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The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network.

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency.

The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.

Experiments on revealed preference often use budget sets that are randomly and independently drawn according to some criteria for each participant. However, this means that the budget sets faced by different individuals are not the same. This paper proposes a method to control for these differences. In particular, we control for the “power” of different budget sets by examining the consistency of an individual’s choices relative to some simulated baseline behavior conditional on budgets faced by the individual. We apply this methodology to two existing experimental datasets. Our results show that failure to account for this variation results in a bias when looking directly at measures of choice consistency and the sign of this bias depends on the measure being used. However, controlling for this variation does not change the correlation between measures of choice consistency and observable demographic characteristics like income and education.