# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground
#### 3.2.2 Extensions with Applications in Transportation
The cloud server collects information from the RSU to create a transaction. These transactions form blocks, which are then voted on using the practical Byzantine fault tolerance (PBFT) consensus algorithm [151] to apply the block into the blockchain. The scheme was tested through formal analysis and informal analysis, each of which proved that this scheme provides better security, lower communication costs, and offers more functionality than other similar models. The introduction of 5G has allowed for many advancements in real-time communication offered in IoV infrastructure. It is important to authenticate all of the information coming in to the system to allow for this kind of infrastructure to be usable. Many of the industry standard schemes have large amounts of delays,lack of privacy, and reduced efficiency in communication. To handle this the authors of [152] propose the use of ECC along with Ant Colony Optimization On-demand Distance Vector protocol for the purpose of routing. The scheme takes advantage of three different components. The first one is the use of Certificate Authorities (CA) for key generation. The vehicle's unique number plate is used to create private-public key pairs when ECC is applied. The second component is the ability of the system to detect malicious vehicles. To do this, the system sends periodic messages and checks for an appropriate response. If the system receives multiple inappropriate responses, the vehicle is marked as malicious. The last component is the Ant Colony Optimization On-demand Distance Vector routing. The On-demand Distance Vector routing portion of the method is reactive and uses route discovery and route maintenance to find the best route. This is improved with Ant Colony Optimization which allows for a source vehicle to find the best route to a destination vehicle through multiple different vehicles. Through simulation testing, the scheme has shown higher throughput, lower delays, and lower routing overhead than other similar state-of-the-art methods. In many cities, there is a missing component of privacy of user-data in toll transponders. In [153] the authors propose a privacy risk reduction model to improve the current toll transponder infrastructure in the city. The scheme uses a fully homomorphic encryption protocol. The scheme would be a post-quantum encryption method that works with a blockchain model. The privacy of this scheme is evaluated and found passable when compared to the European General Data Protection Regulation and the California Consumer Privacy Act requirements. * Misbehavior Detection * Misbehavior detection is similar to fraud detection in that it is used in finding harmful or potentially harmful actors in a network. Misbehavior detection monitors behavior in a network and flags traffic that is abnormal. Deep learning has also gained a lot of attention for misbehavior detection because of its non-linear mapping ability. The issue with this is that deep learning takes time to train and is very hard to scale. The authors of [154] propose a form of misbehavior detection that uses a broad learning system (BLS). This method performs a similar task to deep learning but consumes fewer resources and is in real time. Key features are found in raw data and used to establish the BLS. The system is updated with newly generated data using incremental learning. Through experiments, this scheme performs better than other deep learning algorithms in terms of time and computation costs while remaining accurate. Unlike deep learning, the proposed scheme is also scalable for use in the IoV. Many IoVs are vulnerable to cyber-attacks. This makes data integrity an important part of any IoV system. Many of the conventional ways of checking data integrity will not work in IoVs in terms of overhead and computation cost. Since many methods do not work in the IoV, there is a lack of protection, and an RSU can be hijacked. In [155], the authors propose a lightweight method to check data integrity and find malicious RSUs.