The communication in Intelligent Transportation Systems (ITS) suffers from various security and privacy related issues as several attacks, like replay, man-in-the-middle (MiTM), impersonation, data leakage and unauthorised data update attacks can be mounted by an adversary. Therefore, we need a robust security mechanism to sort out such issues, which can be further enhanced through the use of mechanism of blockchain technology. In this paper, we propose a blockchain-based secure data exchange and storage framework for the ITS (in short, we call it as BDESF-ITS). We conduct the security analysis of the BDESF-ITS to prove its robustness against various possible attacks. The practical implementation of the BDESF-ITS is also carried out to observe its behaviour with the real world settings.

Trust is a fundamental concept in large-scale distributed systems like the Internet of Things (IoT). Trust helps to resolve choices into a decision. However, the trust calculation depends on the amount of uncertainty present in data sources. Trust in an IoT network is proportional to the amount of uncertainty generated by such sources as hardware malfunctions, network stability, adversarial issues, and the nature of data exchanged between the entities. The relationship between trust and uncertainty warrants approaches designed to maximize the former quality whilst minimizing the latter. Unfortunately, there is no consensus on an approach to ensure the trustworthiness of IoT networks, in particular, addressing the uncertainty issues in a fine-grained way. This paper aims to explore a generalized framework designed to manage trust in IoT networks of varying scales. In the proposed framework, several sources of uncertainty are expressed as quantities, trust ratings are calculated for individual entities in an IoT network, and a network model capable of effectively distributing workloads to trustworthy nodes is proposed. We use a practical use case of smart vehicular networks. By realizing this paper, a standardized approach to building trustworthy IoT networks can be established, which can further guide subsequent works in the field of trust management under uncertainty.

This talk does not have an abstract.

This talk does not have an abstract.

This talk does not have an abstract.

Vehicle-to-Everything (V2X) environment is driven by the evolution of the Internet-of-Vehicles (IoV), satisfying the applications of Intelligent Transportation Systems (ITS). The rapid growth in vehicles demanded intelligent services such as traffic congestion, road safety, collision avoidance, and efficient traffic monitoring for autonomous V2X environments. On-board units (OBUs) and Road-side units (RSUs) communicate using Controller Area Network (CAN) protocol to broadcast vehicle and traffic information. But, the CAN does not employ any security feature for encrypting the broadcasted messages. Hence, there is a demand to incorporate Artificial Intelligence (AI) techniques and Blockchain (BC) technology to handle security issues in CAN. Given this background, we proposed an AIempowered, BC-based secure data communication underlying a 6G network. We have used CAN Intrusion Detection (OTIDS) dataset for training and testing purposes. Our proposed architecture uses a Gaussian Naive Bayes classifier to classify the dataset into attacked and attack-free classes and outperforms with 93% accuracy compared to other machine learning algorithms. Also, we have achieved low data storage cost, low latency, and high scalability by employing Inter Planetary File System (IPFS) and a 6G network, respectively.

Smart vehicular network (SVN) intellectualize traditional transportation network, significantly enhancing traffic convenience and safety. However, high connectivity and massive devices bring more vulnerabilities, which severely compromise the security, privacy, and trust of the facilities and data. To address the ever-increasing security threats in SVN, we introduce an intrusion detection system to distinguish the abnormal traffic or behavior. A Deep Reinforcement Learning (DRL) based intrusion detection strategy is proposed in this paper to optimize the detection performance. We exploit a modified Dueling DQN model, in which interaction between agent and environment is transformed into a supervised machine learning task. Through action taking and reward feedback, the Dueling DQN model can be trained to learn the intrinsic features of traffic data. Finally, simulation result on benchmark intrusion detection dataset also verifies the feasibility and effectiveness of the proposed strategy.

Unmanned Aerial Vehicles (UAVs) are capable of a wide variety of social, economic, and military applications. Due to the fact that UAVs communicate through wireless technology, they are vulnerable to security assaults. Establishing trust between the UAV and the base station is a vital component of reducing these hazards in UAV networks. Due to the restricted resources available to UAVs, adopting typical trust-building procedures in UAV networks becomes challenging. Additionally, as the number of unmanned aerial vehicles (UAVs) increases, this issue gets more critical. To address this issue, we offer a lightweight attestation mechanism for unmanned aerial vehicle swarms. Comparative analyses reveal that the proposed approach outperforms the state-of-the-art already available.

Internet of Vehicles (IoV) allows the vehicles to disseminate and exchange various messages among nearby vehicles in the network. These messages includes road safety, road accidents, location sharing, driver assistance, navigation, collision warning, and toll payment. Moreover, the exchange of these messages are carried out using an insecure channel, which leads to several issues during transmission of these messages such as reliable data dissemination, dynamic topology, mobility of the vehicles, vehicle user's privacy, and low-cost authentication mechanism. The existing vehicle-to-anything (V2X) data dissemination techniques have issues like security attacks, high computational communication cost, and throughput. Motivated from these issues, in this article, we propose Secure and Low-Cost (V2X) data dissemination scheme known as P2COMM for connected vehicles using SHA-256, concatenation, and XoR operation. We evaluate the proposed scheme in terms of security and vehicle user's privacy against several security attacks. Then, we carried out an in-depth formal security analysis of the proposed scheme using AVISPA tool and informal security analysis along with security proofs. The performance of the proposed scheme is better in comparison to existing state-of-the-art schemes in terms of security, communication overhead, computation cost, and energy cost.

With the assistance of the fifth generation (5G) and the internet of things (IoT), intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient. To well assist the ITSs, advanced network architecture and reasonable offloading decision should be specially designed while ensuring data security. In this paper, we consider a blockchain-enabled intelligent transportation uplink scenario, which can collect and encrypt the aggregated data from smart vehicles (SVs) with blockchain. Considering the coupling of unmanned aerial vehicles (UAVs) position and offloading decision, we formulate a joint UAVs position optimization and data offloading decision problem in order to reduce the total time and energy consumption of data processing. We first divide UAVs into two categories and form neighborhood UAVs according to their data load. Then, we adapt the non-dominated sorting genetic algorithm II (NSGA-II) iteratively. Simulation results indicate that the algorithm can reduce energy consumption while ensuring the required time and achieving a good balance.

This paper proposes a deployment of UAVs in wireless sensor network (WSN) systems. Considering the energy-constrained nature of the wireless sensors, a multi-UAV deployment algorithm that minimizes the maximum power transmitted among the WSN for a given data rate and power constraints. The problem is divided into two subproblems; UAV-SN association and 2D positioning of the UAVs. First, the UAV-SN association is addressed using a customized Gale-Shapley algorithm. Then, the 2D positions of the UAVs are optimized using a modified pattern search algorithm. Finally, a combined optimization algorithm that integrates the approaches both the subproblems iteratively to provide an optimal or a near-optimal solution is introduced. The results indicate a significant performance gain as compared to the benchmark methods in terms of the number of iterations for convergence, maximum transmission power requirement power and the minimum number of UAV requirements.

Unmanned aerial vehicle (UAV) networks have widespread applications, ranging from surveillance and disaster management in the military domain to transportation and delivery of goods in the civilian domain. Regardless of the application, the placement of routing UAV nodes (especially in networks spanning long distances) is crucial in determining network performance parameters such as network lifetime and data transmission delay. In this paper, an Energy-efficient, Fault-Tolerant, and Area-optimized UAV placement scheme (EFTA) is proposed for search operations. A cluster-based UAV network is considered, in which the Cluster Members (CMs) are mobile and scan the geographic area of interest. The Cluster Heads (CHs) are quasi-static and route information from the CMs to the Ground Control Station (GCS). A multi-objective Cuckoo Search Algorithm is used to determine the placement of the CHs while minimizing energy consumption, maximizing area coverage, and maximizing tolerance to node failures. Further, a comprehensive analysis was performed against a state-of-the-art UAV placement algorithm. The analysis showed that EFTA gives a significant performance improvement when compared to the competing placement scheme in fault tolerance, power consumption, network lifetime, end-to-end delay, and packet delivery ratio.

The flexible altitude of unmanned aerial vehicles (UAVs)-mounted base stations (BSs) and their higher chance of establishing a line-of-sight (LOS) link towards ground users, make them an appealing solution for outdoor mmWave communication. However, the positioning of UAVs is a critical problem that affects both the coverage performance and energy consumption. In this work, considering a heterogeneous set of UAVs acting as aerial mmWave BSs, we develop an effective approach for the 3D positioning of the UAVs that leads to maximum coverage area with minimal power consumption. The UAVs have a varying transmit power and flight altitude range. Given a repository of UAVs, the proposed method finds an optimal subset of the available UAVs and determines their 3D position for maximum LOS coverage area with minimum energy consumption. First, we formulate an optimization problem to find the best subset of available UAVs along with their horizontal position. Next, we optimize the altitude of the UAVs based on the practical data of the geographical environment, such as the number and location of the buildings and other structures. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the heterogeneous UAV-supported small cell networks

This talk does not have an abstract.

This talk does not have an abstract.

This talk does not have an abstract.

This talk does not have an abstract.