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Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware.

Alexandros Gazis1, Eleftheria Katsiri1,2

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This study presents a novel, low-power middleware for analyzing wireless sensor network (WSN) data in indoor locations. It efficiently tracks visitors and monitors occupancy using affordable devices, proving effective for historical sites and museums.

Keywords:
Internet of Things middlewareMapReduce middlewareRaspberry Pidistributed fault-tolerant middlewaredistributed sensing middlewareindoor tracking middlewaremiddlewarevisitor monitoring middlewarewireless sensor network middleware

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Network Engineering

Background:

  • Indoor environments like museums and historical buildings require efficient monitoring systems for visitor tracking and safety.
  • Existing solutions often rely on resource-intensive hardware or complex AI techniques, limiting their applicability in budget-constrained or low-power scenarios.

Purpose of the Study:

  • To introduce a novel, cost-effective middleware for analyzing wireless sensor network (WSN) data using low-power computing devices.
  • To demonstrate the middleware's capability in visitor tracking, occupancy monitoring, and as an evacuation aid in indoor settings.
  • To highlight the system's fault tolerance and distributed nature for reliable data analysis.

Main Methods:

  • Utilized low-power computing devices (e.g., Raspberry Pi) for data analysis.
  • Implemented a basic MapReduce algorithm for data gathering and distribution across nodes.
  • Employed RFID sensors for visitor badge data collection and mini-computers for local storage.
  • Integrated a leader election algorithm for fault tolerance in the distributed system.

Main Results:

  • The middleware successfully gathered and analyzed WSN data, demonstrating visitor tracking and occupancy monitoring capabilities.
  • Performance metrics validated the system's viability, matching resource-intensive methods despite using simpler hardware.
  • The fault-tolerant, distributed setup using budget-friendly devices proved effective in a real-world historical building.
  • The system showed promise for swift prototyping and accurate validation of findings in indoor monitoring applications.

Conclusions:

  • The developed middleware offers an innovative, fault-tolerant, and distributed solution for indoor monitoring using low-power devices.
  • It provides a cost-effective alternative to resource-heavy systems for applications like visitor tracking and occupancy management.
  • The system is particularly relevant for historical buildings, museums, and post-pandemic environments requiring efficient monitoring and crowd management.