Cognitive Internet of Vehicles and disaster management: A proposed architecture and future direction

Arooj, Ansif; Farooq, Muhammad Shoaib; Umer, Tariq; Shan, Rafi Us.

doi:10.1002/ett.3625

Cited by 22 publications

(15 citation statements)

References 65 publications

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“…It fundamentally aims at bridging the transportation system such as a vehicle, road infrastructure and the social world such as human demand, awareness and social behavior and so on. It also aims to enable smart network operation and optimization, resource allocation, emergency responses and intelligent service provisioning [5,7,15,16].…”

Section: A Overview Of C-iovmentioning

confidence: 99%

“…However, in the vehicular networking concept, Arooj et al [15] define CIoV as a network-based framework for intelligent vehicles, where vehicles are primarily considered a context-aware agent. It is a sensory network of road elements such as vehicles and related infrastructure to receive real-time information from the road and surrounding physical world, including humans and the environment with minimal human interaction.…”

Section: However the Question Is What Does Cognitive Refer To Here?mentioning

confidence: 99%

“…The evolved C-IoV host multiple users, multiple vehicles, multiple things and multiple networks and initiates intelligence cooperation. It offers an in-depth integration of the human-vehicle-thing-environment with services and resources, increases transportation efficiency, improves the service level of cities, and ensures safe and comfortable travel [5,15,16,18].…”

Section: However the Question Is What Does Cognitive Refer To Here?mentioning

confidence: 99%

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Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

Hasan¹,

Overall²,

Ansari³

et al. 2022

Next-Generation Enterprise Security and Governance

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The recent advancement of cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, 1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? 2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.

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Section: A Overview Of C-iovmentioning

confidence: 99%

Section: However the Question Is What Does Cognitive Refer To Here?mentioning

confidence: 99%

Section: However the Question Is What Does Cognitive Refer To Here?mentioning

confidence: 99%

See 1 more Smart Citation

Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

Hasan¹,

Overall²,

Ansari³

et al. 2022

Next-Generation Enterprise Security and Governance

View full text Add to dashboard Cite

show abstract

“…Data about active mobile phone users of the region have been collected to facilitate resource allocation during disaster management focusing on post-earthquake activities. Arooj et al (2019) proposed a framework for disaster management using smart vehicles equipped with comprehensive sensory system. Lu, Cao & Porta (2016) observed the utility of smart phones with embedded inertial sensors in the process of disaster recovery and management.…”

Section: Related Workmentioning

confidence: 99%

A framework for the prediction of earthquake using federated learning

Tehseen

Farooq

Abid

2021

PeerJ Computer Science

Self Cite

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Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

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“…In addition, the existing autonomous systems are getting more powerful and take advantage of the capabilities of several types of devices 4,5 . As a new promising way, autonomous vehicle (AV) cannot only reduce accidents but also liberate hands from tedious driving by leveraging precise sensors equipped on vehicles 6,7 . As the typical application of AV, autonomous platoon has captured a lot of attention both in academic and industries 8,9 .…”

Section: Introductionmentioning

confidence: 99%

SPSC: A secure and privacy‐preserving autonomous platoon setup and communication scheme

Lai

Zheng

2020

Trans Emerging Tel Tech

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Summary The new driving model of autonomous platoon has outlined the blueprint for developing the intelligent transportation of system since it can improve safe driving, alleviate traffic congestion and bring more comfortable experience. Vehicles in the platoon can communicate and share driving data with each other through vehicular network for cooperatively driving. Therefore, it is crucial to avoid some misbehavior vehicles which can eavesdrop or even tamper important data. In this paper, we propose a secure and privacy‐preserving autonomous platoon setup and communication scheme, called SPSC, to help vehicles establish secure communication and avoid private information leakage. Specially, considering the dynamic and temporary characteristics of the autonomous platoon, the autonomous platoon communication can be divided into two scenarios, that is, intra‐platoon communication and inter‐platoon communication. In the intra‐platoon communication, the SPSC can securely set up platoon and dynamically manage the platoon members with privacy preservation by using private set intersection and group key agreement techniques, respectively. In the inter‐platoon communication, the SPSC can also achieve secure and anonymous communication by adopting certificateless ring signcyption technique. In addition, vehicle‐out‐platoon can trace the disputed or misbehavior platoon member with the assistance of Road Transportation Authority. The security analysis shows that SPSC can provide authentication, privacy protection, confidentiality and traceability. Finally, the performance of SPSC is comprehensively evaluated by simulation to demonstrate its efficiency in terms of the computation cost.

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Cognitive Internet of Vehicles and disaster management: A proposed architecture and future direction

Cited by 22 publications

References 65 publications

Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

A framework for the prediction of earthquake using federated learning

SPSC: A secure and privacy‐preserving autonomous platoon setup and communication scheme

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