Modified deep residual network architecture deployed on serverless framework of IoT platform based on human activity recognition application

Keshavarzian, Alireza; Sharifian, Saeed; Seyedin, Sanaz

doi:10.1016/j.future.2019.06.009

Cited by 51 publications

(18 citation statements)

References 30 publications

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“…DL significantly differs from SML in that it makes it more convenient to extract and classify complex data even with data coming from heterogeneous sensor sources and modalities [16]. With DL approaches, the idea is such that raw or preprocessed data is fed to a trained DL model at one end and the classification result comes out at the other end including internal feature generation [37]- [40]. Some of the challenges for the systems based on this approach face include; the need for a lot of training data for them to be extensively usable, intra-class variations: where activity has different characteristics when performed by different users, inter-class similarities: where activities share a lot of characteristics, like jogging and running might be perceived as similar from a sensor's perspective, and data imbalances where data in one or more classes is significantly more than that of other classes.…”

Section: B Deep Learningmentioning

confidence: 99%

iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

2021

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Advances in deep learning (DL) model design have pushed the boundaries of the areas in which it can be applied. The fields with an immense availability of complex big data have been big beneficiaries of these advances. One such field is human activity recognition (HAR). HAR is a popular area of research in a connected world because internet-of-things (IoT) devices and smartphones are becoming more prevalent. A major research goal of recent research work has been to improve predictive accuracy for devices with limited computational resources. In this paper, we propose iSPLInception, a DL model motivated by the Inception-ResNet architecture from Google, that not only achieves high predictive accuracy but also uses fewer device resources. We evaluate the proposed model's performance on four public HAR datasets from the University of California, Irvine (UCI) machine learning repository. The proposed model's performance is compared to that of existing DL architectures that have been proposed in the recent past to solve the HAR problem. The proposed model outperforms these approaches on several metrics of accuracy, crossentropy loss, and F 1 score on all the four datasets. The performance of the proposed iSPLInception model is validated on the UCI HAR using smartphones dataset, Opportunity activity recognition dataset, Daphnet freezing of gait dataset, and PAMAP2 physical activity monitoring dataset. The experiments and result analysis indicate that the proposed iSPLInception model achieves remarkable performance for HAR applications.

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Section: B Deep Learningmentioning

confidence: 99%

iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

2021

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“…This scheme involved trapdoor permutation for changing the status counter, makingit hard for anadversary to determine the valid status counter of the record with only the client public key; thiswas developed in [14]. A modified deepresidual network wasdesigned in [15], in which newsmooth pooling layers weredefined to leverage the performance of the model. The researchers also suggested a method to recognize human activities in an IoT cloud environment, thereby enabling users to create situations on the basis of their actions at house.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

Kathamuthu¹,

Annadurai²,

Iruthayanathan³

et al. 2022

Electronics

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The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

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“…Serverless computing runs function in a limited and short execution time, while there are some tasks might require long execution time. This does not support long execution running, since these functions are stateless, which means that if the function is paused it cannot be resumed again [11,202,234,280].…”

Section: Long-runningmentioning

confidence: 99%

Survey on serverless computing

2021

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Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in different contexts. Thus, it is essential to present research evidence that has been published in this area. In this systematic survey, 275 research papers that examined serverless computing from well-known literature databases were extensively reviewed to extract useful data. Then, the obtained data were analyzed to answer several research questions regarding state-of-the-art contributions of serverless computing, its concepts, its platforms, its usage, etc. We moreover discuss the challenges that serverless computing faces nowadays and how future research could enable its implementation and usage.

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Modified deep residual network architecture deployed on serverless framework of IoT platform based on human activity recognition application

Cited by 51 publications

References 30 publications

iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

Survey on serverless computing

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