# 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  
Internet of Vehicles (IoV)  
The IoV is a distributed network of connected cars, RSUs, and central Cloud platforms. The IoV makes it possible to integrate smart vehicles with the Internet and, consequently, their environments, such as other vehicles, pedestrians, public infrastructures, computing nodes, and sensors. The IoV should definitely be considered as a significant trend in recent research on the IoT, especially since software-defined Internet of Vehicles has received research attention in recent years [111]. Although a wide range of objectives such as performance, mobility, reliability, and QoS have been considered in the design of the Internet of Vehicles, security is probably the most important one [112]. Security challenges faced by the Internet of Health Things, as well as security mechanisms used in this area, are listed below:  
* Security Challenges
* Privacy IoV is the collection of sensors and devices located in cars that connect to each other or to a central location. The data collected by these sensors usually consist of location information and the identity of the vehicle. The privacy of this information is paramount to the safety of the vehicle's owner. One method proposed by the authors of [113] is a Concerted Silence-based Location Privacy-Preserving Scheme. The method would create an unlinkable connection between the location services of a vehicle and the safety functionality of the vehicle. The identity of the vehicle on the network must enter a silence period in which the location of the vehicle is not shared before it is able to link to the location of the car. This will allow for a bit of unpredictability in location without making it impossible for there to be a link between the two. In the testing, it was proven to be successful in prevention when simulated against a global passive attacker. The authors of [114] propose a different method based on double \(k\) anonymity (reduces the correlation between requests and users while maintaining service quality) to help secure the link between the identity of a vehicle and its location. The method makes use of a cloud server as an intermediate stop for data between a vehicle and a service provider. In the cloud server, requests from the vehicle are reduced through the use of permutation and combination methods that involve the use of randomly generated matrixes. The authors tested the algorithm extensively to prove its safety and time efficiency. In location services of IoV, Geo-indistinguishability (Geo-Ind) is an important privacy concern. This provides location privacy but does not work if exposed to poorly reported locations. Testing performed by the authors of [115] has shown a probability of over 50% that the true location is reported incorrectly. Tocorrect Geo-Ind, the authors recommend adding an additional mechanism called Perturbation-Hidden. This would allow for the pseudo-locations of the user to be guaranteed. The mechanism used to perform this task will be a differential private exponential approach. To attain 100% plausible pseudo-locations, the authors implement dynamic programming. Blockchain's involvement in the IoT has been important in recent years as more schemes are developed using its decentralized nature, which is fully secure, and information management abilities. The authors of [116] propose a scheme that uses blockchain in conjunction with federated learning to securely transmit learning model parameters instead of the actual data. Federated learning and blockchain, when integrated together, can prevent issues each of them have on their own. Federated learning on its own is susceptible to poisoning attacks, and blockchain is overly robust. The combination of the two allows for the system to work effectively and securely. As mentioned earlier, routing vehicles through the use of location-based data brings about several privacy concerns.