# 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
To combat these many challenges, the authors of [117] propose a Privacy-Preserving-based Secured Framework for the Internet of Vehicles (P2 SF-IoV). The proposed method first uses blockchain technology to securely transmit the data. The blockchain then implements a deep learning algorithm known as the SLSTM technique. This is used to catch other types of attacks, such as malicious nodes or intrusion. The performance of the deep learning algorithm is then evaluated using IoT-Botnet and ToN-IoT datasets to help improve accuracy. The authors compare this method to blockchain and non-blockchain methods and found that it generally outperformed them in terms of detection rates, accuracy, and false alarm rates. Vehicle-to-everything (V2X) communication is the ability of a vehicle to communicate with an entire network, which is composed of infrastructure, other vehicles, and pedestrians. An area that is important to maintain the security of such a network is authentication. In [118], the authors propose a scheme that takes advantage of blockchain to maintain privacy while lowering communication and storage costs. The authors use a form of blockchain known as CyberChain. This is coupled with a Privacy-Preserving Parallel Pedersen Commitment (P4C) to allow for stronger privacy. To further accelerate the authentication process, the authors use Diffused Practical Byzantine Fault Tolerance. These two algorithms allow for the low latency that is required in time-sensitive IoVs. Through simulation and qualitative analysis, the authors show that the proposed scheme has lower latency, communication and storage costs, and greater privacy than other similar methods. In recent years, edge computing has become the center of attention in IoV. It eases congestion in vehicle networks, using end-to-end communication. This is performed by transmitting information to end nodes instead of the server, thereby reducing latency. The authors of [119] propose a secure service offloading method (SOME) that uses edge computing but is able to tackle some of the traditional edge computing issues. The scheme uses a software-defined network (SDN) to handle issues with quality of service, including resource conflicts and communication interruptions. These issues can cause the degradation of other similar schemes. Issues with privacy related to SDN cause a need for modification of current SDN systems. These are handled through the use of an offloading time for drivers, which creates a gap in the location of the vehicle and what can be observed by a server. SOME is evaluated through experiments conducted by the authors. In most instances, the privacy of the IoV focuses mainly on location information. In some taxis, facial imaging data are collected. To protect these data, a new scheme that generates and restores facial images is proposed by the authors of [120]. The method first runs numerous perturbations based on the semantics of the image. Then, it is run through an adversarial network to generate a scrambled version of the original image. The key is then concealed steganographically in the image. A restorative network is able to read the key and understands how to decode the received image. Through experimental testing, the proposed scheme shows high detection resistance, better quality, and more secure filtering defense. To address the issues of MITM, impersonation, unlinkability, and traceability attacks, the authors of [121] suggest the use of a batch verification-based authentication mechanism that takes advantage of ECC techniques. This allows a vehicle to authenticate neighboring vehicles. The authors have compared this scheme to relevant schemes and have concluded that the proposed scheme offers better security and functionality than other similar methods. * Trust There are many issues with current systems of large network IoV communication. In the paper [122], the authors discuss some of the challenges faced by current models and other foreseeable challenges that these networks face.