Swarmtrust: A swarm optimization-based approach to enhance trustworthiness in smart homes

Abstract

As smart home networks become increasingly complex and dynamic, maintaining trust between the various devices and services is crucial. Existing trust management approaches face challenges such as high computational overhead and difficulty in selecting optimal parameters. To address these challenges, we propose a new method for trust management in smart home networks based on swarm optimization, a principle of swarm intelligence. Our proposed approach is based on swarm optimization, which is a principle of swarm intelligence that optimizes communication patterns and manages trust values within the network. In SwarmTrust, the nodes in the smart home network are represented by particles, and the positions and velocities of these particles are adjusted to optimize communication patterns and reduce the computational burden on nodes. This optimization is achieved by evaluating the trust values of each node and adjusting the communication patterns accordingly. The proposed method integrates trust management with several distinct parameters and selects the parameters optimally to maintain efficiency. The proposed approach also addresses optimization challenges by monitoring communication patterns and managing trust values in a distributed manner, which reduces the computational burden on nodes and maintains efficiency. To evaluate the effectiveness of our proposed approach, we conducted simulation experiments and compared the performance of our approach with existing methods. Our results showed notable improvements in terms of computational efficiency and optimized computation.