# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
###### Abstract  
A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings. vehicle network; data collection; optimization; SDVN 2023233600 20233600 20233600 20233600 20233 1000 20233 11 20233600 20233 1000 20233 11 20233600 20233 11 20233600 2023 11 20233600 2023 11 202336  
architecture of SDVNs, data is first collected by the nodes and then transmitted to the centralized controller [1]. In the preceding method, all nodes first broadcast their data, and once broadcast data are received by the nodes, collected data will be transmitted to the centralized controller by each node. In traditional VANETs or, in other words, in a distributed SDVN architecture, nodes usually collect data using broadcasting and there is no communication with a centralized controller [2]. However, the problem is that there is no proper mechanism for data collection in a hybrid SDVN architecture (architecture that can vary the control on the nodes between fully centralized control to fully distributed control) found in the literature to the best of our knowledge. So far, data collection in a hybrid architecture has also occurred in the manner specified for a centralized architecture. However, we note that the data collection approach for a centralized architecture is not the ideal way to collect data. In such an approach, as all nodes act as both data unicasting agents and data broadcasting nodes, we observe that redundant data is collected at the controller. Due to redundant data transmission, there is an extra communication cost and resource utilization, which will also lead to higher latency despite its very high reliability. Thus, we observe that in a hybrid architecture, it is possible to effectively control data plane communication and control plane communication such that all data will be collected at the controller with minimum resource utilization, cost, and latency. In this paper, we propose a novel data collection methodology for the hybrid architecture of SDVNs, where we use optimization techniques to effectively choose which nodes should broadcast and which nodes should unicast data subjected to the minimization of total communication cost, total communication delay, total communication overhead, etc. The data collection optimization framework is modeled as an integer quadratic programming problem where agents are selected to cover the whole vehicular network in order to transfer data from all nodes in the vehicular network to the controller. We identify two problems in the proposed optimization approach: node isolation and redundant data transmission if a new solution to the data collection optimization is not found when the vehicular network topology changes.