# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Clustering with Unsupervised Learning
[144] consider a wireless authentication system that uses UAVs as relay nodes. They discuss the problem of how to detect eavesdropping attacks targeting UAVs. They build a detection model combing one-class support vector machines (OC-SVM) with k-means clustering. Numerical results are presented to show the proposed model in terms of stability. Targeting for mobile communication systems for high-speed trains and vehicles, [PERSON] et al. [145] propose an improved k-means algorithm for estimate frequency offset from the in-phase and quadrature constellation of the received signal. They divide the received signal symbols into multiple groups and find the centroid of each group using the modified k-means algorithm. The algorithm is able detect the constellation rotation within a range between \(-45\) degrees and \(45\) degrees for QPSK signals with additive white Gaussian noise. [PERSON] et al. [146] consider a drone-aided emergency communication network in post-disaster scenarios, and employ a modified k-means algorithm to solve the drone deploy ment problem with the aim of minimizing power consumption of drones while satisfying user requirements regarding the transmission speed. The k-means algorithm is used to find the optimal number, altitude, and transmit power of drones. [PERSON] et al. [147] discuss the effect of vehicle density on the communications in VANETs, and propose using a clustering approach to reduce the packet collisions in VANETs. They use an improved k-means algorithm to group vehicles into different clusters by considering the similarities in movements, vehicle location, and inter-vehicle distance. Based on the vehicle clusters, a message distribution mechanism is designed. Computer simulations are used to show that the mechanism can achieve a higher packet delivery ratio and lower latency compared with existing baselines. Different vehicular applications possibly exhibit different levels of service requirements. Network slicing is a promising technology for fulfilling the service requirements involving a large amount of diversity. [PERSON] et al. [148] propose a k-means++ algorithm to group the services into different clusters based on the service-level agreement--including the capacity, coverage area, and QoS requirements--between the service provider and customer. Based on the clustering results, different network slices are provided to different groups of services. [PERSON] et al. [149] consider a communication network where UAVs act as base stations, providing wireless communications services to other terminals. They discuss the joint UAV placement and frequency division problem in multi-UAV environments. A k-means algorithm is used to derive the best horizontal arrangement of UAVs. The frequency division is conducted after the clusters are generated. [PERSON] et al. [150] study the smartphone placement problem for smartphone-based driver monitoring. They use a kernel-based k-means clustering approach to infer the placement of smartphones within vehicles (the position of the smartphone with respect to the vehicle). They argue that knowing the placement of the smartphone could provide benefits in many scenarios, such as accident reconstructions and improving driving behaviors. [PERSON] et al. [151] study the modeling of the driving cycle of a city tour bus. The driving cycle construction is useful for evaluating vehicle performance and measuring fuel consumption. As the conventional k-means algorithm is sensitive to its initialization parameters, an improved version of k-means--namely, GA-K-means--integrates a genetic algorithm. The GA-K-means algorithm groups micro-trips, specifically, an excursion between two successive time points at which the vehicle is stopped. They further combine GA-K-means with a hidden Markov model to construct the driving cycle of city tour buses in Beijing. [PERSON] et al. [152] study how to identify multiple characteristic driving cycles from vehicle data representing real driving scenarios.