network. Finally, this paper achieved the better performance results compared to existing approaches implemented in disaster management.

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LTE-WIFI Offloading With Realistic Attributes

2019 IEEE Wireless Communications and Networking Conference (WCNC) ◽

10.1109/wcnc.2019.8885978 ◽

2019 ◽

Author(s):

Ferial Hantera ◽

Fadel Digham ◽

Hazim Tawfik

Keyword(s):

Wifi Offloading

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WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

Mobile Information Systems ◽

10.1155/2019/7575037 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Lin Sun ◽

Qi Zhu

Keyword(s):

Heterogeneous Networks ◽

User Satisfaction ◽

Mobile User ◽

User Cost ◽

Analytic Hierarchy ◽

Wifi Networks ◽

Q Learning ◽

Reward Function ◽

Wifi Offloading ◽

Topsis Technique

This paper proposes a WiFi offloading algorithm based on Q-learning and MADM (multiattribute decision making) in heterogeneous networks for a mobile user scenario where cellular networks and WiFi networks coexist. The Markov model is used to describe the changes of the network environment. Four attributes including user throughput, terminal power consumption, user cost, and communication delay are considered to define the user satisfaction function reflecting QoS (Quality of Service), and Q-learning is used to optimize it. Through AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) in MADM, the intrinsic connection between each attribute and the reward function is obtained. The user uses Q-learning to make offloading decisions based on current network conditions and their own offloading history, ultimately maximizing their satisfaction. The simulation results show that the user satisfaction of the proposed algorithm is better than the traditional WiFi offloading algorithm.

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