A Game-Theoretic Approach for Enhancing Security and Data Trustworthiness in IoT Applications

TL;DR

This paper introduces a game-theoretic model using TDMA to improve security and data trustworthiness in IoT wireless sensor networks, effectively detecting malicious attacks and hardware failures while conserving power.

Contribution

It presents a novel repeated game model that enhances IoT security, detects hardware failures, and reduces power consumption, outperforming existing non-cooperative defenses.

Findings

Improved data trustworthiness against selective forwarding attacks.

Effective detection of hardware failures in sensor nodes.

Enhanced power conservation during malicious or faulty conditions.

Abstract

Wireless sensor networks (WSNs)-based internet of things (IoT) are among the fast booming technologies that drastically contribute to different systems management and resilience data accessibility. Designing a robust IoT network imposes some challenges such as data trustworthiness (DT) and power management. This paper presents a repeated game model to enhance clustered WSNs-based IoT security and DT against the selective forwarding (SF) attack. Besides, the model is capable of detecting the hardware (HW) failure of the cluster members (CMs) and conserve the power consumption due to packet retransmission. The model relies on TDMA protocol to facilitate the detection process and to avoid collision between the delivered packets at the cluster head (CH). The proposed model aims to keep packets transmitting, isotropic or non-isotropic transmission, from the CMs to the CH for maximizing the DT and aims to distinguish between the malicious CM and the one suffering from HW failure. Accordingly, it can manage the consequently lost power due to the malicious attack effect or HW malfunction. Simulation results indicate the proposed mechanism improved performance with TDMA over six different environments against the SF attack that achieves the Pareto optimal DT as compared to a non-cooperative defense mechanism.