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While the modeling of QoE has made significant advances over the last couple of years, currently existing models still lack an integration of user behavior aspects and user context factors along with the consideration of appropriate temporal scales. Therefore, the goal of this paper is to present a comprehensive QoE and user behavior model providing a framework which allows joining a multitude of existing modeling approaches under the perspectives of service provider benefit, user well-being and technical system performance. In addition, we discuss the role of a broad range of corresponding influence factors, with a specific emphasis on user and context issues, and illustrate our proposal through a series of related use cases.

Factories become increasingly dependent on network connectivity. The next generation of mobile communications, 5G, will enable better flexibility and service quality through network slicing. Network slicing is a means of creating logically separated use case specific virtual networks over the same physical network. However, there is a lack of techno-economic research related to management of network slices. Network slice management needs to take into account the multiple network domains, business actors and value networks involved in a vertical such as smart factories. The key for network slices to succeed where other resource reservation and quality of service technologies have previously failed is with well-defined and feasible management models and strategies. In this paper, we focus on network slice management and strategies for a smart factory. We study a state-of-the-art electronics assembly factory in Finland to find existing need for network slicing and missing capabilities to support smart factory use cases. Next, we define use case specific network slices and develop a network slice management model based on 5G specifications. The model allows for distribution of network functions between business actors over multiple network domains. The value network analysis method is utilized to develop alternative configurations that constitute the network slicing strategies facilitated by the model. Factory managers can decide on the most suitable strategy based on traditional factors such as make or buy, security, and level of automation. The strategies also differ in their technical applicability to different use cases. A feasibility study reveals the strategic differences from factory, local network operator and large mobile network operator perspectives.

Mobile quality of experience and user satisfaction are growing research topics. However, the relationship between a users satisfaction with network quality and the networks real performance in the field remains unexplored.This paper is the first to study both network and non-network predictors of user satisfaction in the wild. We report findings from a large sample (2,224 users over 12 months) combining both questionnaires and network measurements. We found that minimum download goodput and device type predict satisfaction with network availability. Whereas for network speed, only download factors predicted satisfaction. We observe that users integrate over many measurements and exhibit a known peak-end effect in their ratings. These results can inform modeling efforts in quality of experience and user satisfaction.

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