## 6 IoT Security Solutions
### PKI-Based  
[PERSON] et al. in [214] proposed a scheme to provide security for big data collection in large-scale IoV. Mutual authentication and single sign-on-based authentication schemes were developed for collecting big data in a secure way. It assumes that each vehicle is loaded with a certificate by a certificate authority before registration. During registration, if the certificate is valid to the data center, it will be registered as a valid account, and a private-public key will be issued. It uses sign-on to start data flow. If the message is from a valid account, then a session key will be generated. The private-public key with a signature is used for encryption. If it passes the RSU area, then it needs to update the session key with the new RSU. If RSU finds the signature of the data center and ID in the certificate and also receives within the expired timestamp, then RSU will update the session key. It does not hide the ID of the vehicle. After changing the RSU area, a forgery vehicle can login using a fake certificate.  
In [215], [PERSON] et al. proposed a certificateless conditional authentication protocol to avoid the storage of certificates in TA and vehicles. Each vehicle has two unique tags which are linked with its secret key. Instead of using the secret key directly, two random values are used with the secret key to make the message unlinkability. It uses the discrete logarithm (DL) problem [216], and computational Diffie-Hellman (CDH) problem cryptography algorithm [217] and it assumes that the DL problem is intractable. In future work, they will work to remove vulnerabilities and limitations of this work to achieve security and privacy requirements and incentivize the participants.  
[PERSON] et al. proposed a solution based on the social network in [218]. It will calculate trust values based on online social networks for drivers and passengers. It also takes into account inter-devices authentication for in-vehicle, inter-vehicle trust calculation based on trust ratio, and also RSU trust calculation for each vehicle by getting recommendations from neighbor vehicles regarding a vehicle. Furthermore, it takes into account followed path by users and current mobility. It uses Chaotic map-based Chebyshev polynomials for computing security keys for inter-devices authentication, and social network trust is identified using the Advogato trust metric. The human honesty factor will be required if nodes' behavior is unclear/compromised. This scheme required trusted third parties such as social network platforms, network providers, etc. It introduced delay to compute human factors and location-related trust.  
[PERSON] et al.'s work [219] shows secure performed enriched channel allocation, i.e., shared channel, using commutative RSA (CRSA) [220]. In the CRSA scheme, two prime numbers are required to be sent to all vehicles using random number encryption and decryption parameters, which will be generated, and it is required to exchange keys to each vehicle. Data will be sent to the destination vehicle after encrypting in several vehicles by their keys, and the destination vehicle will decrypt the data using each vehicle's keys.  
[PERSON] et al. [221] also proposed a secure smart city communication protocol using a smart card, password, and extended chaotic maps. The protocol used random number with Hash functions (31 times) to make it lightweight. It contains processes to change password and revocation of smart card. If the smart card is stolen and the attacker can guess the online password through the side-channel attack, the smart city could be compromised.