Learning-Based URLLC-Aware Task Offloading for Internet of Health Things

Zhou, Zhenyu; Wang, Zhao; Yu, Haijun; Liao, Haijun; Mumtaz, Shahid; Oliveira, Luís M. L.; Frascolla, Valerio

doi:10.1109/jsac.2020.3020680

Cited by 87 publications

(49 citation statements)

References 53 publications

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“…Using the edge computing infrastructure, doctors can directly obtain the data without going through remote centralized servers [36], [37]. Patients can wear IoT medical devices at any place for rapid and effective diagnosis.…”

Section: Discussionmentioning

confidence: 99%

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

et al. 2021

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Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses. The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases. CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis. In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex. To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing. First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm. The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges. Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction. A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed. With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency. The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy. The experimental results show that MF-GAN has a better denoising effect than other algorithms. Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.INDEX TERMS Internet of Things, edge computing, generative adversarial network, 3D reconstruction.

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Section: Discussionmentioning

confidence: 99%

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

et al. 2021

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“…In IIoT, an approach proposed in [126] is an approach that learns context based on energy, backlog and conflict of participating nodes. Similarly, the authors of [127] proposed learning for task offloading in low latency and ultrareliable communication scenarios. Therefore, in future, RL can greatly help in IIoT while leveraging EC.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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“…To solve the non-stochastic task offloading problem, adversarial MAB approach can be considered, where each strategy is assigned an arbitrary and unknown sequence of rewards, one for each time step, chosen from a bounded real interval. Especially, Exponential-weight algorithm for Exploration and Exploitation (Exp3) is a well-known learning algorithm for adversarial setting, and has been studied in resource provider selection problems [25]- [27]. Exp3-based online scheme has been proposed with the objective of optimizing the QoS, such as the throughput [25], energy consumption [26] and latency [27].…”

Section: B Task Offloading Algorithmsmentioning

confidence: 99%

“…Especially, Exponential-weight algorithm for Exploration and Exploitation (Exp3) is a well-known learning algorithm for adversarial setting, and has been studied in resource provider selection problems [25]- [27]. Exp3-based online scheme has been proposed with the objective of optimizing the QoS, such as the throughput [25], energy consumption [26] and latency [27]. However, the previous works fail to address mobilityinduced volatile resource availability and resource demand in an adversarial environment at the same time.…”

Section: B Task Offloading Algorithmsmentioning

confidence: 99%

Learning-Based Decentralized Offloading Decision Making in an Adversarial Environment

Cho¹,

Xiao²

2021

IEEE Trans. Veh. Technol.

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Vehicular fog computing (VFC) pushes the cloud computing capability to the distributed fog nodes at the edge of the Internet, enabling compute-intensive and latency-sensitive computing services for vehicles through task offloading. However, a heterogeneous mobility environment introduces uncertainties in terms of resource supply and demand, which are inevitable bottlenecks for the optimal offloading decision. Also, these uncertainties bring extra challenges to task offloading under the oblivious adversary attack and data privacy risks. In this article, we develop a new adversarial online learning algorithm with bandit feedback based on the adversarial multi-armed bandit theory, to enable scalable and low-complexity offloading decision making. Specifically, we focus on optimizing fog node selection with the aim of minimizing the offloading service costs in terms of delay and energy. The key is to implicitly tune the exploration bonus in the selection process and the assessment rules of the designed algorithm, taking into account volatile resource supply and demand. We theoretically prove that the input-size dependent selection rule allows to choose a suitable fog node without exploring the sub-optimal actions, and also an appropriate score patching rule allows to quickly adapt to evolving circumstances, which reduce variance and bias simultaneously, thereby achieving a better exploitation-exploration balance. Simulation results verify the effectiveness and robustness of the proposed algorithm.Index Terms-Vehicular fog computing, task offloading, online learning, adversarial multi-armed bandit.Byungjin Cho received the doctoral degree in communications engineering from Aalto University, in 2016. He is currently a postdoctoral researcher with the Department of Communications and Networking, Aalto University. His research interests include resource managements in networked systems using algorithmic decision theory.Yu Xiao Yu Xiao received the doctoral degree in computer science from Aalto University, in 2012. She is currently an assistant professor with the Department of Communications and Networking, Aalto University. Her current research interests include edge computing, wearable sensing and extended reality. She is a member of the IEEE.

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Learning-Based URLLC-Aware Task Offloading for Internet of Health Things

Cited by 87 publications

References 53 publications

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Learning-Based Decentralized Offloading Decision Making in an Adversarial Environment

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