Collaborative algorithms that combine AI with IoT towards monitoring and control system

Zhang, Tao; Zhao, Yan; Jia, Wenjing; Chen, Mu-Yen

doi:10.1016/j.future.2021.07.008

Cited by 16 publications

(3 citation statements)

References 49 publications

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“…As a result, self-adaptive AI-based algorithms are needed to aggregate, assess, and fully comprehend all program objects. Due to the rise of massive datasets and energy-hungry IoT devices, proper energy management is essential [ 149 ].…”

Section: State Of Art Meh System For Iot Devicesmentioning

confidence: 99%

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Sarker,

Riaz,

Lipu

et al. 2024

Heliyon

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Section: State Of Art Meh System For Iot Devicesmentioning

confidence: 99%

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Sarker,

Riaz,

Lipu

et al. 2024

Heliyon

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“…Artificial intelligence (AI) and internet of things (IoT) technologies were the most influential paradigms that played a vital role during the COVID-19 pandemic [ 6 ]. As the most popular set of technology for the 21st century, this integrated system can perform data informatization [ 7 ], efficient remote-control monitoring in real-time [ 8 , 9 ], and intelligent management [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Adli

Remli

Wong

et al. 2023

Sensors

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As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

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“…Security in the IoT requires additional considerations to protect connected smart devices from threats and other vulnerabilities using effective AI techniques towards monitoring [9][10][11]. In addition, IoT security systems are designed using AI enhanced encryption algorithms to archive privacy [12,13].…”

Section: Introductionmentioning

confidence: 99%

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

et al. 2022

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In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

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Collaborative algorithms that combine AI with IoT towards monitoring and control system

Cited by 16 publications

References 49 publications

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

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