WiFi-based Vehicle Security System for Future Intelligent Transportation Systems

Emerging intelligent transportation systems (ITS) are based on several technologies. A new concept of integrating visible light communications (VLC) in ITS is introduced in this paper. VLC in ITS is a cost effective method of implementation. This paper presents a VLC broadcast system considering LED-based traffic lights. It discusses the integration of traffic light road side unit (RSUs) within the existing ITS architecture. A prototype demonstrator of the designed VLC systems is also presented. A robust modulation technique based on direct sequence spread spectrum (DSSS) sequence inverse keying (SIK) has been implemented to minimize the effect of noise sources. Experimental results show data communication range of over 40m with 200mm custom designed traffic light, even during bright sun light.

Road-to-vehicle communication (RVC) system will support multiple services in future intelligent transportation systems (ITS). To meet different QoS requirements especially on delay, it is important to differentiate data frames of different applications. However the upcoming Northern American DSRC standard based on IEEE 802.11a specification uses legacy DCF MAC and it lacks support for different QoS which is crucial in future multi-service RVC system. In This work we analyze different QoS requirements of the various possible applications in ITS and propose to use enhanced DCF (EDCF) for future RVC systems to give QoS enhancement. Through simulation, we are sure EDCF is much more suitable for future typical ITS applications.

Recent advances in wireless communication, sensing, computation and control technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Demanded by the requirements of information-centric and data-driven intelligent transportation systems (ITS), it is of great significance to explore new paradigms of IoV in supporting large-scale, real-time, and reliable information services. In this article, we propose a hierarchical system architecture, which aims at synthesizing the paradigms of software defined networking and fog computing in IoV and best exploiting their synergistic effects on information services. Specifically, a four-layer architecture is designed, comprising the application layer, the control layer, the virtualization layer, and the data layer, with objectives of enabling logically centralized control via the separation of the control plane and the data plane; facilitating adaptive resource allocation and QoS oriented services based on network functions virtualization and network slicing, and enhancing system scalability, responsiveness, and reliability by exploiting the networking, computation, communication, and storage capacities of fog-based services. On this basis, we further analyze newly arising challenges and discuss future research directions by presenting a cross-layer protocol stack. Finally, for the proof of concept, we implement the system prototype and give two case studies in real-world IoV environments. The results of field tests not only demonstrate the great potential of the new architecture, but also give insight into the development of future ITS.

Globally, intelligent vehicles and telematics R&D through the integration of IT technologies in the vehicle are significant increasing. Real-time data communication for intelligent transportation system (ITS) is very important. It collects real-time data from the vehicle and provides the information collected from ITS center. We propose an effective and secure communication scheme for these communication procedures. In particular, our proposed SIP-based MVPN reduces signaling cost and has many advantages in security aspects. In addition, our proposed scheme performs the mobility management applying Network Mobility (NEMO) for the communication between the vehicles. In other words, we propose an ITS communication mechanism of SIP-based mobile VPN and V2V NEMO. Finally, our performance analysis show that the ITS of SIP-based MVPN is significantly reducing the handoff signaling cost.

Cyber‐physical systems (CPSs) are characterized by integrating computation and physical processes. To cope with the challenges of the application of the CPSs in all kinds of environments, especially the cellular vehicle‐to‐everything (C‐V2X) which needs high quality end‐to‐end communication, the robustness and reliability for CPSs are very crucial. Aiming at the technical challenges of information transmission caused by the fading effect and the fast time‐varying characteristics of the channel for C‐V2X communication, an improved Tomlinson–Harashima precoding (THP) algorithm for multiple input multiple output (MIMO) systems is proposed. Channel state information (CSI) and correlation are exploited to compensate instantaneous CSI, which could reflect current real‐time channel status exactly. Further, the iterative water filling power allocation algorithm and the multiuser scheduling algorithm based on the greedy algorithm are jointly optimized and applied to the THP, which could improve the system performance. Simulation results show that the proposed algorithm can be efficiently applied to high‐speed mobility scenarios and improve bit error ratio (BER) performance as well as spectrum utilization.

Millimeter Waves (mmWave) systems have the potential of enabling multi-gigabit-per-second communications in future Intelligent Transportation Systems (ITSs). Unfortunately, because of the increased vehicular mobility, they require frequent antenna beam realignments - thus significantly increasing the in-band Beamforming (BF) overhead. In this paper, we propose Smart Motion-prediction Beam Alignment (SAMBA), a MAC-layer algorithm that exploits the information broadcast via DSRC beacons by all vehicles. Based on this information, overhead-free BF is achieved by estimating the position of the vehicle and predicting its motion. Moreover, adapting the beamwidth with respect to the estimated position can further enhance the performance. Our investigation shows that SAMBA outperforms the IEEE 802.11ad BF strategy, increasing the data rate by more than twice for sparse vehicle density while enhancing the network throughput proportionally to the number of vehicles. Furthermore, SAMBA was proven to be more efficient compared to legacy BF algorithm under highly dynamic vehicular environments and hence, a viable solution for future ITS services.

Current transportation systems face great challenges due to the increasing mobility. Traffic accidents, congestion, air pollution, etc., are all calling for new methods to improve the transportation system. With the US legislation in progress over vehicle communications and EU’s finalization of the basic set of standards over cooperative intelligent transportation systems (C-ITS), vehicular ad hoc network (VANET) based applications are expected to address those challenges and provide solutions for a safer, more efficient and sustainable future intelligent transportation systems (ITS). In this chapter, transportation challenges are firstly summarized in respect of safety, efficiency, environmental threat, etc. A brief introduction of the VANET is discussed along with state of the art of VANET-based applications. Based on the current progress and the development trend of VANET, a number of new features of future VANET are identified, together with a set of potential future ITS applications. The on-going research and field operational test projects, which are the major enabling efforts for the future VANET-based C-ITS, are presented. The chapter is of great interest to readers working within ITS for current development status and future trend within the C-ITS area. It is also of interest to general public for an overview of the VANET enabled future transportation system.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited battery power in smart vehicles, the concept of energy consumption is one of the main and critical challenges of the IoV environments. Optimizing resource management strategies for improving the energy consumption using AI-based methods is one of important solutions in the IoV environments. There are various machine learning algorithms for selecting optimal solutions for energy-efficient resource management strategies. This paper presents the existing energy-aware resource management strategies for the IoV case studies, and performs a comparative analysis among their applied AI-based methods and machine learning algorithms. This analysis presents a technical and deeper understanding of the technical aspects of existing machine learning and AI-based algorithms that will be helpful in design of new hybrid AI approaches for optimizing resource management strategies with reducing their energy consumption.

Different wireless communication technologies, such as wireless fidelity (Wi-Fi), 4G cellular technologies and worldwide interoperability for microwave access (WiMAX) have been used as alternatives or supplement to wired communication in intelligent transportation systems (ITS). Widespread deployment of wireless technologies in ITS require a comprehensive understanding of their performance, limitations and advantages in different field conditions. The authors evaluated performance of Wi-Fi wireless communication between adjacent roadside ITS devices (i.e., nodes) in different field conditions with varying characteristics of Wi-Fi technology. Field tests revealed that modulation rates, transmission power, line of sight, distance between nodes play critical roles in the performance of Wi-Fi communication in different roadway conditions. To achieve a desired level of performance requirements between adjacent nodes, minimum network performance thresholds must be realized in the field. Transportation agencies can identify the achievable performance, such as saturated throughput, delivery ratio and received signal strength at a particular location, by following the field test procedure developed in this research. The evaluation strategies and results presented in this study will contribute to the future planning and design of Wi-Fi communication for a roadway wireless sensor or device network considering corresponding communication performance requirements for specific ITS applications.

Intelligent Transportation System (ITS) and Internet of Vehicles (IoV) allows the vehicles to disseminate and exchange various messages among nearby vehicles in the network. It includes road safety, road accidents, location sharing, driver assistance, navigation, collision warning, traffic data, and toll payment. Moreover, the exchange of these messages is carried out in an insecure channel. Hence, it leads to several issues during transmission of these messages in resource constrained IoV networks. It includes reliable data dissemination, dynamic topology, mobility of the vehicles, vehicle user’s privacy, and lightweight authentication mechanism. The existing Vehicle to Vehicle (V2V) data dissemination techniques have limitations in security attacks such as side channel, device stolen, cloning attacks, high computational overhead, throughput, and high communication cost. In this article, we propose P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV: Privacy Preserving Lightweight Secure (V2V) Data Dissemination Scheme for IoV environment using lightweight cryptographic primitives such as SHA-256, concatenation and XoR operation. We evaluate P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV in terms of security and vehicle user’s privacy against several security attacks. We do an in-depth security analysis of the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV by using informal security analysis and provides security proofs. We also perform a comparative analysis of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV with the existing state-of-the-art IoV environment using various performance parameters. The test-bed experimental result shows that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV outperforms the existing V2V data dissemination technique in terms of computational cost, communication overhead, and energy consumption.

The presence of line-of-sight (LOS) and non-line-of-sight conditions in an intelligent transportation system (ITS) is critical, as it directly impacts the localisation and overall system performance. Distinguishing LOS, first-order and higher-order multipaths (HOMP) is hindered by the dense multipath (MP) propagation. The computational resource requirement and limited accuracy associated with traditional iterative methods have stimulated the use of deep learning (DL). This work proposes a novel, lightweight, dual limb attention (DLA) based DL network termed as “MOVENetx64”, for robust identification of MPs in ITS. DLA mechanism stacked with convolutional neural networks and long-short-term memory layers forms the prominent feature extractor, which enables feeding raw, unprocessed data into the DL pipeline. A novel alternating loss strategy is designed to nullify the effect of the extreme imbalance associated with the target output classes. Ray-tracing-based vehicular datasets for City, Suburban, and Highway are generated. A combination of time, received power, angular characteristics, and phase spectrum forms the input feature-set. The proposed MOVENetx64 showcases MP classification accuracy of (98.71%,99.41%,96.44%), and achieves the least HOMP prediction error of (0.6%,0.17%,2.28%) on the datasets. These results validate that the proposed MOVENetx64 is scalable and computationally efficient for distinguishing multipaths efficiently and enabling reliable vehicular communication in ITS.

The Intelligent Transport System(ITS) is an important development in this technological era. Effective communication in ITS is possible by means of different network systems and technologies, and as a result, driverless vehicles are becoming the target of cyber-attacks. The CAN bus is mainly used in ITS for internal communication and is vulnerable to cyber-attacks. CAN protocol gives minimal information for the design of intelligent algorithms for detecting intruders. The existing intrusion detection mechanisms are computationally expensive and may not be suitable for low-end ECUs in vehicles. In this work, we have given importance to the data part of the frame while preparing the dataset for the experiments. We have explored the performance of several classical machine learning algorithms like Random Forest algorithm, XGBoost algorithm, LightGBM algorithm, Naive Baye’s algorithm, and Decision Trees with the refined dataset and used the publically available dataset ’Car Hacking: Attack and Defence Challenge’ for the experimental evaluation. In the results, the Random Forest algorithm got the most significant accuracy with 95% and an F1-score of 95%. Compared to the computationally complex algorithms, we could achieve comparable and significant results with the classical machine learning algorithms which can be easily portable to ECUs in vehicles.KeywordsIntrusion detectionController area networkRandom forest algorithm.Lightweight intrusion detection

The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.

This work presents a functionality based model for survivable intelligent transportation system (ITS) control infrastructures. Specifically, a method is shown that allows us to determine the vulnerability of an ITS communication infrastructure to malicious acts and to derive optimal mitigations. The research is applied to a real ITS currently being implemented in a small town. Results show that significant reductions in vulnerability can be achieved utilizing the principle of redundancy with minimal effort. The method presented is especially suitable to reducing vulnerabilities of existing ITSs as it can be effectively applied after an ITS has been built. Mitigation techniques can be derived based on maximal benefit under consideration cost-benefit ratio.