A two-phase spatiotemporal chaos-based protocol for data integrity in IoT

Area of Science:

• Computer Science
• Cybersecurity
• Network Engineering

Background:

• Data integrity is a critical challenge in expanding Internet of Things (IoT) applications across industries.
• Existing data integrity solutions for IoT often face limitations in efficiency and cost-effectiveness.

Purpose of the Study:

• To propose a novel, secure, and efficient data integrity methodology for Internet of Things applications.
• To address the limitations of conventional data integrity protocols in IoT environments.

Main Methods:

• The proposed protocol involves two stages: "sequence sharing" and "data exchange."
• The "sequence sharing" stage utilizes a chaotic model for secure identity exchange and common sequence generation between nodes.
• The "data exchange" stage focuses on packet validation, leveraging authentication and timing calculations from the first stage.

Main Results:

• The new methodology demonstrates effectiveness in ensuring data integrity across various testing scenarios.
• Compared to conventional IoT data integrity protocols, the proposed method is more efficient and cost-effective.
• The approach significantly reduces energy consumption and packet overhead, eliminating the need for third-party auditors.

Conclusions:

• The proposed data integrity methodology is a robust and practical solution for Internet of Things applications.
• The method offers enhanced security against diverse threats.
• It presents a viable, cost-effective, and energy-efficient alternative for ensuring data integrity in IoT.