We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.
Dynamic nature of Vehicular Ad-hoc Networks (VANETs) and Wireless Sensor Networks (WSN) makes them hard to deal accordingly. For such dynamicity, Machine learning (ML) approaches are considered favourable. ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network. In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs. Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed. We have listed some of the ML techniques to take additional consideration of this emergent field. A summary is given for ML techniques application with their complexities to cover on open issues to kick start further research investigation. This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.
Information is inevitable when it comes to national security. The information revolution seems to hold the massive potential to strengthen national security against current and upcoming threats and cyberattacks. However, advancements in information accessibility possess innumerable complications for retaining stable national security. One of the preeminent information sources is social media which certainly raises information manipulation factors and destabilizes national security. To accomplish better national security plans, information technology can help countries to identify potential threats, share information securely, and protect mechanisms in them. Artificial Intelligence (AI) is one of the smart areas that robustly facilitates secure information handling to avoid threats and cyber-attacks. It intelligently scrutinizes information available to the public through social media and assists in refraining negative effects on national security. This research article widely focuses on four main analytical milestones; 1) Information available to the public 2) Information affecting national security 3) Risks of cyber-attacks 4) AI as paramount to national security for accomplishing competent information role. Our principal objective is to demystify information accessibilities perspectives for readers to understand the fundamentals of information accessibility and inaccessibility corresponding to national security. To support and manifest our milestones and objectives, a Systematic Literature Review (SLR) is methodologically adapted to draw suitable conclusions and develop a farsighted frame of reference. This paper concludes with AI tool based categorization and domain-specific analysis with area-based limitations to highlight current needs. Above all, this article is a thought-provoking kick-start for many naive social media users that usually avoid information-bearing elements and are victimized by cyber-attacks followed by national security compromises.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.