Abstract
For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.
Highlights
As the infrastructure of mobile vehicle networks, the Internet of Vehicles (IoV) combines the advantages of the Internet of Things (IoT) and an Intelligent Transportation System (ITS)
At the bottom Layer for the Service (LS), we receive all the feedback information from end users, and extract the key attributes of service requests; at the second Layer for the End User (LEU), we evaluate the fairness index in multiple forms according to the attributes of service requests; at the third Layer for the Network Coding Set (LNCS), we calculate the utility of a network coding set based on the utility of member end users; and at the top Layer for the Control Entity (LCE), we maximize the utility of the control entity, and optimize the coding cache queue to make the optimal scheduling decision
round robin pairing and scheduling algorithm (RRPS) selects a pair of users carrying out transmission simultaneously in uplink virtual multiple input multiple output (DRSO selects the member end users of a network coding set with similar attributes for credits and fairness index)
Summary
As the infrastructure of mobile vehicle networks, the Internet of Vehicles (IoV) combines the advantages of the Internet of Things (IoT) and an Intelligent Transportation System (ITS). The IoV is a multi-source, multi-destination, and multi-user wireless network system with the characteristics of unstable network topology, fast node mobility, and frequent data exchange. End users (vehicles) share the wireless channel. Due to the instability of the wireless channels, the IoV may encounter channel error during transmission, and the network throughput is constrained. For error control and throughput improvement, network coding can be introduced due to its advantage in data fusion [1]. Intermediate nodes conduct linear network coding on multiple original data packets to generate the coded data packets. The destination recovers the original data packets according to the coding matrix
Published Version (Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have