Implementation of Smart Indoor Agriculture System and Predictive Analysis

Uddin, Md. Salah; Asaduzzaman, Md.; Farzana, Rafia; Hasan, Md. Samaun; Rahman, Mohammad Ashiqur; Allayear, Shaikh Muhammad

doi:10.1007/978-981-13-9939-8_38

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In a practical demonstration of predictive capabilities, Salah Uddin, Asaduzzaman [25] showcase the implementation of a Smart Indoor Agriculture System, emphasizing the pragmatic utilization of predictive analysis within IoT for resource management efficiency. This exemplifies how AI-driven analytics extend beyond theoretical frameworks, finding tangible application in optimizing resource utilization.…”

Section: Advancements In Data Analyticsmentioning

confidence: 99%

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2024

Preprint

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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

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Section: Advancements In Data Analyticsmentioning

confidence: 99%

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2024

Preprint

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“…The study by Mukherjee et al [46] provides an exhaustive investigation into the various applications and algorithms of big data analytics in 5G-enabled IoT and industrial IoT systems, offering a detailed taxonomy of existing analytical systems specific to IoT applications. In addition, the work presented by Uddin et al [77] focuses on the implementation of a Smart Indoor Agriculture System with predictive analysis, showcasing the practical utilization of predictive capabilities in IoT for efficient resource management [77]. Furthermore, the survey by Saadia [61] sheds light on the integration of cloud, IoT, artificial intelligence, and big data, highlighting new challenges and research issues, underscoring the complex interplay between these technologies [61].…”

Section: Advancements In Data Analyticsmentioning

confidence: 99%

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2023

Preprint

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This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

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Smart Agriculture Using Internet of Things (IoT) and Wireless Sensor Network: Problems and Prospects

Poonia

Banerjee

et al. 2021

Advances in Mechanical Engineering

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Implementation of Smart Indoor Agriculture System and Predictive Analysis

Cited by 3 publications

References 6 publications

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Smart Agriculture Using Internet of Things (IoT) and Wireless Sensor Network: Problems and Prospects

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