Routing using reinforcement learning in vehicular ad hoc networks

Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon's environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.

Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET

The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.

Hybrid routing protocol for VANETs: Delay and trust based approach

VANET is mainly aimed at providing safety and security related information and traffic management. In future, VANET contributes to smart transportation system. Based on vehicle mobility, different routing protocols and traffic models were developed. In routing, trust between vehicles place an important role to forward safety related information. This paper aims at design of trust and delay based routing for hybrid communication in sparse VANET to avoid network attacks by malicious nodes. The proposed hybrid routing protocol works on the computation of trust in between vehicles and message reachable time (MRT). Route selection is done by considering the highest trust factor and minimum MRT. The performance effectiveness of the proposed scheme is evaluated by comparing with the Delay-aware and Backbone-based Geographic Routing for Urban VANETs (DBGR). The proposed scheme exhibits better performance in terms of packet delivery ratio, bandwidth utilization, end-to-end delay and control overheads.