Bibliometric Mapping of Cloud Linked Artificial Intelligence in the Internet of Things

Abstract

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) is increasing interest in how intelligence is distributed across cloud, edge, fog, and device layers. This study maps the 2020-2026 development of cloud-linked AI-IoT research using Scopus metadata. The reported Scopus query required AI or machine-learning terms, IoT terms, and cloud-related terms in TITLE-ABS-KEY fields. The original export contained 5,620 records. The supplied analytical file contained 5,472 records, all matched to the raw export by Scopus EID; the available materials do not preserve a defensible record-level reason for the 148-record difference, so it is reported as a prior curation step rather than as duplicate removal. The analytical corpus was examined using descriptive performance indicators, supplied VOSviewer country, keyword, and source co-citation exports, and a reproducible temporal keyword analysis. Publication output increased from 413 records in 2020 to 1,166 in 2025, corresponding to a 23.1% compound annual growth rate; the 664 records for 2026 represent a partial year through 10 August. India, China, and the United States were the leading countries by document count in the supplied country network. Lecture Notes in Networks and Systems was the most productive publication source by document count, while IEEE Internet of Things Journal and IEEE Access accumulated the largest source-level citation totals in the analytical corpus. Keyword evidence shows a stable AI-IoT-cloud core alongside stronger recent visibility of edge computing, federated learning, digital twins, predictive maintenance, green computing, data privacy, sustainability, and smart agriculture. The results support an interpretation of increasing scholarly emphasis on distributed cloud-edge-device intelligence, rather than a complete replacement of cloud computing.