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This study introduces fog architectures to verify location data in smart cities. These systems effectively detect fraudulent location claims, enhancing the trustworthiness of urban sensing data.

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Area of Science:

  • Computer Science
  • Urban Computing
  • Internet of Things

Background:

  • Smart cities require extensive urban sensing for self-monitoring and response.
  • Massive data generation necessitates effective event-filtering mechanisms for relevance and trust.
  • Mobile event producers increase the importance of location data for event context and producer trust.

Purpose of the Study:

  • To address the imperative need for location information validation in urban event-based systems (DEBS).
  • To propose novel communication schemes and architectures for Internet of Things (IoT)-based urban services.
  • To enhance the trustworthiness of data in large-scale urban sensing deployments.

Main Methods:

  • Design and implementation of three distinct fog architectures combining proximity and cloud communication.
  • Utilizing network simulations with realistic urban traces for performance evaluation.
  • Assessing the capability of the proposed architectures to identify false location claims.

Main Results:

  • The developed fog architectures demonstrated high accuracy in identifying false location claims.
  • Detection rates for fraudulent location data ranged from 73% to 100% across the evaluated scenarios.
  • The proposed solutions are effective in validating location information within urban event-based systems.

Conclusions:

  • Fog architectures offer a viable solution for location verification in IoT-based smart city services.
  • The integration of proximity and cloud communication enhances the reliability of urban sensing data.
  • These findings are crucial for building trustworthy and self-response urban systems.