# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.1 AI-Assisted IoT Extensions  
AI-Assisted Internet of Vehicles  
The IoV is a growing area where many different advancements and devices are being connected to the networks. UAVs are something that has a wide capability of monitoring traffic patterns and parking capacity in parking lots. The issue is that the privacy of communication of these two devices is vulnerable to attack. The authors of [211] propose a method based on Federated Learning (FL) to allow for privacy-preserving collaborative machine learning across networks allowing for UAVs to be used in IoV. The scheme uses a multi-dimensional contract that creates an environment for truthful reporting of information. The Gale-Shapley algorithm is used to identify the UAV that provides the most information at the lowest cost in a sub-region. Simulation verifies the efficiency of the matching used in the scheme while providing an optimal amount of security. Resource allocation in intelligent transportation systems (ITS) is hard to manage due to the many different vehicular applications and time-varying network statuses. The authors of [212] believe that artificial intelligence algorithms are able to solve those challenges. The system they propose is an intent-based traffic control system. The system uses 5G-driven deep reinforcement learning algorithms to improve edge computing and content caching. The system uses base stations and RSUs to execute tasks and deliver information to take the computation strain off the vehicles. To keep a high performing network of mobile operators and a high level of quality of experience a function optimizes both through a resource allocation algorithm. A mobile edge computing (MEC) protocol is used in the servers to create a mobility-aware and cache-efficient scheme. The MEC uses a Deep Deterministic Policy Gradient model to optimize task assignment and resource allocation. The scheme was tested experimentally using real traffic data and proven to be effective as an ITS. Cooperative positioning (CP) allows for better lane alignment of vehicles, which improved the GPS, but in most schemes, they lack security and robustness. The authors of [213] propose a blockchain-enabled IoV that uses CP to improve GPS accuracy. The combination of the IoV and blockchain improves congestion in the network and improves security. The scheme uses a deep learning algorithm to predict the distance between the intelligent vehicle and a traffic sign. To ensure security, a smart contract system is implemented. Through simulation testing, the scheme was proven to be effective and accurate in positioning and secure when compared to other similar methods. License plate numbers are used as unique identifiers on the roads. With so many new vehicles being added to the roads, it is important to have a way to securely use license plate recognition (LPR) to make the road ways safer. To increase the safety and usability of such systems, there needs to be sufficient privacy and resource allocation. In [214] the authors propose a federated learning-based LPR. The scheme will use the computing resources of a mobile device to lower latency and consumption. The scheme is designed to be deployable on mobile devices while maintaining accuracy. A correctional algorithm is designed to adjust for the angle in which the device reads license plates in order to maintain accuracy. Privacy and effectiveness of the model are validated through experimental testing. Due to high mobility environments and multihop ad hoc networks, IoV suffers from weak links for communication. Many of the current proposed schemes in the literature fail to provide effective routing that can predict connectivity between two vehicles. The authors of [215] propose a machine learning and analytical compatibility-based ad hoc routing protocol. The scheme is able to estimate the compatibility time of all of the routes and choose the best one that allows for a longer connectivity time. This is to ensure the messages do not get interrupted as often.