A Review of Machine Learning Algorithms on IoT Applications

Kiran, Siripuri; Kumar, Udaya; Kumar, T. Mahesh

doi:10.1109/icosec49089.2020.9215430

Cited by 35 publications

(3 citation statements)

References 8 publications

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“…For example, machine learning algorithms may be implemented at IoT gateways or at the network edge to provide real-time data processing, local decision-making, and a decreased dependency on cloud resources. Plus, it also improves scalability, dependability, and energy efficiency which are contributed by great resource allocation, adaptive routing, and self-optimizing networks [6]. This investigation aims to examine the synergistic relationship between AI and communication/networking, highlighting the transformative possibilities that arise from this powerful combination.…”

Section: The Convergence Of Communication and Networking Formentioning

confidence: 99%

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Zainuddin,

Zakirudin,

Syafiq Zulkefli

et al. 2024

IJPCC

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The confluence of Artificial Intelligence (AI) with the Internet of Things (IoT) has created new opportunities for distributed sensor networks in a variety of sectors. The potential of AI as a new paradigm for distributed sensor networks in the IoT is investigated in this review article, with an emphasis on the convergence of architecture, methodologies, platforms, sensors, devices, energy approaches, communication and networking, and applications. An analysis was conducted to examine the existing research in this field using a comprehensive literature review, revealing notable advancements, and indicating the need for further investigation. Furthermore, Moreover, suggestions and ideas are presented regarding potential innovation within the sector, with a particular emphasis on the imperative for further research and development. Our findings highlight AIoT's transformational ability in allowing more efficient and intelligent sensor networks, with implications for smart homes, healthcare, environmental monitoring, and industrial automation. The potential for harnessing the power of AI holds the key to unlocking novel opportunities and addressing the challenges inherent in dispersed sensor networks within the realm of the Internet of Things (IoT). This research advances our understanding of AIoT convergence and lays the groundwork for future breakthroughs in this interesting topic.

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Section: The Convergence Of Communication and Networking Formentioning

confidence: 99%

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Zainuddin,

Zakirudin,

Syafiq Zulkefli

et al. 2024

IJPCC

View full text Add to dashboard Cite

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“…It addressed different DL techniques to facilitate the learning and desired analytics in IoT applications. Several existing works are done based on ML for IoT in several applications [36,37,[42][43][44]. Nonetheless, the authors did not cover all ML techniques for IoT; they only focused on DL for IoT big data.…”

Section: Related Surveysmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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“…Machine learning solutions currently have universal utilization in IoT applications [1][2][3]. Specifically, machine learning helps to extract insights and knowledge from IoT data, attributes which would have been extremely difficult to obtain with other means [4].…”

Section: Introductionmentioning

confidence: 99%

IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Kelli

Argyriou

Λάγκας

et al. 2021

Sensors

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Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

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A Review of Machine Learning Algorithms on IoT Applications

Cited by 35 publications

References 8 publications

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

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