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A Taylor Flamingo Shark Optimization–Based Traffic Aware Content Caching Vehicular Social Networks.

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Title: A Taylor Flamingo Shark Optimization–Based Traffic Aware Content Caching Vehicular Social Networks.
Authors: D., Vedha Vinodha1 (AUTHOR) dvedha1975@gmail.com, Subramanium, Malathy2 (AUTHOR)
Source: International Journal of Communication Systems. 9/10/2025, Vol. 38 Issue 13, p1-18. 18p.
Subjects: Optimization algorithms, Routing algorithms, Content delivery networks, Traffic monitoring, Telecommunication
Abstract: Vehicular Social Networking (VSN) is an emerging and developing application of the Internet of Vehicles (IoV) that aims to integrate vehicular networks with social networks seamlessly. Nevertheless, unique vehicular network features, namely, high mobility as well as frequent communication interruptions, make content delivery to end users under strict delay constraints very problematic. The Taylor Flamingo Shark Optimization (TFSO) method is introduced to address the limitations of existing caching and routing strategies in VSN under real‐time traffic conditions. With the amalgamation of FSA and WSO with the Taylor series, TFSO provides a powerful tool for accurate, delay‐aware, and mobility‐sensitive content caching, thereby improving content delivery efficiency in VSNs. The proposed optimization‐based traffic‐aware content caching is implemented using several stages. Initially, the shortest path with the vehicular content provider is found based on the proposed hybrid Flamingo Shark Optimization (FSO). The FSO is devised by using the Flamingo Search Algorithm (FSA) as well as White Shark Optimization (WSO). Subsequently, traffic‐aware content recommendations are carried out based on conditional likelihood probability. Additionally, the vehicular distribution managed by the content provider is optimized across the network by using the proposed TFSO, which is devised using the proposed FSO along with the Taylor series concept. Moreover, the effectiveness of the developed TFSO approach is assessed by leveraging metrics including computational cost, delivery delay, and delivery rate. The computational cost recorded value is 1.057, which shows that the algorithm operates with low computational overhead; the delivery delay of TFSO is 0.611 s, which indicates that the system required less time to deliver content to end users in high‐mobility scenarios; and the delivery rate is 85.996, which reflects the high success rate of content delivery across the network using 2000 rounds with 150 vehicles. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Vehicular Social Networking (VSN) is an emerging and developing application of the Internet of Vehicles (IoV) that aims to integrate vehicular networks with social networks seamlessly. Nevertheless, unique vehicular network features, namely, high mobility as well as frequent communication interruptions, make content delivery to end users under strict delay constraints very problematic. The Taylor Flamingo Shark Optimization (TFSO) method is introduced to address the limitations of existing caching and routing strategies in VSN under real‐time traffic conditions. With the amalgamation of FSA and WSO with the Taylor series, TFSO provides a powerful tool for accurate, delay‐aware, and mobility‐sensitive content caching, thereby improving content delivery efficiency in VSNs. The proposed optimization‐based traffic‐aware content caching is implemented using several stages. Initially, the shortest path with the vehicular content provider is found based on the proposed hybrid Flamingo Shark Optimization (FSO). The FSO is devised by using the Flamingo Search Algorithm (FSA) as well as White Shark Optimization (WSO). Subsequently, traffic‐aware content recommendations are carried out based on conditional likelihood probability. Additionally, the vehicular distribution managed by the content provider is optimized across the network by using the proposed TFSO, which is devised using the proposed FSO along with the Taylor series concept. Moreover, the effectiveness of the developed TFSO approach is assessed by leveraging metrics including computational cost, delivery delay, and delivery rate. The computational cost recorded value is 1.057, which shows that the algorithm operates with low computational overhead; the delivery delay of TFSO is 0.611 s, which indicates that the system required less time to deliver content to end users in high‐mobility scenarios; and the delivery rate is 85.996, which reflects the high success rate of content delivery across the network using 2000 rounds with 150 vehicles. [ABSTRACT FROM AUTHOR]
ISSN:10745351
DOI:10.1002/dac.70188