Open Access

ARTICLE

Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

by Mahmoud Alawi¹, Raed Alsaqour², Abdi Abdalla³, Maha Abdelhaq^4,*, Mueen Uddin⁵

1 Department of Computer Engineering and IT, Faculty of Engineering, Zanzibar University, Zanzibar, Tanzania
2 Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh, 93499, Saudi Arabia
3 Department of Electronics and Telecommunications Engineering, College of ICT, University of Dar es Salaam, Tanzania
4 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
5 Faculty of Science, Universiti Brunei Darussalam, Negara Brunei Darussalam

* Corresponding Author: Maha Abdelhaq. Email:

Computers, Materials & Continua 2021, 69(2), 2313-2337. https://doi.org/10.32604/cmc.2021.018282

Received 03 March 2021; Accepted 08 April 2021; Issue published 21 July 2021

Abstract

The growing demands of vehicular network applications, which have diverse networking and multimedia capabilities that passengers use while traveling, cause an overload of cellular networks. This scenario affects the quality of service (QoS) of vehicle and non-vehicle users. Nowadays, wireless fidelity access points Wi-Fi access point (AP) and fourth generation long-term evolution advanced (4G LTE-A) networks are broadly accessible. Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks. However, utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task. This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs. Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision. However, depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available. The present study proposed a multi-criteria wireless availability prediction (MWAP) mechanism, which utilizes historical connection patterns, historical data rate information, and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions. The proposed mechanism is decentralized, where each vehicle makes the prediction by itself. This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications. A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms. A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios. The MWAP mechanism exhibited a lower prediction error (i.e., below 20%) and higher offloading ratio (i.e., above 90%) than the existing mechanisms for several tested scenarios.

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