Optimizing Resource and Service Allocations for IoT-Assisted Intelligent Transportation Systems
Abstract
Intelligent Transportation Systems provide ubiquitous communication for the driving users through heterogeneous interconnections. The heterogeneous interconnections are required for uninterrupted resource sharing. Spontaneous resource availability due to vehicle speed and infrastructure connectivity disturb prompt service utilization. In this manuscript, a Permissible Service Selection and Allocation (PSSA) method is proposed to address spontaneous issues in vehicular communication and connection. This method considers vehicle displacement and minimum interconnection factors in accessing a cloud service. Both factors and their balancing impact are analyzed throughout the vehicle’s service requesting interval. In this process, random forest learning is induced to identify the balancing factors’ adjustments. The service access is probed through the active infrastructure based on the balancing factor. The ordering process of the learning intervals provides ease of service selection and allocation. In this process, reallocation is not preferred due to the random displacement of the vehicles. Therefore, the interval dropouts are reduced in both handoff and non-handoff communication scenarios. Further metrics such as service ratio, delay, and connectivity are used in validating the proposed method’s performance.