Cognitive Internet of Vehicles and disaster management: A proposed architecture and future direction. Issue 8 (2nd May 2019)

• Record Type: Journal Article
• Authors: Arooj, Ansif; Farooq, Muhammad Shoaib; Umer, Tariq; Shan, Rafi Us
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-02
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3625
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Historically, the vehicle has just been a component of the human ambulatory system and slave to the commands of driving force. However, recent advancement in technologies such as 5G wireless systems, cloud/edge computing, machine learning, artificial intelligence, and deep learning have opened the new paradigm of the Cognitive Internet of Vehicles (CIoV). The network of heterogeneous intelligent vehicles, not only having the social interaction capabilities but also have the ability to visualize, capture, and disseminate information to dynamic cognitive engines for identifying and analyzing different patterns and prediction of optimized outcomes. From secure navigation to traffic control, transportation security for pollution control has advocated plausible applications in real‐time operations of Internet of Vehicles in humanitarian operations. Proceeding this research, this paper aimed to propose a state‐of‐the‐art architecture based on CIoV for identifying emerging capabilities of available technologies to observe, detect, and mitigate natural disasters, and to develop the CIoV ecosystem for processing the global scale data for intelligent decision making and better service provisioning in natural disaster management. Abstract : This paper presents Cognitive Internet of Vehicles (CIoV) insights and capabilities by identifying the immense potential of IOV to enhance the ability to learn, understand, and respond without human interaction and use these cognitive abilities to create sophisticated scanning and sensing algorithms and real‐time transformation over the respective edge/cloud network for disaster management and recovery.