Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks

Area of Science:

• Computer Science
• Network Security
• Wireless Communication

Background:

• Wireless systems in the Internet of Things (IoT) are vulnerable to faults and attacks, particularly identity deception in multi-hop routing.
• Harsh network conditions and attacks on routing protocols exacerbate the challenges in ensuring IoT system trustworthiness and faultlessness.

Purpose of the Study:

• To propose a novel trust-aware routing mechanism (TARM) for enhancing security and reliability in IoT networks.
• To develop a trust evaluation method for segregating faulty and anomalous nodes from normal ones.
• To optimize routing paths for secure data collection from trustworthy nodes.

Main Methods:

• A trust-aware routing mechanism (TARM) utilizing a mobile edge node for data collection from faultless nodes.
• Modified Gray Wolf Optimization (GWO) for cluster formation among sensor nodes and subsequent trust value calculation.
• Artificial Bee Colony optimization algorithm for establishing optimal routing paths to the mobile edge node.

Main Results:

• The proposed TARM demonstrated a 58% increase in trustworthiness compared to existing methods.
• Achieved a 96.7% detection rate for untrustworthy nodes.
• Reached the highest accuracy of 91.96% among similar contemporary schemes.

Conclusions:

• TARM effectively enhances IoT network security and trustworthiness through a robust trust evaluation and optimized routing strategy.
• The proposed mechanism overcomes weaknesses of previous techniques, offering a low-cost and less complex solution.
• TARM significantly improves the reliability and security of data transmission in challenging IoT environments.