Abstract Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested research issue. As the IoT grows, the data and the processes will need more space. The data in cases like healthcare, smart cities, autonomous vehicles, smart agriculture, etc. needs to be analyzed and processed in real-time. Cisco refers to the dependence of edge and cloud as “The Fog”. The data can be analyzed at the fog layer to maximize data utilization. This paper presents a new Effective Prediction and Resource Allocation Methodology (EPRAM) for Fog environment, which is suitable for Healthcare applications. Resource Allocation (RA) represents a hard mission as it involves a set of various resources and fog nodes to achieve the required computations for IoT systems. EPRAM tries to achieve effective resource management in Fog environment via real-time resource allocating as well as prediction algorithm. EPRAM is composed of three main modules, namely: (i) Data Preprocessing Module (DPM), (ii) Resource Allocation Module (RAM) and (ii) Effective Prediction Module (EPM). The EPM uses the PNN to predict a target field, using one or more predictors. In order to detect the probability of the heart attack, PNN is trained using the training dataset. Then PNN will be tested using the user’s sensing data coming from the IoT layer to predict the probability of heart attack and then take the most appropriate action accordingly. The main goal of the system is to achieve a low latency while improving the Quality of Service (QoS) metrics such as (the allocation cost, the response time, bandwidth efficiency and energy consumption). Unlike other RA techniques, EPRAM employs deep Reinforcement Learning (RL) algorithm in a new manner. It also uses the PNN for the prediction algorithm. It has achieved such acceptable performance due to using deep RL and PNN. Deep RL has shown impressive promises in resource allocation. PNN generates accurate predicted target and is much faster than multilayer perceptron networks. Comparing the EPRAM with the state-of-the-art algorithms, EPRAM achieved the minimum Makespan as compared to previous LB algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). Accordingly, EPRAM is a suitable algorithm in the case of real-time systems in FC which leads to load balancing. ERAM is effective in monitoring and predicting the status of the patient accurately and quickly. 

/pdf/effective-prediction-and-resource-allocation-method-epram-in-1okum4sm.pdf

Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system

https://doi.org/10.1016/j.displa.2022.102235

ViDMASK dataset for face mask detection with social distance measurement

Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images

With the rapid development of industrialization, the environmental pollution issue is becoming increasingly serious, especially the air pollution problem. As the core of the prevention and control of air pollution, air pollution prediction plays a very significant role in human survival and development. Therefore, it is highly essential to develop an accurate air pollution prediction model for mass rallies (e.g., playground and bazaar). Recent studies have suggested that multiple air contaminants, e.g., PM2.5 and PM10, which belong to a kind of aerosol, can carry the Covid-19 virus and spread it rapidly through the atmosphere, and this dramatically increases the risk of Covid-19 infection, particularly in the crowded and enclosed environment. Nevertheless, most existing air pollution prediction methods, which rely on large amounts of historical data for modeling and assume that the crowd flows relatively slow, are difficult to apply well to predict air pollution in mass rallies. To solve the aforementioned problem and better assist the decision-makers in managing environmental risk to human beings, in this article, we come up with a novel air pollution prediction model for mass rallies. More specifically, we first propose a temporally weighting matrix to differentiate the significance of training samples in the time domain. Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies. Finally, based on the extended TSVR, we develop a multitask TSVR (MTSVR) that simultaneously considers the related tasks. Since different air contaminants are correlated with each other, all the tasks can benefit by sharing information. The results of comparison experiments demonstrate that our presented MTSVR outperforms state-of-the-art single-task learners, multitask learners, and air pollution predictors when applied for air pollution prediction in mass rallies. Particularly, when under the six-task condition, the error values of the prediction of PM2.5, PM10, and O3 obtained by our proposed method are relatively lower, outperforming the most advanced method tested by 15.2%, 6.1%, and 4.3%, and the precision values of the predicted values outperform the advanced method tested by 28.3%, 25.1%, and 24.8%.

Air Pollution Prediction in Mass Rallies With a New Temporally-Weighted Sample-Based Multitask Learner

A new PM2.5 forecasting model based on data preprocessing, reinforcement learning and gated recurrent unit network

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods’ shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

Hongyan Liu

Papers

PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning