With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

A major development in the field of access control is the dominant role-based access control (RBAC) scheme. The fascination of RBAC lies in its enhanced security along with the concept of roles. In addition, attribute-based access control (ABAC) is added to the access control models, which is famous for its dynamic behavior. Separation of duty (SOD) is used for enforcing least privilege concept in RBAC and ABAC. Moreover, SOD is a powerful tool that is used to protect an organization from internal security attacks and threats. Different problems have been found in the implementation of SOD at the role level. This paper discusses that the implementation of SOD on the level of roles is not a good option. Therefore, this paper proposes a hybrid access control model to implement SOD on the basis of permissions. The first part of the proposed model is based on the addition of attributes with dynamic characteristics in the RBAC model, whereas the second part of the model implements the permission-based SOD in dynamic RBAC model. Moreover, in comparison with previous models, performance and feature analysis are performed to show the strength of dynamic RBAC model. This model improves the performance of the RBAC model in terms of time, dynamicity, and automatic permissions and roles assignment. At the same time, this model also reduces the administrator’s load and provides a flexible, dynamic, and secure access control model.

Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept. INDEX TERMS Access control, hybrid access control, secure vehicle localization, machine learning, neural networks, Internet of Things.

The intelligent transportation system is currently an active research area, and vehicle re-identification (Re-Id) is a fundamental task to implement it. It determines whether the given vehicle image obtained from one camera has already appeared over a camera network or not. There are many possible practical applications where the vehicle Re-Id system can be employed, such as intelligent vehicle parking, suspicious vehicle tracking, vehicle incident detection, vehicle counting, and automatic toll collection. This task becomes more challenging because of intra-class similarity, viewpoint changes, and inconsistent environmental conditions. In this paper, we propose a novel approach that re-identifies a vehicle in two steps: first we shortlist the vehicle from a gallery set on the basis of appearance, and then in the second step we verify the shortlisted vehicle’s license plates with a query image to identify the targeted vehicle. In our model, the global channel extracts the feature vector from the whole vehicle image, and the local region channel extracts more discriminative and salient features from different regions. In addition to this, we jointly incorporate attributes like model, type, and color, etc. Lastly, we use a siamese neural network to verify license plates to reach the exact vehicle. Extensive experimental results on the benchmark dataset VeRi-776 demonstrate the effectiveness of the proposed model as compared to various state-of-the-art methods.

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.