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From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications.

Irida Shallari1, Mattias O'Nils1

  • 1Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden.

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Summary

Intelligence partitioning optimizes energy consumption for Wireless Vision Sensor Nodes (WVSN). This approach improves smart camera performance and meets real-time processing demands, outperforming traditional methods.

Keywords:
IoTWVSNcloudenergy-efficiencyfogin-sensor processingintelligence partitioningsmart camera

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Embedded Systems

Background:

  • The Internet of Things (IoT) has seen rapid expansion, leading to increased data traffic from interconnected devices.
  • Smart cameras in IoT generate substantial data (megabytes per second), posing challenges for battery-powered devices with energy constraints.
  • Intensive image processing on large datasets within limited power budgets is a key challenge for smart camera nodes.

Purpose of the Study:

  • To analyze the impact of intelligence partitioning on the energy consumption of smart camera nodes.
  • To evaluate the real-time performance of Wireless Vision Sensor Nodes (WVSN) under different processing task distribution scenarios.
  • To determine the efficiency of intelligence partitioning compared to traditional design space exploration for WVSN.

Main Methods:

  • Investigated various intelligence partitioning scenarios for processing tasks distributed across smart camera nodes, fog computing, and cloud computing layers.
  • Analyzed the effects of these partitioning strategies on the energy consumption of the smart camera node.
  • Assessed the real-time performance and timing constraints of the Wireless Vision Sensor Node (WVSN).

Main Results:

  • Traditional design space exploration methods were found to be inefficient for WVSN.
  • Intelligence partitioning significantly enhances the energy consumption performance of smart camera nodes.
  • The proposed intelligence partitioning approach successfully meets the timing constraints for WVSN operations.

Conclusions:

  • Intelligence partitioning is a more effective strategy than traditional methods for optimizing WVSN.
  • Distributing processing tasks via intelligence partitioning improves energy efficiency in battery-operated smart cameras.
  • This method ensures that WVSN can meet demanding real-time performance requirements.