1,271 research outputs found
A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks
To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.N/
Towards video streaming in IoT environments: vehicular communication perspective
Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues
A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks
The 5G networks have the capability to provide high compatibility for the new
applications, industries, and business models. These networks can tremendously
improve the quality of life by enabling various use cases that require high
data-rate, low latency, and continuous connectivity for applications pertaining
to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of
Things (IoT). However, these applications need secure servicing as well as
resource policing for effective network formations. There have been a lot of
studies, which emphasized the security aspects of 5G networks while focusing
only on the adaptability features of these networks. However, there is a gap in
the literature which particularly needs to follow recent computing paradigms as
alternative mechanisms for the enhancement of security. To cover this, a
detailed description of the security for the 5G networks is presented in this
article along with the discussions on the evolution of osmotic and catalytic
computing-based security modules. The taxonomy on the basis of security
requirements is presented, which also includes the comparison of the existing
state-of-the-art solutions. This article also provides a security model,
"CATMOSIS", which idealizes the incorporation of security features on the basis
of catalytic and osmotic computing in the 5G networks. Finally, various
security challenges and open issues are discussed to emphasize the works to
follow in this direction of research.Comment: 34 pages, 7 tables, 7 figures, Published In 5G Enabled Secure
Wireless Networks, pp. 69-102. Springer, Cham, 201
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned
Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version
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