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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

Jun Wu1, Zhou Su2, Shen Wang3

  • 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. junwuhn@sjtu.edu.cn.

Sensors (Basel, Switzerland)
|August 1, 2017
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Summary

This study introduces social fog computing, leveraging "friend" relationships for service provisioning. A novel crowd sensing method enhances security service recommendations in these complex social networks.

Keywords:
fog computingservice discoveryservice recommendationsocial networks

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Area of Science:

  • Computer Science
  • Networking
  • Distributed Systems

Background:

  • Fog computing shifts resources to edge networks for low-latency communication.
  • Internet of Things (IoT) devices are increasingly forming social-like relationships.
  • Social networks offer potential for flexible, secure, and collaborative fog networking.

Purpose of the Study:

  • To design an architecture for social fog computing, provisioning services based on social connections.
  • To address security service recommendation challenges in social fog computing environments.
  • To propose a crowd sensing-enabled method for accurate security service provisioning.

Main Methods:

  • Designed a novel social fog computing architecture.
  • Developed a crowd sensing-enabling security service provisioning method.
  • Utilized simulations to evaluate the proposed method's feasibility and efficiency.

Main Results:

  • The proposed architecture enables fog service provisioning based on social relationships.
  • The crowd sensing method effectively recommends security services in social fog computing.
  • Simulation results demonstrate the feasibility and efficiency of the security recommendation approach.

Conclusions:

  • Social fog computing presents a promising paradigm for future networked systems.
  • Crowd sensing is an effective approach for enhancing security in social fog computing.
  • The developed method improves the accuracy and efficiency of security service recommendations.