TACTIRSO: trust aware clustering technique based on improved rat swarm optimizer for WSN-enabled intelligent transportation system

Osamy, Walid; Khedr, Ahmed M.; Vijayan, Dilna; Salim, Ahmed

doi:10.1007/s11227-022-04889-3

Cited by 10 publications

(4 citation statements)

References 55 publications

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“…varying time from 10 min to 50 min and varying malicious nodes from 2 to 10. The DFTDS is tested against TACTIRSO [33] and L2RMR [34]. The simulation parameters are presented in Table II.…”

Section: Simulation Environment and Analysis Of Resultsmentioning

confidence: 99%

“…By maintaining a sector-by-sector sequence through the pivot node, known as the sector head, the data forwarding mechanism is carried out vertically. In [33], authors presented a secure approach for choosing Cluster Heads (CHs) based on nodes' trust value called Trust Aware Clustering Technique for WSN-based Intelligent Transportation System (TACTIRSO). To effectively choose CHs, the proposed approach used the Rat Swarm Optimizer (RSO), a more modern swarm-based optimization technique.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

Rehman,

Haseeb,

Alam

et al. 2024

IEEE Sensors J.

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“…varying time from 10 min to 50 min and varying malicious nodes from 2 to 10. The DFTDS is tested against TACTIRSO [33] and L2RMR [34]. The simulation parameters are presented in Table II.…”

Section: Simulation Environment and Analysis Of Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

Rehman,

Haseeb,

Alam

et al. 2024

IEEE Sensors J.

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“…Walid at al. (2023) present a trust-aware clustering technique based on the rat swarm optimization algorithm for the secure selection of cluster heads in wireless sensor networks for intelligent transportation systems [26]. Ibrahim et al (2022) utilize the search capability of the rat swarm optimization algorithm to identify optimal cluster centers, demonstrating its effectiveness over other clustering techniques [27].…”

Section: Referencesmentioning

confidence: 99%

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Wei,

Xiao,

Wang

2024

Mathematics

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The vehicle routing problem with time windows (VRPTW) remains a formidable challenge, due to the intricate constraints of vehicle capacity and time windows. As a result, an algorithm tailored for this problem must demonstrate robust search capabilities and profound exploration abilities. Traditional methods often struggle to balance global search capabilities with computational efficiency, thus limiting their practical applicability. To address these limitations, this paper introduces a novel hybrid algorithm known as large neighborhood search with modified rat swarm optimization (LNS-MRSO). Modified rat swarm optimization (MRSO) is inspired by the foraging behavior of rat swarms and simulates the search process for optimization problems. Meanwhile, large neighborhood search (LNS) generates potential new solutions by removing and reinserting operators, incorporating a mechanism to embrace suboptimal solutions and strengthening the algorithm’s prowess in global optimization. Initial solutions are greedily generated, and five operators are devised to mimic the position updates of the rat swarm, providing rich population feedback to LNS and further enhancing algorithm performance. To validate the effectiveness of LNS-MRSO, experiments were conducted using the Solomon VRPTW benchmark test set. The results unequivocally demonstrate that LNS-MRSO achieves optimal solutions for all 39 test instances, particularly excelling on the R2 and RC2 datasets with percentage deviations improved by 5.1% and 8.8%, respectively, when compared to the best-known solutions (BKSs). Furthermore, when compared to state-of-the-art algorithms, LNS-MRSO exhibits remarkable advantages in addressing VRPTW problems with high loading capacities and lenient time windows. Additionally, applying LNS-MRSO to an unmanned concrete-mixing station further validates its practical utility and scalability.

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“…When the calculation task is completed on the server, the calculation result is much smaller than the task size. Therefore, the time required to return the task result and the corresponding energy consumption can be ignored [23]. When Xi = 0, the task Qi will be sent to MEC server j for calculation.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic Analysis System Using Deep Learning

Ayesha Asif Pailwan,

Dr. Mr. B. D. Jitkar

2024

Int Res J Adv Engg Mgt

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Due to the rapid increase in both vehicle traffic and urbanization, effective traffic control systems are now essential. This review paper integrates Intelligent Traffic Analysis Systems (ITAS) with Convolutional Neural Networks (CNNs), a deep learning technology, to provide accurate and real-time data analysis. Advanced technologies used by ITAS monitor, analyze, and reduce traffic flow. Specifically designed deep learning models for object detection and tracking are employed to recognize and monitor cars, trucks, and other relevant entities in the recorded data. Transfer learning from previously trained models is used to train the proposed CNN architecture, which is modified for traffic analysis. This approach enhances efficiency and helps avoid road traffic congestion.

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TACTIRSO: trust aware clustering technique based on improved rat swarm optimizer for WSN-enabled intelligent transportation system

Cited by 10 publications

References 55 publications

Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Intelligent Traffic Analysis System Using Deep Learning

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