Cognitive Intelligent Transportation System for Smart Cities

Raja, Gunasekaran; Ganapathisubramaniyan, Aishwarya; Selvakumar, Madhumitha Sri; Ayyarappan, Thiruveni; Mahadevan, K.

doi:10.1109/icoac44903.2018.8939091

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Deriving an approximate value of h(n) takes less time than obtaining the exact value. The approximate value of h(n) can be obtained by any of the three ways such as, Manhattan distance, Euclidean distance and diagonal distance [27] which are expressed in equations ( 4), ( 5) and (6).…”

Section: ) Uav-to-controller Communicationmentioning

confidence: 99%

Efficient and Secured Swarm Pattern Multi-UAV Communication

Raja

Anbalagan

Ganapathisubramaniyan

et al. 2021

IEEE Trans. Veh. Technol.

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Unmanned Aerial Vehicle (UAV) or drone, is an evolving technology in today's market with an enormous number of applications. Mini UAVs are developed in order to compensate the performance constraints imposed by larger UAVs during emergency situations. Multiple mini autonomous UAVs require communication and coordination for ubiquitous coverage and relaying during deployment. Multi-UAV coordination or swarm optimization is required for reliable connectivity among UAVs, due to its high mobility and dynamic topology. In this paper, a Secured UAV (S-UAV) model is proposed which takes the location of the UAVs as inputs to form a Wireless Mesh Network (WMN) among multiple drones with the help of a centralized controller. After WMN formation, efficient communication takes place using A* search, an intelligent algorithm that finds the shortest communication path among UAVs. Further, the S-UAV model utilizes cryptographic techniques such as Advanced Encryption Standard (AES) and Blowfish to overcome the security attacks efficiently. Simulation results show that the S-UAV model offers higher throughput, reduced power consumption and guaranteed message transmission with reduced encryption and decryption time.

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Section: ) Uav-to-controller Communicationmentioning

confidence: 99%

Efficient and Secured Swarm Pattern Multi-UAV Communication

Raja

Anbalagan

Ganapathisubramaniyan

et al. 2021

IEEE Trans. Veh. Technol.

Self Cite

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“…Marais et al (2014) devised an approach to deal with the inaccuracy of signal propagation conditions for urban users who demand accurate localization by associating GNSS data and imaging information. Raja et al (2018) proposed a cognitive intelligent transportation system (CITS) model that provides efficient channel utilization, which is the key to make any application successful in vehicular ad hoc networks. Zheng et al (2020) used an adaptation evolutionary strategy to control arterial traffic coordination for a better passage rate along one single road with several junctions.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

Zheng

et al. 2022

Front. Environ. Sci.

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The intelligent transportation system (ITS) is one of the effective solutions to the problem of urban traffic congestion, and it is also one of the important topics of smart city construction. One particular application is the traffic monitoring and flow prediction. However, there are still challenges regarding both aspects. On the one hand, the current traffic monitoring relies heavily on the single object detection method that cannot achieve accurate statistics of moving target counting and, meanwhile, has limited speed advantage; on the other hand, the existing traffic flow prediction models rarely consider different weather conditions. Therefore, the present article attempts to propose a packaged solution, which combines a new target tracking and moving vehicle counting method and an improved long short-term memory (LSTM) network for traffic flow forecast with weather conditions. More specifically, the DCN V2 convolution kernel and MultiNetV3 framework are used to replace YOLOv4’s conventional convolution kernel and backbone network to realize multi-target tracking and counting, respectively. Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow at the road junction level. This study carries out a series of experiments using the real traffic video data with a 2-month time span at a popular road junction in the downtown of Shenzhen, China. The results suggest that the proposed algorithms outperform the previous methods in terms of the 10% higher accuracy of target detection tracking and about a half reduction of traffic prediction error, when considering weather conditions.

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“…Li et al 7 presented an improved SVR model which performs outstanding accuracy for short‐term traffic flow prediction since optimal parameter combination is obtained faster and better than that in prior works. Raja et al 8 clustered the vehicles at intersection and filtered the redundant message with proposed C‐ITS model, which succeeds in reducing congestion and realizes security services to a certain extent. Boukerche et al 9 summarized both statistics‐based and machine learning‐based models in traffic flow prediction.…”

Section: Introductionmentioning

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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Cognitive Intelligent Transportation System for Smart Cities

Cited by 4 publications

References 21 publications

Efficient and Secured Swarm Pattern Multi-UAV Communication

Efficient and Secured Swarm Pattern Multi-UAV Communication

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

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

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