Vehicular Ad-hoc Networks (VANETs) have been gaining significant attention from the research community due to their increasing importance for building an intelligent transportation system. The characteristics of VANETs, such as high mobility, network partitioning, intermittent connectivity and obstacles in city environments, make routing a challenging task. Due to these characteristics of VANETs, the performance of a routing protocol is degraded. The position-based routing is considered to be the most significant approach in VANETs. In this paper, we present a brief review of most significant position based unicast routing protocols designed for vehicle to vehicle communications in the urban environment. We provide them with their working features for exchanging information between vehicular nodes. We describe their pros and cons. This study also provides a comparison of the vehicle to vehicle communication based routing protocols. The comparative study is based on some significant factors such as mobility, traffic density, forwarding techniques and method of junction selection mechanism, and strategy used to handle a local optimum situation. It also provides the simulation based study of existing dynamic junction selection routing protocols and a static junction selection routing protocol. It provides a profound insight into the routing techniques suggested in this area and the most valuable solutions to advance VANETs. More importantly, it can be used as a source of references to other researchers in finding literature that is relevant to routing in VANETs.

In order to improve patient outcomes, brain tumors—which are notorious for their catastrophic effects and short life expectancy, particularly in higher grades—need to be diagnosed accurately and treated with care. Patient survival chances may be hampered by incorrect medical procedures brought on by a brain tumor misdiagnosis. CNNs and computer-aided tumor detection systems have demonstrated promise in revolutionizing brain tumor diagnostics through the application of ML techniques. One issue in the field of brain tumor detection and classification is the dearth of non-invasive indication support systems, which is compounded by data scarcity. Conventional neural networks may cause problems such as overfitting and gradient vanishing when they use uniform filters in different visual settings. Moreover, these methods incur time and computational complexity as they train the model from scratch and extract the pertinent characteristics. This paper presents an InceptionV4 neural network architecture-based Transfer Learning-based methodology to address the shortcomings in brain tumor classification methods. The goal is to deliver precise diagnostic assistance while minimizing calculation time and improving accuracy. The model makes use of a dataset that contains 7022 MRI images that were obtained from figshare, the SARTAJ dataset, and Br35H, among other sites. The suggested InceptionV4 architecture improves its ability to categorize brain tumors into three groups and normal brain images by utilizing transfer learning approaches. The suggested InceptionV4 model achieves an accuracy rate of 98.7% in brain tumor classification, indicating the model’s remarkable performance. This suggests a noteworthy progression in the precision of diagnosis and computational effectiveness to support practitioners making decisions.

Drones are of different shapes, sizes, characteristics, and configurations. It can be classified for the purpose of its deployment, either in the civilian or military domain. The earliest usage of drones was totally for military purposes, but manufacturers promptly tested it for civilian fields like border surveillance, disaster relief, pipeline inspection, and rescue. Drone manufacturing, equipment installation, power supply, multi-rotor system, and embedded sensors are not the pressing issues for researchers of drone technologies. What is required is to utilize a drone for a complex operation and ensure secured data broadcasting among drones with the ground control station via a self-organized, resourceless, and infrastructureless network (Flying Ad Hoc Networks (FANETs)). These operations are no less important in areas like an emergency, search and rescue operations, border surveillance, and physical phenomenon sensing for the end-user. However, it is not without some challenges for the researchers keeping in view the threats these operations are exposed to concerning security issues and challenges. To overcome these challenges, the researchers have to strive towards a secured drone operation by developing a robust and lightweight key agreement protocol for IoD deployment civilian drones. Consequently, the researchers in this study have attempted to design a key agreement scheme for the IoD deployment of civilian drones. The security of the proposed key agreement scheme has been verified by ProVerif2.02 and Real-Or-Random (ROR) model, while its performance scenario has been tackled by considering storage, computation, and communication overheads analysis. In comparing the proposed framework with prior protocols, it has been demonstrated that the scheme is quite efficient and may be recommended for operations in a given IoD environment.

This paper presents a novel position-based routing protocol for vehicular ad hoc networks (VANETs) to enhance traffic safety and traffic organization and facilitate driving through a smart transportation system. The protocol is referred to as the traffic flow-oriented routing (TFOR) protocol for VANETs. It considers a real-time urban scenario with multi-lane and bi-directional roads. It chooses junction optimally considering vehicular traffic flows to accomplish robust routing paths and thereby forwarding the data packets. The new junction selection mechanism and routing between the junctions is based on two-hop neighbor information, which increases packet-delivery ratio and decreases end-to-end delay. We designed, implemented, and compared TFOR against existing routing protocols of VANETs (greedy-perimeter stateless routing (GPSR), geographic source routing (GSR), and enhanced greedy traffic-aware routing (E-GyTAR)). Simulation outcomes in urban scenarios show that TFOR minimizes average end-to-end delay and routing overhead by on average 15.3% and 19.5%, respectively, compared to GPSR. It reduces routing overhead up to 17% compared to GSR. TFOR maximizes packet-delivery ratio on an average of 17.5%, 10.7%, and 7.2% compared to GPSR, GSR, and E-GyTAR, respectively.

This study presents the current state of research on multi-factor authentication. Authentication is one of the important traits in the security domain as it ensures that legitimate users have access to the secure resource. Attacks on authentication occur even before digital access is given, but it becomes quite challenging with remote access to secure resources. With increasing threats to single authentication schemes, 2Factor and later multi-factor authentication approaches came into practice. Several studies have been done in the multi-factor authentication discipline, and most of them proposed the best possible approaches, but there are very limited studies in the area that can comprehend all these innovative and effective approaches. Using Web of Science data of the research publications on the topic, the study adopted the bibliometric approach to find the evolution of authentication in the security domain, especially multi-factor authentication. This study finds the impact of the research in the selected domain using bibliometric analysis. This research also identifies the key research trends that most of the researchers are paying attention to. The highest number of publications on multi-factor authentication were published in 2019 while the highest number of citations were received in 2014. United States, India, and China are the leading countries publishing the most on multi-factor authentication.

Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent literature. The proposed protocol’s security has been analyzed formally using BAN logic and ProVerif2.02 and informally using pragmatic illustration. Simultaneously, at the end of the paper, the performance analysis result shows a delicate balance of security with performance that is often missing in the current protocols.