Linking Users' Subjective QoE Evaluation to Signal Strength in an IEEE 802.11b/g Wireless LAN Environment

Moor, Katrien De; Joseph, Wout; Ketykó, István; Tanghe, Emmeric; Deryckere, Tom; Martens, Luc; Marez, Lieven De

doi:10.1155/2010/541568

Cited by 19 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On average, participants rated using Dropbox on high-speed mobile network higher than when using Wi-Fi (p<0.01, Fisher's exact test) ( Figure 5). We found no significant effect of network signal strength and QoE, however we know this to not be true [18], surely a consequence of our limited samples of signal strength while on a mobile network (N=49). …”

Section: Effect Of Network Contextmentioning

confidence: 61%

See 1 more Smart Citation

Mobile cloud storage

Vandenbroucke

Ferreira

Gonçalves

et al. 2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

Self Cite

View full text Add to dashboard Cite

In an increasingly connected world, users access personal or shared data, stored "in the cloud" (e.g., Dropbox, Skydrive, iCloud) with multiple devices. Despite the popularity of cloud storage services, little work has focused on investigating cloud storage users' Quality of Experience (QoE), in particular on mobile devices. Moreover, it is not clear how users' context might affect QoE. We conducted an online survey with 349 cloud service users to gain insight into their usage and affordances. In a 2-week follow-up study, we monitored mobile cloud service usage on tablets and smartphones, in real-time using a mobilebased Experience Sampling Method (ESM) questionnaire. We collected 156 responses on in-situ context of use for Dropbox on mobile devices. We provide insights for future QoE-aware cloud services by highlighting the most important mobile contextual factors (e.g., connectivity, location, social, device), and how they affect users' experiences while using such services on their mobile devices.

show abstract

Section: Effect Of Network Contextmentioning

confidence: 61%

“…Lastly, a hiatus in Study 2 is that we only logged signal strength in a mobile context and not in a fixed (Wi-Fi) environment. Therefore, we were not able to link QoE to Wi-Fi signal strength, although we assume this to be related [18]. This might provide a better understanding of fluctuations in QoE ratings in a Wi-Fi context.…”

Section: Limitationsmentioning

confidence: 99%

Mobile cloud storage

Vandenbroucke

Ferreira

Gonçalves

et al. 2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

Self Cite

View full text Add to dashboard Cite

show abstract

“…Per contra, our QoE method places quality degradation in frame-by-frame timeline and directly in relation to the network (migration) dynamics. (2) No jitter. The previous case has been repeated and the uncontrolled behavior of the network has resulted in no jitter.…”

Section: A Scenario 1 Streaming In the Local Networkmentioning

confidence: 99%

“…This situation arises because QoS-based performance analysis does not capture the combined end-toend properties of networks and applications. According to [2], those services and applications which will take into account the user expectations will gain more success and will be adopted widely into the future. Hence, the Quality of Experience (QoE) assessment techniques [3], [4], [5] can be used to assess the network services performance, by focusing on the quality perceived by the user [6].…”

Section: Introductionmentioning

confidence: 99%

Network performance assessment with Quality of experience benchmarks

Mocanu

Santandrea

Cerroni

et al. 2014

10th International Conference on Network and Service Management (CNSM) and Workshop

View full text Add to dashboard Cite

Abstract-Today the performance of network services and devices is mainly assessed using Quality of Services (QoS) factors. These provide statistics about the quality of the network behavior but cannot accurately reflect how the unpredictable impairments which might occur in the network end up affecting the perception of the final beneficiary of these services, i.e. the user. This situation arises because QoS-based performance analysis does not capture the combined end-to-end properties of networks and applications. In this paper, we introduce a new network performance methodology based on Quality of Experience benchmarks, whereby we estimate the quality of the service as it is perceived by the user. We illustrate this approach in the context of video streaming services, showing how to evaluate quality degradation in Software Defined Networks. Our approach is better suited to the evaluation of dynamic networks and helps better pinpointing the critical factors that affect the applications the most.

show abstract

“…For example, in the IEEE802.11, the handover operation typically involves sweeping of the access points in all the channels available. The selection of the appropriate access point, and its association with this access point, may even involve access control operations as specified in the IEEE802.1x De Moor et al (2010).…”

Section: Handover In the Mipv4mentioning

confidence: 99%