https://www.techscience.com/cmc/v66n1/40468/pdf

FogMed: a Fog-based Framework for Disease Prognosis Based Medical Sensor Data Streams

We are traversing the growing emerging technology paradigms in today’s advanced technological world. In this present era, the Internet of Things (IoT) is extensively used in all sectors. IoT is the ecosystem of smart devices which contains sensors, smart objects, networking, and processing units. These integrated devices provide better services to the end user. IoT is impacting our environment and is becoming one of the most popular technologies. The leading use of IoT in human life is to track activities anywhere at any time. The utmost utilities achieved by IoT applications are decision-making and monitoring for efficient and effective management. In this paper, an extensive literature review on IoT has been done using the systematic literature review (SLR) technique. The main focus areas include commercial, environmental, healthcare, industrial, and smart cities. The issues related to the IoT are also discussed in detail. The purpose of this review is to identify the major areas of applications, different popular architectures, and their challenges. The various IoT applications are compared in accordance with technical features such as quality of service and environmental evaluation. This study can be utilized by the researchers to understand the concept of IoT and provides a roadmap to develop strategies for their future research work.

/pdf/dimensions-of-internet-of-things-technological-taxonomy-3dv7om3p.pdf

Dimensions of Internet of Things: Technological Taxonomy Architecture Applications and Open Challenges—A Systematic Review

With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A user's Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the user's ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90 % identification accuracy for a user, we can just take five minutes to collect the user's ECG data (around 500 heartbeat samples). 

PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System

With the development of Internet of Medical Things, massive healthcare sensor data (HSD) are transmitted in the Internet, which faces various security problems. Healthcare data are sensitive and important for patients. Automatic classification of HSD has significant value for protecting the privacy of patients. Recently, the edge computing-based federated learning has brought new opportunities and challenges. It is difficult to develop a lightweight HSD classification system for edge computing. In particular, the classification system should consider the dynamic characteristics of HSD, e.g., the change of data distributions and the appearance of initially unknown classes. To solve these problems, the paper proposes a scalable and transferable classification system, called SCALT. It is a one-classifier-per-class system based on federated learning. It comprises a one-dimensional convolution-based network for feature extraction, and an individual mini-classifier for each class. It is easy to be scaled when new class appears since only a mini-classifier will be trained. The feature extractor is updated only when it is transferred to a new task. SCALT has a parameter protection mechanism, which can avoid catastrophic forgetting in sequential HSD classification tasks. We conduct comprehensive experiments to evaluate SCALT on three different physiological signal datasets: Electrocardiogram, Electroencephalogram and Photoplethysmograph. The accuracies on the three datasets are 98.65%, 91.10% and 89.93% respectively, which are higher than the compared state-of-the-art works. At last, an application of applying SCALT to protect the privacy of patients is presented. 

A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data

https://www.techscience.com/cmc/v70n2/44610/pdf

Efficient Resource Allocation in Fog Computing Using QTCS Model

With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

/pdf/study-qos-optimization-and-energy-saving-techniques-in-cloud-55t8nq80cw.pdf

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Building a quality service-based system (SBS) is one of the most important research topics in software engineering. Many studies investigate intelligent methods to simplify the process of building SBSs. In particular, some keyword-based SBS building methods allow service users to automatically build an SBS by only providing a few of keywords. This type of work usually constructs a directed weighted graph of a service repository. A set of minimum-weight group Steiner trees (MSTs) is extracted from the graph to represent the service functions and their relations. However, to the best of our knowledge, none of the existing keyword-based SBS building methods allow the relaxation of the function requirements for a user. A relaxed SBS may achieve a comparable functionality versus a complete SBS containing all the query functions. To fill in the above gap, we define a new problem: a bounded skyline SBS building problem, whose solution is more adaptive and less limited than the traditional keyword-based SBS building methods. To solve this problem, we propose two algorithms based on skyline query, dynamic programming, and lower bound pruning. In the experiments, we collect real-world datasets and label the nodes with keywords. We conduct a comprehensive study to demonstrate the time efficiency of our algorithms on automatically finding SBSs. We make the annotated real-world datasets and our source code open to peer researchers.

Automatically Building Service-Based Systems With Function Relaxation

Study QoS-aware Fog Computing for Disease Diagnosis and Prognosis

Healthcare time series classification is to classify the collected human physiological information based on artificial intelligence technologies. The main purpose is to use pattern recognition technology to enable machines to analyze characteristics of human physiological signals based on deep learning in electronic health (E-health) industry 4.0. Healthcare time series classification can analyze various physiological information of the human body, make correct disease treatments, and reduce medical costs. In this paper, we propose a multiple-head convolutional LSTM (MCL) model for healthcare time series classification. MCL is a convolutional LSTM (ConvLSTM) model with multiple heads. It can extract both time and spatial features of healthcare data and increase the number of features to achieve more accurate classification results.

A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0

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