Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems

Gupta, Brij B.; Gaurav, Akshat; Cano-Marin, Enrique; Alhalabi, Wadee

doi:10.1109/tits.2022.3174333

Cited by 37 publications

(21 citation statements)

References 52 publications

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“…Table 5 shows the summary of the sequential model HDDNO methodology and gives information related to the proposed model. Table 5 gives the information related to layer type, output shape, and several parameters in different layers of the proposed model HDDNO, the total number of parameters (2,204,481), the trainable parameters (2,204,481), and nontrainable parameters (0). The results conclude that the model's performance depends on even the optimizer's selection for this data set.…”

Section: Resultsmentioning

confidence: 99%

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT‐based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO‐based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

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Section: Resultsmentioning

confidence: 99%

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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“…Therefore, publishing standard specifications and documents for using technologies such as XAI is important. Setting high standards for the usage of XAI for smart cities is crucial in a manner that will lessen vagueness and increase robustness and could assist in enhancing living/working understanding, advanced transportation [206,207], and quicker availability of enough information for informed decision making [208,209]. Furthermore, it is also essential to make the stakeholders of smart cities aware of various standards and policies for implementing XAI for smart cities applications and systems.…”

Section: Standard Specifications For Implementing Xai For Smart Citiesmentioning

confidence: 99%

A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.

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“…VANET provides a system that can revolutionise traffic services and minimise the threat of road accidents. Each vehicle that becomes part of the network also manages and controls the communication on this network [4–7]. Many types of research have been published to utilise and perform reliable connections [8–11].…”

Section: Introductionmentioning

confidence: 99%

Enhanced clustering based routing protocol in vehicular ad‐hoc networks

Naeem

Rizwan

Alsubai

et al. 2023

IET Electrical Syst in Trans

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A vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and overhead transmission.

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Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems

Cited by 37 publications

References 52 publications

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

A Survey of Explainable Artificial Intelligence for Smart Cities

Enhanced clustering based routing protocol in vehicular ad‐hoc networks

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