# SROR: A Secure and Reliable Opportunistic Routing for VANETs
###### Abstract  
In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols. vehicular ad hoc networks; opportunistic routing; deep reinforcement learning; black hole attack; gray hole attack +
Footnote †: journal: Accepted: 2  
cyber attacks including but not limited to distributed denial-of-service [5], jamming, and spoofing. Among many cyber attacks, the packet drop attack is the most representative. The loss of packets endangers the transmission of information and even life safety of pedestrians and drivers. Therefore, to achieve a high packet delivery ratio, designing an attack detection method is essential for VANETs [6]. Constructing a reliable and secure routing is a huge challenge because of the inherent mobility of vehicles and packet drop attacks. The mobility characteristic results in recurrent topology deviations and intermittent connection. To this end, it is hard to construct a stable entire path between a source node and its destination. Reluctantly, some relay nodes are continually chose on the basis of current network environment. Finally, the packet is delivered hop by hop toward its destination with the help of some relay nodes. Due to high mobility, how to choose a relay node to successfully forward packet is a challenging issue [7]. The employed metric and strategy for selecting a relay node may affect the performance of transmitting packets. The former is applied to evaluate the link from a sender to a received node, and the latter refers to whether a relay node is chose from the candidate set opportunistically or deterministically. As dynamic changes of topology, opportunistic routing is more appropriate for VANETs compared with deterministic routing. In opportunistic routing, a source node transmits packet toward a set of neighbor nodes instead of just one neighbor node. This transmission mechanism aids in improvement of the success rate of transmitting packet. One of nodes in this set is selected to be a relay node, and it will be responsible for forwarding the packet. [PERSON] et al. [8] proposed an adaptive routing protocol based on ant colony optimization. In the routing protocol, the routing problem is formulated as a constrained optimization problem. Then, an ant colony optimization algorithm is employed to solve the problem. However, the computation time of ant colony optimization increases with the number of nodes. Besides, some important network metrics like relative velocity and connectivity are not considered in route discovery. In [9], an efficient multi-metric routing algorithm was proposed. The routing algorithm considered the connectivity and relative velocity, but it did not consider the attack issues.