# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.2 AI-Assisted Secure IoT Extensions  
AI-Assisted Secure Internet of Vehicles  
As discussed in Section 3.2.2 (the main file), privacy in the Internet of Vehicles is important to a secure IoV network. In the IoV, the main concerns with privacy are user identity and location when using localization applications. Many of these systems either do not securely mask this information or use delay as a way to provide location privacy (this makes a real-time system impossible). To tackle these issues, the authors of [234] propose a data collection and preprocessing scheme that uses deep learning algorithms. The architecture of the network is based on edge computing. An adaptive transmission algorithm is used and adapts to the best method based on the quality of service evaluation. The scheme will use semisupervised learning for data augmentation and label guessing. It is able to identify data that are similar and process it faster reducing a time delay. Federated learning is used in the cloud server, which collects information from edge nodes so as to provide more privacy for users. Experiments show that the scheme is able to effectively reduce data uploaded by reducing redundant data, which, in turn, reduces the latency of the system. Another important aspect of security is the ability of a system to identify and remove misbehaving actors in the network. In [235], the authors propose a deep learning-based classification framework that works with edge computing to find vehicles that are sending communication that may be harmful to the network. The deep learning algorithm is a combination of stacked LSTM and CNN-LSTM. Together, they are able to find complex patterns in communication, allowing them to identify and remove misbehaving vehicles from the network. The deep learning algorithms are also able to classify the misbehaving actors into faults and attacks. Edge computing removes the strain of computation and communication from the vehicles to allow for a less latent and better-performing system. Testing of the scheme shows that it is a better option for fault and attack detection than other similar methods. Ad hoc networks in IoV are dynamic network topologies that have led to much of the advancement in IoV. High mobility, heterogeneity, and unstable connectivity make these networks unreliable in some settings. The authors of [236] propose the use of an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. Like the previously proposed scheme, this scheme uses a stacked LSTM to identify malicious actors in the network. A testing of the network shows the lack of strain on the vehicles, and the deep learning algorithm shows a high level of accuracy in identifying malicious actors. It is paramount to the safety of a system that there is correctness of data exchange. Most misbehavior systems, due to rapid dynamics, do not securely defend a vehicle. The authors of [237] propose a data-centric supervised machine learning approach to misbehavior detection with integrated plausibility checks. The system uses two plausibility checks. The first check is location, which checks the GPS against the predicted range of the vehicle. The second one is a movement plausibility check, which checks the velocity of the vehicle calculated from GPS against the velocity the vehicle sends back. Through experimental testing of a variety of supervised machine learning methods, the authors determined that, with the help of plausibility checks, ensemble algorithms outperform the rest in terms of error rates and are implemented into the system. The proposed algorithm is proven to be the most effective in terms of supervised machine learning when tested against the Vehicular Reference Misbehavior (VeReMi) dataset. Tests with and without plausibility checks show that the checks increase the accuracy and performance of the misbehavior detection system. Large amounts of data are transmitted over IoV networks. When the information can be analyzed together, the quality of service and experience can be improved.