# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Ensemble Learning for IoV  
[PERSON] and [PERSON] [123] propose a fault detection approach for vehicular cyber-physical systems based on an ensemble learning scheme. An accurate fault diagnosis requires the support of a large amount of data. However, these data are from different types of sensor devices and show different values in different weather and road conditions. Therefore, the fault detection should consider the variety of sensor data and environments. To conduct a more accurate diagnosis in various conditions, [PERSON] and [PERSON] use an ensemble learning scheme to improve the adaptability of the fault diagnosis. The procedure of the scheme is as follows. First, different types of sensor information are collected through multiple sensors. Second, the data are processed by multiple models, where each model can recognize a special environmental condition. While each model reflects a limited feature of the whole system, by using the ensemble learning strategy to combine the results from multiple models, the scheme is able to enable more efficient data-driven fault diagnosis in complex vehicular environments. [PERSON] et al. [124] discuss the use of ensemble learning in finding the best modulation and coding scheme (MCS) for communications between underwater vehicles and underwater base stations. They employ the boosting technique, a type of ensemble learning, to analyze a measured sea trial dataset that includes channel parameters under different conditions. They further combine the regression tree technique with a boosting technique to find the best mapping between the MCS level and channel parameters. They claim that their approach can achieve 99.97% accuracy in classifying MCS levels. [PERSON] et al. [125] use random forests (RF), a well-used ensemble learning method, to better satisfy different service requirements, including extra-low latency and large throughput, in different vehicular scenarios. They consider a heterogeneous network scenario that includes multiples types of networks, including cellular communications through a base station, cellular V2V, and IEEE 802.11p. The objective of the ensemble learning method is to find the best available network according to the current vehicular environment and application requirements. The RF algorithm constructs multiple trees, where each tree is generated by parts of parameters (features) of the data. By using the RF algorithm, [PERSON]. [125] is able to build an efficient system by making a decision based on the knowledge from multiple trees, which is not possible by a single simple algorithm. [PERSON] et al. [126] use an ensemble learning approach in real-time driver behavior recognition tasks. They employ a deep CNN with multiple inputs, namely, side video streams, side optical flows, front video streams, and front optical flows, where each input is a weak classifier but has its own advantages in extracting some special features. By merging four different classifiers with the ensemble learning approach, [PERSON]. [126] can improve the classification accuracy compared with existing baselines with low computational complexity. Making a good prediction about the future network status and user traffic patterns is essential when it comes to providing successful network resource allocation in complex environments. In Ref. [127], [PERSON] et al. discuss the use of ensemble learning in forecasting network performance metrics for fifth-generation (5G) networks. They propose a real-time distributed framework that uses multiple algorithms to make better forecasting. They show that an ensemble of multiple good algorithms can achieve a better prediction for different performance metrics. A vehicular network dataset in Porto, Portugal, is used to evaluate the performance of the framework. [PERSON] et al. [128] use an ensemble learning approach to enable an efficient intrusion detection system in vehicular ad hoc networks (VANETs).