QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

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Saovapakhiran B., Naruephiphat W., Charnsripinyo C., BAYDERE Ş., ÖZDEMİR S.

IEEE ACCESS, vol.10, pp.84579-84621, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 10
  • Publication Date: 2022
  • Doi Number: 10.1109/access.2022.3197585
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.84579-84621
  • Keywords: Quality of experience, Quality of service, Internet of Things, Optimization, Computer architecture, Resource management, Internet of Things, quality of service, quality of experience, IoT services, IoT applications, QoS for IoT services, QoS metrics, QoE metrics, IoT architecture, QUALITY, INTERNET, EXPERIENCE, ALLOCATION, NETWORKS, OPTIMIZATION
  • Hacettepe University Affiliated: Yes


Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: 1) QoE-driven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies; 2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture; and 3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed.