Article: AI-driven communication networks for real-time sports analytics and fan engagement in edge-IoT environments Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.53 pp.66 - 87 Abstract: Artificial intelligence (AI), edge computing, and the internet of things are all helping to make real-time analytics and immersive fan interaction possible in the sports world. However, typical cloud-based sports communication networks have too much latency, too much bandwidth congestion, and limited scalability, which makes real-time sports analytics and interactive fan experiences difficult. This paper presents an AI-driven EdgeIoT sports communication framework (AIESCF) for the intelligent processing of sports data, utilising edge-based deep learning inference, adaptive bandwidth-aware communication protocols, and distributed IoT sensing infrastructures. It uses spatiotemporal event recognition models, edge-level data filtering, and AI-assisted communication optimisation to find events and look at how well players are doing without putting too much strain on the network. The system has an accuracy of 90%, a latency of 85 ms, a bandwidth optimisation of 55%, and an engagement rate of 87%. Results demonstrate scalable efficient architecture deployment. Inderscience Publishers - linking academia, business and industry through research

Title: AI-driven communication networks for real-time sports analytics and fan engagement in edge-IoT environments

Authors: Qiankun Li

Addresses: School of Journalism and Communication, Chengdu Sport University, Chengdu, Sichuan Province, China

Abstract: Artificial intelligence (AI), edge computing, and the internet of things are all helping to make real-time analytics and immersive fan interaction possible in the sports world. However, typical cloud-based sports communication networks have too much latency, too much bandwidth congestion, and limited scalability, which makes real-time sports analytics and interactive fan experiences difficult. This paper presents an AI-driven EdgeIoT sports communication framework (AIESCF) for the intelligent processing of sports data, utilising edge-based deep learning inference, adaptive bandwidth-aware communication protocols, and distributed IoT sensing infrastructures. It uses spatiotemporal event recognition models, edge-level data filtering, and AI-assisted communication optimisation to find events and look at how well players are doing without putting too much strain on the network. The system has an accuracy of 90%, a latency of 85 ms, a bandwidth optimisation of 55%, and an engagement rate of 87%. Results demonstrate scalable efficient architecture deployment.

Keywords: AI-driven networks; Edge-IoT; sports analytics; fan engagement; real-time communication.

DOI: 10.1504/IJICT.2026.153717

International Journal of Information and Communication Technology, 2026 Vol.27 No.53, pp.66 - 87

Received: 12 Feb 2026
Accepted: 18 Mar 2026
Published online: 21 May 2026 *

Title: AI-driven communication networks for real-time sports analytics and fan engagement in edge-IoT environments

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