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Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search.

Ruyan Wang1,2,3, Yuzhe Liu1,2,3, Puning Zhang1,2,3

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This study introduces an edge-cloud collaborative method for Internet of Things (IoT) data recommendations. It enhances real-time entity information acquisition and accuracy by optimizing IoT data and user behavior analysis.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things

Background:

  • Real-time acquisition of dynamic entity states is crucial for intelligent systems.
  • Existing Internet of Things (IoT) data recommendation methods often fail to account for the unique characteristics of IoT data and user search patterns, leading to suboptimal performance.
  • The time-varying nature of IoT entity states and diverse user search behaviors necessitate advanced recommendation strategies.

Purpose of the Study:

  • To propose an edge-cloud collaborative entity recommendation method that leverages the strengths of both edge and cloud computing.
  • To enhance the real-time performance and accuracy of entity state information retrieval in IoT environments.
  • To improve the overall quality and precision of recommendations by considering user search needs and entity characteristics.

Main Methods:

  • Designed a novel edge-cloud collaborative system architecture for entity recommendation.
  • Developed an edge-based entity identification method utilizing deep clustering to analyze entity features, improving real-time search accuracy.
  • Devised a cloud-based interest group division method using clustering to analyze user search needs, enhancing recommendation quality.

Main Results:

  • The proposed method significantly improves the real-time performance of entity recommendation compared to traditional approaches.
  • The accuracy of entity state information search is substantially enhanced through effective entity identification.
  • User interest group division effectively refines recommendations by considering potential user needs.

Conclusions:

  • The edge-cloud collaborative approach offers a superior solution for real-time IoT data recommendation.
  • Integrating edge computing for entity identification and cloud computing for user analysis optimizes recommendation systems.
  • This method effectively addresses the limitations of existing IoT recommendation techniques, providing more precise and timely information.