Secure and Authenticated Message Dissemination in Vehicular ad hoc Networks and an Incentive-Based Architecture for Vehicular Cloud

Vehicular ad hoc Networks (VANETs) allow vehicles to form a self-organized network. VANETs are likely to be widely deployed in the future, given the interest shown by industry in self-driving cars and satisfying their customers various interests. Problems related to Mobile ad hoc Networks (MANETs) such as routing, security, etc.have been extensively studied. Even though VANETs are special type of MANETs, solutions proposed for MANETs cannot be directly applied to VANETs because all problems related to MANETs have been studied for small networks. Moreover, in MANETs, nodes can move randomly. On the other hand, movement of nodes in VANETs are constrained to roads and the number of nodes in VANETs is large and covers typically large area. The following are the contributions of the thesis. Secure, authenticated, privacy preserving message dissemination in VANETs: When vehicles in VANET observe phenomena such as accidents, icy road condition, etc., they need to disseminate this information to vehicles in appropriate areas so the drivers of those vehicles can take appropriate action. When such messages are disseminated, the authenticity of the vehicles disseminating such messages should be verified while at the same time the anonymity of the vehicles should be preserved. Moreover, to punish the vehicles spreading malicious messages, authorities should be able to trace such messages to their senders when necessary. For this, we present an efficient protocol for the dissemination of authenticated messages. Incentive-based architecture for vehicular cloud: Due to the advantages such as exibility and availability, interest in cloud computing has gained lot of attention in recent years. Allowing vehicles in VANETs to store the collected information in the cloud would facilitate other vehicles to retrieve this information when they need. In this thesis, we present a secure incentive-based architecture for vehicular cloud. Our architecture allows vehicles to collect and store information in the cloud; it also provides a mechanism for rewarding vehicles that contributing to the cloud. Privacy preserving message dissemination in VANETs: Sometimes, it is sufficient to ensure the anonymity of the vehicles disseminating messages in VANETs. We present a privacy preserving message dissemination protocol for VANETs

Highly Variable Genomic Landscape of Endogenous Retroviruses in the C57BL/6J Inbred Strain, Depending on Individual Mouse, Gender, Organ Type, and Organ Location.

Transposable repetitive elements, named the "TREome," represent ~40% of the mouse genome. We postulate that the germ line genome undergoes temporal and spatial diversification into somatic genomes in conjunction with the TREome activity. C57BL/6J inbred mice were subjected to genomic landscape analyses using a TREome probe from murine leukemia virus-type endogenous retroviruses (MLV-ERVs). None shared the same MLV-ERV landscape within each comparison group: (1) sperm and 18 tissues from one mouse, (2) six brain compartments from two females, (3) spleen and thymus samples from four age groups, (4) three spatial tissue sets from two females, and (5) kidney and liver samples from three females and three males. Interestingly, males had more genomic MLV-ERV copies than females; moreover, only in the males, the kidneys had higher MLV-ERV copies than the livers. Perhaps, the mouse-, gender-, and tissue/cell-dependent MLV-ERV landscapes are linked to the individual-specific and dynamic phenotypes of the C57BL/6J inbred population

A novel human glucocorticoid receptor SNP results in increased transactivation potential.

Glucocorticoids are one of the most widely used therapeutics in the treatment of a variety of inflammatory disorders. However, it is known that there are variable patient responses to glucocorticoid treatment; there are responders and non-responders, or those that need higher dosages. Polymorphisms in the glucocorticoid receptor (GR) have been implicated in this variability. In this study, ninety-seven volunteers were surveyed for polymorphisms in the human GR-alpha (hGRα), the accepted biologically active reference isoform. One isoform identified in our survey, named hGR DL-2, had four single nucleotide polymorphisms (SNPs), one synonymous and three non-synonymous, and a four base pair deletion resulting in a frame shift and early termination to produce a 743 amino acid putative protein. hGR DL-2 had a decrease in transactivation potential of more than 90%. Upon further analysis of the individual SNPs and deletion, one SNP, A829G, which results in a lysine to glutamic acid amino acid change at position 277, was found to increase the transactivation potential of hGR more than eight times the full-length reference. Furthermore, the hGRα-A829G isoform had a differential hyperactive response to various exogenous steroids. Increasing our knowledge as to how various SNPs affect hGR activity may help in understanding the unpredictable patient response to steroid treatment, and is a step towards personalizing patient care

Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship Between Obesity and Walking Patterns

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health

Experimental Analysis of Calculation of Fuel Consumption Rate by On-Road Mileage in a 2.0 L Gasoline-Fueled Passenger Vehicle

The five-driving test mode is vehicle driving cycles made by the Environment Protection Association (EPA) in the United States of America (U.S.A.) to fully reflect actual driving environments. Recently, fuel consumption value calculated from the adjusted fuel consumption formula has been more effective in reducing the difference from that experienced in real-world driving conditions, than the official fuel efficiency equation used in the past that only considered the driving environment included in FTP and HWFET cycles. There are many factors that bring about divergence between official fuel consumption and that experienced by drivers, such as driving pattern behavior, accumulated mileage, driving environment, and traffic conditions. In this study, we focused on the factor of causing change of fuel efficiency value, calculated according to how many environmental conditions that appear on the real-road are considered, in producing the fuel consumption formula, and that of the vehicle’s accumulated mileage in a 2.0 L gasoline-fueled vehicle. So, the goals of this research are divided into four major areas to investigate divergence in fuel efficiency obtained from different equations, and what factors and how much CO2 and CO emissions that are closely correlated to fuel efficiency change, depending on the cumulative mileage of the vehicle. First, the fuel consumption value calculated from the non-adjusted formula, was compared with that calculated from the corrected fuel consumption formula. Also, how much CO2 concentration levels change as measured during each of the three driving cycles was analyzed as the vehicle ages. In addition, since the US06 driving cycle is divided into city mode and highway mode, how much CO2 and CO production levels change as the engine ages during acceleration periods in each mode was investigated. Finally, the empirical formula was constructed using fuel economy values obtained when the test vehicle reached 6500 km, 15,000 km, and 30,000 km cumulative mileage, to predict how much fuel consumption of city and highway would worsen, when mileage of the vehicle is increased further. When cumulative mileage values set in this study were reached, experiments were performed by placing the vehicle on a chassis dynamometer, in compliance with the carbon balance method. A key result of this study is that fuel economy is affected by various fuel consumption formula, as well as by aging of the engine. In particular, with aging aspects, the effect of an aging engine on fuel efficiency is insignificant, depending on the load and driving situation

SSKM: Scalable and Secure Key Management Scheme for Group Signature Based Authentication and CRL in VANET

The security in vehicular ad hoc networks (VANETs) has become a large consideration in safeguarding growing applications and intelligent transport systems. A group signature, a popular authentication approach for VANETs, can be implemented to protect vehicular communications against malicious users. However, the issue of securely distributing group keys to fast-moving vehicular nodes arises. The growing size of the certificate revocation list (CRL) has provided the corresponding complication in its management and distribution in VANETs. In this paper, an efficient key management protocol for group signature based authentication is proposed. A group is extended to a domain with various roadside units forming a hierarchical topology. Our proposed scheme provides a secure method to deliver group keys to vehicular nodes, ensuring the security requirements. Similarly, through utilizing the two Bloom filters in our hierarchical topology, an efficient and scalable vehicle revocation mechanism can be achieved that can minimize the CRL size. Our experiment results demonstrate a scalable, efficient, and secure key distribution scheme in vehicular networking. Moreover, an effective CRL management mechanism can be accomplished using the hierarchical topology

SSKM: Scalable and secure key management scheme for group signature based authentication and CRL in VANET

The security in vehicular ad hoc networks (VANETs) has become a large consideration in safeguarding growing applications and intelligent transport systems. A group signature, a popular authentication approach for VANETs, can be implemented to protect vehicular communications against malicious users. However, the issue of securely distributing group keys to fast-moving vehicular nodes arises. The growing size of the certificate revocation list (CRL) has provided the corresponding complication in its management and distribution in VANETs. In this paper, an efficient key management protocol for group signature based authentication is proposed. A group is extended to a domain with various roadside units forming a hierarchical topology. Our proposed scheme provides a secure method to deliver group keys to vehicular nodes, ensuring the security requirements. Similarly, through utilizing the two Bloom filters in our hierarchical topology, an efficient and scalable vehicle revocation mechanism can be achieved that can minimize the CRL size. Our experiment results demonstrate a scalable, efficient, and secure key distribution scheme in vehicular networking. Moreover, an effective CRL management mechanism can be accomplished using the hierarchical topology

Multimodal Frame-Scoring Transformer for Video Summarization

As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator and multimodal feature extractors. To address these two problems, this paper proposes the Multimodal Frame-Scoring Transformer (MFST) framework exploiting visual, text and audio features and scoring a video with respect to frames. Our MFST framework first extracts each modality features (visual-text-audio) using pretrained encoders. Then, MFST trains the multimodal frame-scoring transformer that uses video-text-audio representations as inputs and predicts frame-level scores. Our extensive experiments with previous models and ablation studies on TVSum and SumMe datasets demonstrate the effectiveness and superiority of our proposed method

CONTVERB: Continuous Virtual Emotion Recognition Using Replaceable Barriers for Intelligent Emotion-Based IoT Services and Applications

International audienc