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Journal ArticleDOI

An Efficient Context-Aware Vehicle Incidents Route Service Management for Intelligent Transport System

TL;DR: The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters, and provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims.
Abstract: The continuous urbanization with extensive dynamic situations on evolving cities, urban, and suburban areas, it is not feasible to categorize the navigation as fastest route, toll-free, and other variants. Metropolitan areas are more prone to traffic congestion, lane blocking, accidents, etc., due to the overcrowding and dynamic change of commuters’ arrival rates. In the metropolitan areas, most of the commuters’ use Google map to reach their desired destinations. It is quite often that route specified by navigation will not be reliable because sometimes due to the inability to update the sudden occurrence of incidents on the routes. Currently, Google map and GPS provide the time required to cover the distance and shortest route to reach the destination. The main issues with the existing Google map are it does not considers the impact of sudden occurrence of incidents, does not show the type of incidents that occurred, clearance time, and optimal routes. These issues are solved by designing an efficient context-aware vehicle incidents route service management for an intelligent transport system. The proposed system takes the context information of incidents, vehicles, weather conditions, roadside units, roads, and so on. This context information will be collected and shared with the nearby vehicles and roadside units using both mobile agents and dedicated short-range communication protocols. The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters. Also, it provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims. The proposed system is exhaustively simulated in objective modular network testbed in C++, simulation of urban mobility, and Veins with different simulation parameters. The proposed system’s simulation results reduce the travel time (7 min) compared to the without the context information system (25 min), least collision rate (0.785%) compared to the existing system, minimizes the traffic clearance time in the incident zone, and uniform distribution of vehicle traffic on the estimated routes.
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Journal ArticleDOI
TL;DR: In this paper, a comprehensive review of the road safety aspects of Advanced Driver Assistance Systems (ADAS) subsystems is presented, focusing on collision avoidance and overtaking advice (CAOA).

16 citations

Journal ArticleDOI
TL;DR: A novel edge-based AI-IoT integrated energy efficient intelligent transport system for smart cities by using distributed multi-agent system that improves the freight vehicles mileage by reducing the traffic congestion in the urban areas.
Abstract: With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

8 citations

Journal ArticleDOI
01 Oct 2022
TL;DR: A conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action is proposed.
Abstract: The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

4 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the state-of-the-art solutions that deal with such limitations is presented in this paper , where a discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives is presented.
Abstract: With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society, such as healthcare, industry, farming, and housing. Accordingly, in recent years, attention to localization has increased significantly since it is one of the main facets of a WSN. In a nutshell, localization is the process in which the position of a sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracies, complexities, and, hence, applicabilities. The localization accuracy is limited by fundamental limitations, theoretical and practical, which restrict the localization accuracy regardless of the technique employed in the localization process. In this article, we pay special attention to such limitations from theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. In addition, a discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives is presented.

4 citations