An enhanced whale optimization algorithm for vehicular communication networks

Kittusamy, Senthil Ragavan Valayapalayam; Elhoseny, Mohamed; Shankar, K.

doi:10.1002/dac.3953

Cited by 60 publications

(16 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) ALL ROUND CONNECTIVITY Smart city must be connected with highways. It connects all communication media in the city with high-speed bandwidth, and connects it to the entire information network [39]. At the same time, the information and data obtained from the Internet of things can be accessed to this information network in real time, which helps the government to observe the overall situation, effectively analyze and deal with problems, and carry out multi-faceted cooperative management through the network [40].…”

Section: E Characteristics Of Smart Citymentioning

confidence: 99%

Retracted: Image Target Detection Algorithm of Smart City Management Cases

2020

View full text Add to dashboard Cite

With the rapid development and wide application of Internet technology, the use of the concept of "smart city" has been concerned and promoted. It is an inevitable way to strengthen the breadth and depth of urban services, and to move forward from digital to intelligent application. The purpose of this study is to further promote the small and medium-sized cities in China to improve the "smart city" management mode and promote the harmonious development of society, which has important reference and practical significance. In this paper, in-depth analysis of the background of smart city, different image target detection algorithms are studied. The infrared image target detection algorithm suppresses the background by high pass filter, and the correlation coefficient between the features is used as the fusion weight, and the weighted feature fusion of gray, area and centroid offset is carried out. Hyperspectral image target detection algorithm extracts some comprehensive indicators from the original data, and finally realizes the optimization of the algorithm. The mean filtering algorithm can reduce the influence of noise by preprocessing the image. Hog target detection algorithm describes the edge features of the object surface in areas such as graphics and image processing, and calculates the feature vector by the tilt direction of the specific block in the image. These algorithms have their own advantages and characteristics. The experimental results show that the accuracy and recall rate of the infrared image target detection algorithm after feature fusion are higher than other algorithms, the accuracy is higher than 6.3% of the original infrared image algorithm, and the recall rate is 5.4% higher than that of the untreated infrared image algorithm. Changing the m value of the main vector dimension will affect the target detection accuracy.

show abstract

Section: E Characteristics Of Smart Citymentioning

confidence: 99%

Retracted: Image Target Detection Algorithm of Smart City Management Cases

2020

View full text Add to dashboard Cite

show abstract

“…ACO has been used to optimize node placement, routing, and power allocation in wireless networks. It has been found to be effective in solving complex optimization problems and has been shown to be a useful tool for managing the chaos of wireless network communication [9]. Artificial bee colony (ABC) is another population-based algorithm that has been applied to wireless network node optimization [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Efficient Optimization Algorithm for Information Nodes in Wireless Network Communication Chaos

Qi¹

2023

J. Appl. Data Sci.

View full text Add to dashboard Cite

Due to the poor node optimization effect of traditional mathematical model methods, an efficient node optimization algorithm based on ant colony genetic algorithm is proposed. The ant colony algorithm is a kind of bionic optimization algorithm, and its dynamics and self-similarity are very similar to the optimization principles of messy information nodes. "Chaos node efficient optimization algorithm" is dedicated to effectively aggregating various resources such as computing, storage, knowledge, communication, information, distributed around the world, serving the public, and realizing resource sharing and collaborative work. Among them, chaos nodes efficiently search for excellent problems. If the number of parent nodes and the order of the nodes are known, the ant colony genetic algorithm is used to find the largest supporting tree, so as to obtain the best node to obtain the largest number of iterations, thereby effectively optimizing the information node.

show abstract

“…It results in robust topology changes due to the result accomplished in rapid speed and concluded with prominent segmentation which depends upon efficient diameter and network intensity (Pustokhina et al, 2021). The features of VANET legitimizes the Velocity and represent that the data is generated rapidly (Valayapalayam Kittusamy et al, 2019).…”

Section: Dynamic Topology and Mobility Modellingmentioning

confidence: 99%

Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities

Aljehane

Mansour

2021

Expert Systems

View full text Add to dashboard Cite

Presently, smart city is designed to enhance the quality of life in city, fulfil the safety of the people, safe travelling, etc. Besides, big data has attracted significant attention among researchers in different fields as a large amount of data is being produced with diverse day-to-day applications. Besides, Vehicular adhoc network (VANET) is a kind of mobile adhoc network (MANET) that considers the vehicles as the nodes in a network. Since the VANET generates large amount of data, big data analytics can be used to gain meaningful understanding for improving the traffic management process such as planning, engineering, and operations. This paper designs a Big Data Analytics with Oppositional Moth Flame Optimization based Vehicular Routing Protocol for Future Smart Cities. The presented model maps the features of VANET with the attributes of the big data. In addition, oppositional moth flame optimization based vehicular routing (OMFOVR) technique is developed for VANET over the Hadoop Map Reduce standalone distributed framework. For validating the effectual performance of the proposed OMFOVR technique, a series of experiments were performed and the results are compared with the conventional NetBeans IDE platform. The experimental values showcased the betterment of the OMFOVR technique on the selection of routes over the compared methods.

show abstract

An enhanced whale optimization algorithm for vehicular communication networks

Cited by 60 publications

References 38 publications

Retracted: Image Target Detection Algorithm of Smart City Management Cases

Retracted: Image Target Detection Algorithm of Smart City Management Cases

Analysis of Efficient Optimization Algorithm for Information Nodes in Wireless Network Communication Chaos

Big data analytics with oppositional moth flame optimization based vehicular routing protocol for future smart cities

Contact Info

Product

Resources

About