# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 5 Discussion  
For the hybrid SDVN architecture, we proposed a novel data collection method by modeling it as an integer quadratic programming problem. The decision for optimization is taken by inspecting the knowledge generated as normalized heterogeneous link-state entropy calculated using the collected neighbor set IDs and neighbor sizes. According to our best knowledge, we are the first to optimize data collection for the hybrid SDVN architecture, as it has not been previously investigated by any researcher. In order for future researchers to investigate, a performance evaluation for data collection was comparatively analyzed for the proposed data collection in the hybrid SDVN architecture, distributed SDVN architecture, and centralized SDVN architecture. Vehicular communication supports a maximum latency of 500 ms for the transport of V2X messages between a piece of User Equipment (UE) supporting the V2X application and an RSU via another UE supporting the V2X application. Further, the BSM broadcast and similar uses of the cooperative awareness message for V2V safety generally allow a nominal 100 ms latency for human-assisted driving [59]. The 3 GPP system should support a maximum latency of 3 ms, 10 ms, and 50 ms latency for message transfer among a group of UE pieces for fully autonomous driving within 200 m, 500 m, and 1000 m, respectively [48]. The requirement for low latency for vehicular network applications such as autonomous driving is highlighted in recent work [60]. It was evident from all research experiments in Section 4.3 that the proposed method's latency per node per data-gathering cycle is confidently below 45 ms under any given scenario. Thus, the proposed data collection method's latency lies below the maximum latency allowed (50 ms) by the 3 GPP system for message transfer among a group of user equipment within 1000 m, and also it lies well below the 100 ms maximum latency allowed for status data collection (cooperative awareness message broadcasts such as BSM). The reliability of vehicular communication is standardized based on the application. An ultra-reliability of 99.9% is required for the exchange of safety-related driving maneuvers [58]. For video data sharing applications, a minimum reliability of 90% is required, whereas, for human assistance systems, autonomous driving, and for machine-centric data analysis, a minimum reliability of 99.9% is required [48]. According to the results, a minimum nominal optimization frequency of 4 Hz and a maximum entropy threshold of 0.005 satisfy the minimum average packet delivery ratio (99.9% ultra-reliability benchmark) required for high-end vehicular network communication applications such as human assistance systems, autonomous driving, machine-centric data analysis, etc., as specified in the 3 GPP standard for V2X applications. In the proposed data collection mechanism, the agent nodes collect and then send the collected data along with their own data, where packets can drop even when agent nodes collect data. However, the fact that the proposed method is ultra-reliable even under scenarios where packets can drop due to node isolation is a remarkable achievement. On the other hand, for the centralized architecture, the reliability is always almost 100% for any data collection frequency or any mobility scenario, or any network size. This high reliability occurs because of redundant data transmission by multiple nodes in a given neighborhood of a node. On the other hand, we observed that the PDR of distributed architecture is much poorer than both other architectures due to the presence of isolated nodes in the VANET. However, improved PDR for the distributed architecture was observed for rural and autobahn mobility scenarios and for large vehicular networks. The explanation for that increment is due to the higher coverage in rural and autobahn mobility scenarios and due to the increment of normalized network link entropy in large networks.