Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.Comment: Accepted in IEEE Global Communications Conference (GLOBECOM) 201

Throughput-efficient blockchain for Internet-of-Vehicles

Internet-of-Vehicle (IoV) is empowering smart vehicles with data collection and sharing capabilities, and blockchains have been introduced to manage the IoV data due to many advantages, including decentralization, security, reliability, and scalability. Nevertheless, existing IoV blockchain models suffer from poor security against collusion attacks instigated by malicious blockchain miners typically represented by roadside units (RSUs). To address this problem, additional block verifiers, e.g., vehicles, can be recruited during block verification, which enhances security but also can lead to the reduced throughput. Therefore, in this paper, we propose a resource management scheme for IoV blockchains to enhance the system security while maximizing the throughput by optimizing contributed computing resources from RSUs and recruited vehicles. We show that the optimal strategies of RSUs and vehicles can be found through the Karush-Kuhn-Tucker (KKT) conditions and verify (using simulations) that our scheme achieves the higher throughput with enhanced security compared to the existing IoV blockchains

Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications

In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45% compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.Comment: Accepted in IEEE Transactions on Wireless Communications, Sept. 201