ENABLING TECHNOLOGIES FOR INTERNET OF THINGS: OPTIMIZED NETWORKING FOR CONNECTED AND AUTONOMOUS VEHICLES

Cluster stability-driven optimization for enhanced routing in heterogeneous vehicular networks

Future Connected and Autonomous Vehicles (CAVs) will be equipped with a large set of sensors. The large amount of generated sensor data is expected to be exchanged with other CAVs and the road-side infrastructure. Both in Europe and the US, Dedicated Short Range Communications (DSRC) systems, based on the IEEE 802.11p Physical Layer, are key enabler for the communication among vehicles. Given the expected market penetration of connected vehicles, the licensed band of 75 MHz, dedicated to DSRC communications, is expected to become increasingly congested. In this paper, we investigate the performance of a vehicular communication system, operated over the unlicensed bands 2.4 GHz - 2.5 GHz and 5.725 GHz - 5.875 GHz. Our experimental evaluation was carried out in a testing track in the centre of Bristol, UK and our system is a full-stack ETSI ITS-G5 implementation. Our performance investigation compares key communication metrics (e.g., packet delivery rate, received signal strength indicator) measured by operating our system over the licensed DSRC and the considered unlicensed bands. In particular, when operated over the 2.4 GHz - 2.5 GHz band, our system achieves comparable performance to the case when the DSRC band is used. On the other hand, as soon as the system, is operated over the 5.725 GHz - 5.875 GHz band, the packet delivery rate is 30% smaller compared to the case when the DSRC band is employed. These findings prove that operating our system over unlicensed ISM bands is a viable option. During our experimental evaluation, we recorded all the generated network interactions and the complete data set has been publicly available.

Optimal Control for Connected and Autonomous Vehicles at Signal-Free Intersections

Considering mixed-autonomy traffic with connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs), a CAV cooperative control strategy was proposed to improve the traffic flow efficiency in freeway ramp merging areas. The roadside unit controlled the movements of CAVs both on the ramp and on the mainline before the merging point, according to real-time traffic conditions. Microscopic simulations based on cellular automata were performed to evaluate the influences of the proposed cooperative control strategy on the average vehicle delays of the mainline, the ramp, and the entire merging area. Sensitivity analyses were performed with varying CAV market penetration rates and traffic demand levels. Results show that, compared to no-control case, the proposed cooperative control strategy may effectively reduce the average vehicle delay of the mainline. Though the average vehicle delay of the ramp increases, the average vehicle delay of entire merging area is still improved, because the mainline flow volume is usually much larger than the ramp flow volume. Furthermore, the proposed cooperative control strategy only requires a limited number of control variables, making it feasible to determine the optimal control variables based on sensitivity analysis.

With the adoption of sensing technology, vehicles are able to gather information about driver's environment, like speed, temperature, moisture and road condition, etc. The state-of-the-art wireless technologies also provide vehicles with the ability of communicating to their neighboring vehicles on the road, as well as the static frameworks on the road side. Vehicular Sensor Network (VSN) is an emerging paradigm in vehicular networks. In this paper, we propose a new VSN scenario, Road Probing. The Road Side Unit (RSU) initiates the probing process and selects the passing by vehicles as probes to collect traffic and environment information. The selected vehicles sense the desired data and forward it back to the RSU in a multi-hop fashion. A dynamically growing one-dimension probing chain originated from the RSU is formed by participated vehicles as the probing process going on. A mobility profile based probe selection method is developed to improve this chain's connectivity as it's the key issue to assure the quality of data collection. The performance of Road Probing is demonstrated by simulation experiments.

Vehicular mesh networks could be an important new way to provide Internet access in urban areas using dedicated short range communications (DSRC). In some circumstances, DSRC technology is more cost-effective than expanding the capacity of cellular networks. We determine what those circumstances are by combining our simulation model with data collected from an actual vehicular network that is operating in Portugal. We use the model to estimate how much Internet traffic can be offloaded to vehicular networks that would otherwise be carried by cellular networks, under a variety of conditions. We use offloaded traffic to estimate the benefits of cost savings of reduced cellular infrastructure due to offload, and the cost of the DSRC vehicular network to carry that traffic. Then, we determine when benefit exceeds cost. We find that the benefits from the Internet traffic alone are not enough to justify a universal mandate to deploy DSRC in all vehicles, i.e., the benefits of Internet access alone are less than total costs. However, the majority of DSRC-related costs must be incurred anyway if safety is to be enhanced. Thus, soon after a mandate to put DSRC in new vehicles becomes effective, the benefits of Internet access through vehicular networks in densely populated areas would be significantly greater than the remaining costs, which are the costs of roadside infrastructure that can serve as a gateway to the Internet. Moreover, the benefit of Internet access would exceed DSRC infrastructure cost in regions with lower and lower population densities over time.

The increasing cyber-physical integration in connected and autonomous vehicles (CAVs) has amplified the need for robust intrusion detection systems (IDS) to secure controller area network (CAN) communications. Existing deep learning approaches, such as CNNs and LSTMs, have demonstrated strong potential but often struggle to detect stealthy and sophisticated attacks, primarily due to inadequate temporal modeling and the use of datasets with limited fidelity that do not reflect real-world attack complexities. To address these limitations, we propose an optimized hybrid IDS model that integrates one-dimensional convolutional neural networks (1D-CNN) with bidirectional long short-term memory (BiLSTM) layers. The architecture is specifically designed to extract spatio-temporal features from CAN traffic with minimal complexity, enabling efficient detection of subtle attack patterns. Our model incorporates a time-aware design that allows it to detect attacks as they evolve, even when they occur in localized segments of the payload. It was trained and evaluated using the high-fidelity ORNL ROAD dataset, which includes physically validated fuzzing and targeted CAN ID attacks. The proposed model achieved an accuracy of 99.73%, a Matthews Correlation Coefficient (MCC) of 0.9401, and maintained average low false positive of (≤ 0.00045) and false negative of (≤ 0.00047) rates. It also demonstrated a low average prediction latency of 0.535 milliseconds per CAN frame, highlighting its architectural efficiency. This performance reflects a deliberate design balance between lightweight complexity and high detection accuracy, advancing the development of practical and robust deep learning-based IDS solutions for modern vehicular networks.

To meet the ever-increasing on-road wireless traffic, internet of vehicles is designed to provide flexible deployment energy-efficient operation and low-latency communication with roadside units (RSUs). However, according to the weakness of the dedicated short-range communication (DSRC) technology, it can not guarantee the reliable and efficient communication of the vehicular network, especially in the high density scenario. In this paper, we propose a heterogeneous vehicular network consists of LTE eNBs and RSUs, which could support high transmission data rate, large coverage and low latency by widely deployed existing wireless LTE infrastructures. In order to improve the network energy efficiency (EE), we introduce an energy-efficient resource allocation algorithm, which consists of the vehicles and resource association algorithm and EE based auction algorithm. In the first algorithm, we optimize the association of vehicle pairs and resource blocks according to the individual match degree in an iterative manner. To ensure the fairness among vehicles, we employ the EE based auction algorithm to assign the optimal vehicles to resource blocks. Simulation results show that the proposed algorithm could achieve superior performance in terms of EE and fairness in all aspects than comparison algorithms in literatures.

Vehicular ad hoc networks (VANETS) are simply a collection of onboard units (OBUs) and roadside units (RSUs) that communicate using dedicated short-range communications (DSRC) utilizing 75 Mhz in 5.9 GHz frequency band for wireless access in vehicular environment (WAVE). The frequency channel layout of WAVE has been divided into seven channels, with one channel for service safety applications and the remaining for service non-safety applications. The nature of the VANET environment being dynamic, it is most likely that packet forwarding over varied time intervals may be disconnected, causing network delays. In such a case the message containing information needs to be with the current vehicle until a new intelligent node becomes active. Extensive studies have been conducted on the propagation of messages in vehicular networks. Although conventional methods are mainly focused on similar traffic, in this only high, low-density traffic is considered. The intelligent mobile node structure in NS-3 has been designed in such a way as to support 802.11 MAC with a single channel. However, this intelligent mobile node is not appropriate for WAVE multi-channel operation.

DIVINE: Data offloading in vehicular networks with QoS provisioning

Connected and autonomous vehicles (CAVs), unlike conventional cars, will utilise the whole space of intersections and cross in a lane-free order. This article formulates such a lane-free crossing of intersections as a multi-objective optimal control problem (OCP) that minimises the overall crossing time, as well as the energy consumption due to the acceleration of CAVs. The constraints that avoid collision of vehicles with each other and with road boundaries are smoothed by applying the dual problem theory of convex optimisation. The developed algorithm is capable of finding the lower boundary of the crossing time of a junction which can be used as a benchmark for comparing other intersection crossing algorithms. Simulation results show that the lane-free crossing time is better by an average of 40% as compared to the state-of-the-art reservation-based method, whilst consuming the same amount of energy. Furthermore, it is shown that the lane-free crossing time through intersections is fixed to a constant value regardless of the number of CAVs.

With the development of the vehicular communications technology, roadside units (RSUs) placement problem has recently received considerable attentions in vehicular ad hoc networks (VANETs). This paper addresses the roadside units (RSUs) placement problem for traffic flow coverage requirement number in vehicular ad hoc networks (VANETs). Given a set of road segments, some traffic flows and a coverage requirement number n, the problem is how to find the optimal placement of RSUs, such that each traffic flow is covered by at least n RSUs. We first investigate full RSUs placement problem, which place all the RSUs on the given road segments totally. We prove that this problem is NP-hard. Then, we propose a greedy algorithm to solve the problem and theoretical analysis is given. We also study incremental RSUs placement problem, which place the partial RSUs based on the deployed RSUs on the given road segments. Based on the proposed greedy algorithm, we also propose a algorithm to solve the incremental RSUs placement problem. Extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms.

The reliability of communication in vehicular networks depends mostly on the density of DSRC-enabled vehicles that form the network. In highway scenarios, and depending on the time of day, the probability of having a disconnected vehicular network can be very high, which hinders communication reliability. To improve communication in these scenarios, infrastructure points known as Road Side Units (RSU) may be used. RSUs, however, have an associated cost, and therefore the number of RSUs needs to be minimized while still providing a significant improvement on communications. In this paper we study the effect of including RSUs as relay nodes to improve communication in highway scenarios. We model the average time taken to propagate a packet to disconnected nodes (denoted as re-healing time) when considering both scenarios of connected and disconnected RSUs. We then compare the results of both these models and of a model with no RSUs. Results show significant improvements with RSU deployments, both connected and disconnected, particularly in multi-cluster communication scenarios.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the Internet. For the reliable data upload and download, TCP can be used for vehicular networks. However, TCP requires the connection initialization using three-way handshaking for the data exchange between two end systems over the Internet. Thus, the efficient operation of TCP is important for data services in the vehicular networks. This paper proposes a method of TCP Context Migration Scheme (TOMS) for the enhancement of data services in vehicular networks. TOMS provides vehicles with proactive TCP connection initialization using a moving TCP proxy as a cluster head, which will have the Internet connectivity with a Road-Side Unit (RSU). A cluster member can initiate its TCP connection toward its corresponding TCP end-system (e.g., server and peer) via the TCP proxy within its cluster. The TCP proxy performs the TCP connection set-up for the sake of other cluster member vehicles and acknowledges the received TCP segments toward these vehicles. When the TCP proxy moves out of the communication range of the RSU, it transfers the TCP contexts of other vehicles to another vehicle, which will play the role of a TCP proxy through the proposed TCP context migration scheme. Also, the RSU works as a fixed TCP proxy for handling the acknowledgement of TCP segments and TCP timer handling (e.g., persist timer and keepalive timer) when there happens the disconnection between the moving proxy and the RSU. Thus, it is shown that our TOMS outperforms the legacy TCP in vehicular networks.

Roadside Units (RSUs), which enable the vehicles-to-infrastructure communication, are deployed along the roadside to handle the growing communication demands as the number of vehicles increases. How to optimally deploy the RSUs and efficiently use them to enhance the vehicular network performance are important problems in the RSU-aided opportunistic networks. In order to solving these problems, we need to understand fundamental laws and properties of the opportunistic contacts. Due to the prohibitive costs to collect enough realistic contact records, to the best of our knowledge, little experiment work has been conducted to study the communication contact patterns in RSU-aided opportunistic networks. In this work, we carry out an extensive experiment involving tens of thousands of operational taxis in Beijing city. Based on this newly collected Beijing trace and the existing Shanghai trace, we find some invariant characters for the communication contacts for large scale RSU-aided vehicular opportunistic networks. In terms of inter-contact time between the RSUs and vehicles, we find it obeys exponential distribution, while in terms of contact rate, it follows Poisson distribution. Our observations reveal fundamental patterns for large scale vehicular mobility, and further provide useful guidelines for the design of new routing protocols and their performance evaluation for RSU-aided vehicular opportunistic networks.