On the use of artificial intelligence techniques in intelligent transportation systems

Machin, Mirialys; Sanguesa, Julio A.; Garrido, Piedad; Martínez, Francisco J.

doi:10.1109/wcncw.2018.8369029

Cited by 43 publications

(11 citation statements)

References 33 publications

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“…A combination of IoT-based WSN and AI is emerging as a solution toward improving agricultural productivity and efficiency ( Mekonnen et al, 2019 ). AI implemented in Intelligent Transportation System (ITS) based WSN for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS ( Goswami et al, 2022 ; Machin et al, 2018 ). Using AI and WSNs, we can develop a “smart pension” that can improve the quality of life for the elderly at different levels (mainly including the daily life, health care, and spiritual comfort of the elderly) ( Li, Jiang & Liu, 2021 ).…”

Section: Background Informationmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

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Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.

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Section: Background Informationmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

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“…Forest fire detection: Feedforward Neural Network (FNN) in con-junction with WSN can identify firestorms in the forest and by predicting the firestorm in the forest [29]. Intelligent Transportation System: AI-based WSNs can be integrated with Intelligent Transportation System for solving challenges in Intelligent Transportation Systems [30,31]. Health care and smart environments: AI-based WSNs can improve the quality of life for people and provide a smart environment [32][33][34][35].…”

Section: Overview: Artificial Intelligence (Ai) Methodsmentioning

confidence: 99%

Recent Advances and Future Prospects of Using AI Solutions for Security, Fault Tolerance, and QoS Challenges in WSNs

et al. 2022

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The increasing relevance and significant acceptance of Wireless Sensor Network (WSN) solutions have aided the creation of smart environments in a multitude of sectors, including the Internet of Things, and offer ubiquitous practical applications. We examine current research trends in WSN using Artificial Intelligence (AI) technologies and the potential application of these methods for WSN improvement in this study. We emphasize the security, fault detection and tolerance, and quality of service (QoS) concerns in WSN, and provide a detailed review of current research that used different AI technologies to satisfy particular WSN objectives from 2010 to 2022. Specifically, this study’s purpose is to give a current review that compares various AI methodologies in order to provide insights for tackling existing WSN difficulties. Furthermore, there has been minimal existing related work concentrating employing AI approaches to solve security, fault detection and tolerance, and quality of service (QoS) concerns associated to WSN, and our goal is to fill the gap in existing studies. The application of AI solutions for WSN is the goal of this work, and we explore all parts of it in order to meet different WSN challenges such as security, fault detection and tolerance, and QoS. This will lead to an increased understanding of current AI applications in the areas of security, fault detection and tolerance, and QoS. Secondly, we present a comprehensive study and analysis of various AI schemes utilized in WSNs, which will aid the researchers in recognizing the most widely used techniques and the merits of employing various AI solutions to tackle WSN-related challenges. Finally, a list of open research issues has been provided, together with considerable bibliographic information, which provides useful recent research trends on the topics and encourages new research directions and possibilities.

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“…19 With data from client nodes in a wireless sensor network, Thakur et al 20 studied IoT-based solutions in transport environment, which helps motorists obtain prior contextual guidance, so as to reduce congestion and avoid potential hazards. Machin et al 21 discussed artificial intelligence methods in ITSs. The results highlight the effectiveness of artificial neural networks in traffic prediction based on wireless big data.…”

Section: Intelligent Traffic Servicementioning

confidence: 99%

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

Che

Mao

et al. 2022

Int J of Intelligent Sys

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Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real-time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine-grained recommendation method for intelligent traffic services (PF-ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder-decoder long short-term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine-grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarsegrained route recommendation based on path planning

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On the use of artificial intelligence techniques in intelligent transportation systems

Cited by 43 publications

References 33 publications

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Recent Advances and Future Prospects of Using AI Solutions for Security, Fault Tolerance, and QoS Challenges in WSNs

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

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