Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI)
| Autor: | Omar Bin Samin, Nasir Ahmed Abdulkhader Algeelani, Ammar Bathich, Maryam Omar, Musadaq Mansoor, Amir Khan |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | PeerJ Computer Science, Vol 10, p e2276 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2376-5992 72114010 |
| DOI: | 10.7717/peerj-cs.2276 |
| Popis: | The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security. |
| Databáze: | Directory of Open Access Journals |
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