In-network generalized trustworthy data collection for event detection in cyber-physical systems

Area of Science:

• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• Sensor Networks

Background:

• Data collection in CPS is often unreliable due to resource constraints, environmental factors, and security issues.
• Event detection in CPS is challenging without validated sensor data during acquisition, transmission, and aggregation.
• Existing methods struggle to ensure data integrity and identify faulty sensors effectively.

Purpose of the Study:

• To introduce a novel framework, In-network Generalized Trustworthy Data Collection (IGTDC), for reliable event detection in CPS.
• To enhance data aggregation at the edge of CPS by ensuring data trustworthiness.
• To identify and mitigate the impact of faulty sensors in CPS data streams.

Main Methods:

• Developed the In-network Generalized Trustworthy Data Collection (IGTDC) framework.
• Implemented local data validation at the sensor module before transmission.
• Utilized gate-level modeling with Verilog User Defined Primitive (UDP) and Programmable Logic Device (PLD) for data validation.
• Employed Gray code in gate-level modeling for enhanced data reliability and faulty sensor identification.

Main Results:

• Demonstrated reliable data collection through simulations and performance analysis.
• The IGTDC framework successfully validates data trustworthiness at multiple stages.
• Faulty sensors were effectively identified, improving overall system reliability.

Conclusions:

• The IGTDC framework significantly improves the reliability of data collection in CPS.
• The proposed methods ensure trustworthy data for event detection and aggregation.
• IGTDC is suitable for a wide range of Cyber-Physical Systems applications requiring dependable data.