Secured dynamic path planning and multi-obstacle avoidance model for UAV networks

As the number of the obstacles are increasing along with the navigation paths, the security of the communication data also increasing exponentially in the dynamic UAV networks. A large number of path planning and obstacle avoidance models have been proposed on the static and dynamic UAV networks for data communication. Most of the traditional obstacle avoidance models are independent of security for communication data or path planning procedures. Since the UAV sensitive data are stored in each sensor along with the path planning information; there exists an unauthorized access or malicious access to the sensitive data from third party applications or users. Also, traditional standard cryptographic algorithms (both symmetric and asymmetric) are not efficient to provide complete security to vast amount of UAV data dynamically due to its high computational memory and time.In this paper, a novel trust based Ant Colony optimization model was proposed to secure the sensitive UAV data against unauthorized access. In this model, a novel cipher text policy based encryption and decryption procedure is used as an extension to KP-ABE for UAV path security. Experimental results proved that the proposed trust based optimization model is better than the traditional UAV security models in terms of computational time and memory.