Content sharing in Internet of Vehicles: Two matching-based user-association approaches

The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks (VANETs) into the Internet of Vehicles (IoV), where a variety of applications, such as road safety, traffic efficiency, driver assistance and so on, have been envisioned. Benefiting from the 3GPP study case on the LTE assisted vehicular communications, the information dissemination within IoV can become more reliable and efficient, thus enabling some safety critical applications. This paper discusses the user association methods to optimize the information dissemination in IoV. With joint consideration of vehicles' quality of service (QoS) requirement (i.e., the communication link quality) and the information gained through communication (i.e. the data value), the user association problem is formulated as a mix integer linear programming (MILP) problem. In pursuit for distributive solutions, two matching-based user association methods are proposed. The first method is clustering based, modeled as the hospital resident (HR) matching game. In particular, a stable matching between the cluster heads and ordinary nodes can be achieved with the proposed resident-oriented Gale-Shapley (RGS) algorithm. The second user association method provides an independent and equal relationship between vehicles, different from the ordinated/coordinated relation in the clustering method. The stable fixture (SF) model is adopted to model such relations; the so-called Irving's stable fixture (ISF) algorithm is utilized to find a stable matching within the vehicles if one exists. The performance of the two proposed approaches is evaluated under different traffic scenarios w.r.t. network connectivity, network partition and overall data exchanged, by comparing the proposed approaches with some heuristic algorithms. The simulation results point out that both matching-based approaches are capable of achieving high performance, highlighting also good stability, flexibility, scalability and affordable complexity.