Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review

Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems

Mobile ad hoc networks in transportation data collection and dissemination

The field of transportation is rapidly changing with new opportunities for systems solutions and emerging technologies. The global economic impact of congestion and accidents are significant. Improved means are needed to solve them. Combined with the increasing numbers of vehicles on the road, the net economic impact is measured in the many billions of dollars. Promising methodologies explored in this thesis include the use of the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANET). Interconnecting vehicles using Dedicated Short Range Communication technology (DSRC) brings many benefits. Integrating DSRC into roadway vehicles offers the promise of reducing the problems of congestion and accidents; however, it comes with risks such as loss of connectivity due to power outages as well as controlling and managing loading in such networks. Energy consumption of vehicle communication equipment is a crucial factor in high availability sensor networks. Sending critical emergency messaged through linked vehicles requires that there always be energy and communication reserves. Two algorithms are described. The first controls energy consumption to guarantee an energy reserve for sending alert signals. The second exploits Long Term Evolution (LTE) to guarantee a reliable communication path

Centralized simulated annealing for alleviating vehicular congestion in smart cities

Vehicular traffic congestion is a serious problem arising in many cities around the world, due to the increasing number of vehicles utilizing roads of a limited capacity. Often the congestion has a considerable influence on the travel time, travel distance, fuel consumption and air pollution. This paper proposes a novel dynamic centralized simulated annealing based approach for finding optimal vehicle routes using a VIKOR type of cost function. Five attributes: the average travel speed of the traffic, vehicles density, roads width, road traffic signals and the roads' length are utilized by the proposed approach to find the optimal paths. The average travel speed and vehicles density values can be obtained from the sensors deployed in smart cities and communicated to vehicles and roadside communication units via vehicular ad hoc networks. The performance of the proposed algorithm is compared with four other algorithms, over two test scenarios: Birmingham and Turin city centres. These show the proposed method improves traffic efficiency in the presence of congestion by an overall average of 24.05%, 48.88% and 36.89% in terms of travel time, fuel consumption and CO2 emission, respectively, for a test scenario from Birmingham city in the UK. Additionally, similar performance patterns are achieved for the a test with data from Turin, Italy

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

Coalition game for emergency vehicles re-routing in smart cities

Traffic jam is considered as a difficult problem to deal with in many cities around the world due to the continuously increasing number of vehicles compared to the available infrastructure. Traffic congestion significantly influences drivers travel journey, fuel consumption and air pollution. However, the most important factor has affected the delay of emergency vehicles, such as ambulances and police cars, leading to increased road deaths and significant financial losses. To reduce this problem, we propose an advanced traffic control allows rapid emergency services response in smart cities. This can be achieved through a traffic management system capable of implementing path planning in road network monitoring and driving the emergency vehicle in the best possible way to reach the hazard zone. The performance of the proposed algorithm is compared with two other algorithms over Birmingham city centre test scenarios. Simulation results show that the proposed approach improves traffic efficiency of emergency vehicles by an overall average of 21.78%, 29.32%, 32.79% and 46.77% in terms of travel time, fuel consumption, CO2emission and average speed, respectively

Centralized simulated annealing for alleviating vehicular congestion in smart cities

Vehicular traffic congestion is a serious problem arising in many cities around the world, due to the increasing number of vehicles utilizing roads of a limited capacity. Often the congestion has a considerable influence on the travel time, travel distance, fuel consumption and air pollution. This paper proposes a novel dynamic centralized simulated annealing based approach for finding optimal vehicle routes using a VIKOR type of cost function. Five attributes: the average travel speed of the traffic, vehicles density, roads width, road traffic signals and the roads' length are utilized by the proposed approach to find the optimal paths. The average travel speed and vehicles density values can be obtained from the sensors deployed in smart cities and communicated to vehicles and roadside communication units via vehicular ad hoc networks. The performance of the proposed algorithm is compared with four other algorithms, over two test scenarios: Birmingham and Turin city centres. These show the proposed method improves traffic efficiency in the presence of congestion by an overall average of 24.05%, 48.88% and 36.89% in terms of travel time, fuel consumption and CO2 emission, respectively, for a test scenario from Birmingham city in the UK. Additionally, similar performance patterns are achieved for the a test with data from Turin, Italy. Document type: Articl

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community

Context-Aware Data-Driven Intelligent Framework for Fog Infrastructures in Internet of Vehicles

Internet of Vehicles (IoV) is the evolution of VANET (Vehicular Ad-hoc Networks) and Intelligent Transportation Systems (ITS) focused on reaping the benefits of data generated by various sensors within these networks. The IoV is further empowered by a centralized cloud and distributed fog-based infrastructure. The myriad amounts of data generated by the vehicles and the environment have the potential to enable diverse services. These services can benefit from both variety and velocity of the generated data. This paper focuses on the data at the edge nodes to enable fog-based services that can be consumed by various IoV safety and non-safety applications. The paper emphasizes the challenges involved in offering the context-aware services in a IoV environment. In order to overcome these challenges, the paper proposes a data analytics framework for fog infrastructures at the fog layer of traditional IoV architecture that offers context-aware real time, near real-time and batch services at the edge of a network. Finall