Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi, Abdussalam; Dini, Pierpaolo; Saponara, Sergio; Zheng, Qinghe

doi:10.3390/electronics12244925

Cited by 14 publications

(5 citation statements)

References 146 publications

(143 reference statements)

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“…We train the processed data into one of these models, and then the model predicts the unseen data by learning from the previous data. This technique helps us to avoid failure and losses and improve the performance [9]. For example, predicting tomorrow's whether and temperature can be done using ML for the IoT based real time applications [10].…”

Section: Data Modelmentioning

confidence: 99%

Artificial Intelligence and Internet of Things Integration Taxonomy

T R,

et al. 2024

Int. J. Res. Publ. Rev.

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The Internet's transformation into the Internet of Things (IoT) is becoming the core of real-time and advanced technology appliances nowadays in all the fields. These real-time connected devices generate a large quantity of data. Artificial Intelligence (AI) represents the new direction of computer science, encompassing many branches including machine learning (ML), deep learning (DL), natural language processing (NLP), and many more. It can be defined by the term 'automated intelligence' as it is a program that handles prediction, forecasting, and decision-making without human interference. The automated intelligent processing and analysis of this large data (Big Data) is the key to developing smart IoT applications. ML may be used in cases where the desired effect is defined (supervised learning), where data itself is not defined beforehand (unsupervised learning), or where learning is the outcome of the interaction between the learning model and the environment (reinforcement learning). In this article, we present and discuss an important taxonomy of ML algorithms that can be used with IoT applications. Furthermore, we illustrate how various ML techniques are used to derive higher-level information from the data. Finally, it provides a basic line of investigation into real-world IoT data characteristics that involve an integration of AI with IoT.

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Section: Data Modelmentioning

confidence: 99%

Artificial Intelligence and Internet of Things Integration Taxonomy

T R,

et al. 2024

Int. J. Res. Publ. Rev.

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“…In CPSs, graph learning technology has been widely employed to optimize information interaction and enhance system security. Elhanashi et al (2023) [15] reviewed the application of deep learning in the Internet of Things (IoT), highlighting the advantages of graph learning technology in managing complex network relationships. These studies provide valuable references and foundations for our methods.…”

Section: Link Predictionmentioning

confidence: 99%

“…Additionally, models such as GraphSAGE and the GCN were considered to further substantiate the advantages of our approach. For the baseline methods on the OGB, we used GraphSAGE [27], the GCN [4], and GCN + DRNL [15], all of which utilize pairwise node representations generated by GNNs for link representation. Specifically, GCN + DRNL employs the same node labeling approach as Policy-SEAL discussed in this paper.…”

Section: Comparative and Ablation Experimentsmentioning

confidence: 99%

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

Zhu,

Yang,

Qiu

et al. 2024

Electronics

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To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.

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“…Deep learning models can analyze complex medical data such as imaging scans, genetic information, and patient records to aid in the identification of diseases and conditions. Realtime processing capabilities enable the analysis of incoming data streams, facilitating rapid decision-making and timely interventions [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices

Elhanashi,

Dini,

Saponara

et al. 2024

J Real-Time Image Proc

Self Cite

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Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field.

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Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Cited by 14 publications

References 146 publications

Artificial Intelligence and Internet of Things Integration Taxonomy

Artificial Intelligence and Internet of Things Integration Taxonomy

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices

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