{"id":1088,"date":"2026-03-15T14:26:49","date_gmt":"2026-03-15T06:26:49","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=1088"},"modified":"2026-04-09T21:36:07","modified_gmt":"2026-04-09T13:36:07","slug":"secure-and-distributed-edge-intelligence-for-consumer-grade-smart-agriculture","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=secure-and-distributed-edge-intelligence-for-consumer-grade-smart-agriculture","title":{"rendered":"Secure and Distributed Edge Intelligence for Consumer- Grade Smart Agriculture"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 31<\/a><\/p>\n<\/div>\n\n\n\n

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Jiangtao Deng1,2<\/sup>, Shuya Zhao3<\/sup>, Jijing Cai3<\/sup>, Yuchao Xia3<\/sup>, Meilei Lv2<\/sup><\/i><\/a>, Kai Fang3<\/sup><\/i><\/a>, Hailin Feng3<\/sup>, and Thippa Reddy Gadekallu3,4<\/sup><\/p>\n\n\n\n

1<\/sup>College of Automation, Hangzhou Dianzi University, Hangzhou, China<\/p>\n\n\n\n

2<\/sup>College of Electrical and Information Engineering, Quzhou University, Quzhou, China<\/p>\n\n\n\n

3<\/sup>College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China<\/p>\n\n\n\n

4<\/sup>Division of Research and Development, Lovely Professional University, Phagwara, India<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 23, 2025
Accepted:\u00a0January 26, 2026
Publication Date:\u00a0March 15, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

A layered view of the Agri-AIoT cybersecurity landscape, mapping prominent threats to their targeted system components and corresponding defensive strategies discussed in this paper.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202608_31.033<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Consumer-grade Agricultural AIoT (Agri-AIoT) systems increasingly rely on cloud-based intelligence, which introduces latency, privacy, and connectivity limitations. These limitations are particularly severe for hetero geneous consumer devices operating under strict cost, energy, and computational constraints. This review advocates a shift from cloud-centric architectures toward distributed, edge-centric intelligence. We examine lightweight model compression techniques, including pruning, quantization, and knowledge distillation, that enable real-time and on-device decision-making. We further analyze security threats such as data poisoning and adversarial attacks that arise in decentralized agricultural systems. Privacy-preserving learning mechanisms, including Federated Learning, are discussed as key enablers of collaborative intelligence without raw data sharing. By integrating lightweight Artificial Intelligence techniques with AI-native networking principles, this paper provides a unified perspective on distributed intelligence in consumer Agri-AIoT ecosystems. We conclude that the convergence of these approaches is essential for building sustainable, secure, and self-adaptive consumer-grade agricultural electronics.<\/p>\n\n\n\n

Keywords:\u00a0AI-Native Networking; Resource-Constrained Devices; Agri-AIoT; Lightweight AI<\/em><\/p>\n\n\n\n

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