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With the dawn of the Internet of Things (IoT) technology made up of distributed sensors and intelligent terminals that generate much data that traverse the internet to the cloud where it should be saved, several challenges have also risen (Laghari et al., 2021). These challenges include but are not limited to high latency, congestion of network, loss of reliability, poor Quality of Service (QoS) and Quality of Experience (QoE), among other challenges (Michaela et al., 2018). The challenges mentioned above are caused by the geographical distance between cloud computing servers and IoT devices. Fog computing was introduced to bridge the geo-graphical distance and address some of the aforementioned challenges (Michaela et al., 2018) . Ever since fog computing inception by Cisco in 2012, much research has been done in academics and industries. Fog computing has been implemented in different platforms and application areas such as smart homes, smart grids, smart vehicles, and health data management (Laghari et al., 2021) . Moreover, the survey findings by Laghari et al., (2021), Babu et al., (2018) and Vambe et al., (2019) showed that fog computing provides improved QoS in different application areas where it was implemented.
However, due to the ever increase of IoT devices' use, users' need to get a guaranteed user experience. Quality of Service alone is no longer good enough to meet end-user requirements. As such, there is a need to improve fog computing to offer improved QoE since end-users are the ones who are supposed to benefit much from IoT devices. Quality of Experience is not only good for end-users, but network operators can also benefit if and only if they can have the ability to measure it (Nobre, 2018). Network operators' ability to measure QoE will contribute to the overall user's satisfaction regarding reliability, availability, scalability, speed, accuracy, and efficiency (Dr. Jens Berger, 2019)(Krasula et al., 2020). Many networks and other researchers now focus on improving QoE in fog computing to ensure that the user's needs are satisfied with minimum network resources. Thus, guaranteeing QoE to end-users. Therefore, this survey paper seeks to establish the following:
- 1.
What are the similarities and or differences between QoS and QoE in general?
- 2.
What are the QoE application areas, factors, measurements and management techniques?
- 3.
How fog computing has been used to improve QoE in existing systems/applications?
- 4.
What has been done to maintain and improve QoE in fog computing?
- 5.
What are the open research gaps that need attention in fog computing to maintain and or improve QoE?
This paper starts by highlighting the methodology adopted in section 2. Section 3 gives a clear distinction and or similarities, if any, between QoS and QoE to understand QoE. Moreover, a synopsis of QoE application areas and factors are discussed. Followed by a brief discussion of QoE measurements and management techniques, respectively. Section 4 will give an in-depth systematic scrutiny of literature, looking specifically at works whose motive was to improve QoE in fog computing. Open research challenges are pinpointed and discussed in section 5. These open research gaps will act as future research areas in fog computing that need to be addressed to guarantee satisfactory QoE for users. In section 6, the summary of the whole paper is then given as a conclusion.