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Fog-assisted de-duplicated data exchange in distributed edge computing networks.

Ghawar Said1,2, Anwar Ghani3,4, Ata Ullah5

  • 1Department of Computer Science, International Islamic University, Islamabad, 44000, Pakistan.

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The new Controlled Cut-point Identification Algorithm (CCIA) improves data deduplication for Internet of Things (IoT) sensors. CCIA optimizes resource use by enhancing data transmission and storage efficiency in IoT systems.

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Internet of Things (IoT) systems generate vast amounts of sensor data, posing challenges for resource-scarce devices.
  • Inefficient data handling in IoT leads to increased communication and storage costs due to redundant transmissions from overlapping sensor areas.
  • Existing data deduplication schemes like Asymmetric Extremum (AE) and Rapid Asymmetric Maximum (RAM) struggle with optimal window size selection, leading to poor deduplication rates.

Purpose of the Study:

  • To address the limitations of existing data deduplication techniques in resource-constrained IoT environments.
  • To propose a novel algorithm, the Controlled Cut-point Identification Algorithm (CCIA), for efficient data chunking and deduplication.
  • To enhance data collection, transmission, and storage efficiency in IoT-enabled healthcare services and other applications.

Main Methods:

  • Developed the Controlled Cut-point Identification Algorithm (CCIA) with a restricted variable-sized window to a defined threshold.
  • Ensured the index value for threshold determination is greater than half the fixed window size to maximize duplicate detection.
  • Implemented an upper limit offset to prevent excessively large windows and mitigate computational overhead.
  • Conducted extensive simulations using Windows Communication Foundation services deployed on the Azure cloud platform.

Main Results:

  • CCIA demonstrated superior performance compared to AE and RAM across multiple metrics, including chunk number, average chunk size, and minimum chunk size.
  • CCIA achieved significant improvements in total number of chunks (6.81% and 14.17% better than competitors) and average number of chunks (4.39% and 18.45% better).
  • The algorithm showed substantial gains in minimum chunk size (153% and 190% improvement), indicating more effective data compression and deduplication.

Conclusions:

  • The Controlled Cut-point Identification Algorithm (CCIA) effectively optimizes data transmission and storage in IoT systems.
  • CCIA offers improved resource utilization and reduced operational costs for IoT applications, particularly in data-intensive fields like healthcare.
  • The proposed algorithm represents a significant advancement in efficient data management for resource-constrained IoT environments.