High reliability and low latency are crucial for urban rail transits. In this paper, we introduce communication strategies for communication-based train control (CBTC) systems using long-term evolution for metro (LTE-M) to improve the reliability and latency. To be specific, the FlashLinQ-based Train-to-Train (T2T) communication schemes are introduced considering both the transmission delay and the packet drop. The quantified resilience is also introduced as a system metric to evaluate the preservation and recovery performance of CBTC systems. First, a novel urban rail transit wireless communication model is established using FlashLinQ-based T2T communications. Then, we introduce a novel cognitive control scheme based on LTE-M with T2T communication to enhance the quality of service and the resilience of multi-train CBTC systems. In the introduced scheme, Q-learning is used to generate optimal control strategies considering both wireless communication parameters adaption and train control parameters. Extensive simulations are conducted and the results show that the resilience of CBTC systems can be enhanced using the introduced scheme. Furthermore, using the introduced scheme, not only the gaps in optimal velocity versus distance curve are smaller, but also the unplanned traction and breaking are reduced as well.

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.

In vehicular ad hoc networks (VANETs), road safety and road traffic efficiency can be improved through message interaction between vehicle users, which inevitably relies on secure identity authentication, and message credibility verification. Existing authentication and message verification mechanisms are prone to severe single points of failure and low authentication efficiency due to their reliance on the trusted third party, especially during the user registration phase. This article proposes a double-layer blockchain and decentralized identifiers assisted secure registration and authentication (BDRA) mechanism for decentralized VANETs, which can achieve the following advantages: 1) realizing a secure and decentralized user registration phase by using the decentralized identifier (DID) technology; 2) accomplishing efficient authentication and message verification by combining double-layer blockchain, DIDs and a reputation feedback strategy; and 3) enabling a more efficient cross section reregistration that reduces the communication time by 30%. The security features and efficiency of the BDRA mechanism are demonstrated by carrying out security analysis and performance evaluation, which is based on the hyperledger fabric (HLF) platform.

Quality of service (QoS) guarantee is critical in urban rail transit. In this paper, the train-centric communication-based train control (CBTC) systems through train-to-train (T2T) wireless communication is introduced based on the modification of LTE vehicle-to-everything (LTE-V2X). To be specific, a novel train-centric CBTC systems is established based on T2T wireless communication where distributed sensing-based semi-persistent scheduling (DS-SPS) is served as the resource allocation scheme in the T2T scenario. The quantized age of information (AoI) is used as an integrated system QoS indicator of the CBTC wireless communication systems in urban rail transit. Machine learning techniques especially Q-learning is further utilized to improve system AoI performance. Simulation results show that the proposed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning can achieve improved system AoI and peak AoI performance compared with fixed SPS policy. Furthermore, the system performance of the designed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning is shown to be better than traditional LTE-M and WLAN based wireless communication systems.

In recent years, intelligent transportation systems and automatic train control technologies have been developed rapidly. The communication-based train control (CBTC) system through train-to-train communications is very important in the intelligent transportation system and it uses wireless communication links to transmit the train control commands. However, the CBTC system is sensitive to information security attacks. Therefore, a comprehensive resilient control strategy is proposed for the CBTC system under Denial of Service (DoS) attacks. As a cyber physical system, the CBTC system includes two layers. In the communication layer, a strongly robust train-to-train communication topology is proposed which adopts a bidirectional communication strategy and a three times communication protocol to achieve the goal of resisting multi-channel DoS attacks. In the physical layer, an observer-based distributed intrusion detection method is firstly proposed to detect the attack. Further, a resilient control method based on a distributed state observer of the leader train is proposed to avoid triggering the train safety protection mechanism frequently. Finally, simulation results prove that the proposed comprehensive resilient control algorithm is effective in the CBTC system.

Vehicular Edge Computing and Networks (VECoNs) have gained popularity for its enhanced Internet of Vehicles (IoV) capabilities. To satisfy the needs of delay-sensitive and computation-intensive in-vehicle applications, VECoNs need to provide low-latency task offloading services. However, existing offloading frameworks generally overlook the spatially and temporally heterogeneous computation task arrival patterns. The former causes overloading and underloading of RSU computational resources and thus hinders further reduction of offloading latency on the macro-scale, while the latter emphasizes the importance of long-term system performance, especially energy constraints, posing challenges to the design of offloading framework and optimization strategies. This paper introduces a novel distributed two-stage task offloading architecture based on Lyapunov and multi-agent deep deterministic policy gradient (MADDPG). On one hand, it jointly optimizes the initial offloading stage within VEC subsystems and the RSU peer offloading stage to minimize offloading delays for each VEC subsystem. On the other hand, it incorporates RSU energy consumption within long-term constraints to formulate the offloading optimization problem. After decoupling the energy coupling between RSU time slots using the Lyapunov algorithm, a Lyapunov and MADDPG-based distributed task offloading (LAMETO) algorithm is presented to solve the optimal problem in a distributed manner. Simulation results show that the proposed framework and algorithm can reduce the system delay, energy consumption, and energy deficit while stabilizing convergence.