Managing Events: The IoT Way

The concept of IoT in the current world where speed, accuracy and efficiency are of a high importance, can do wonders if implemented in a structured manner, into a machine, project, hardware, idea which can improve technology. So, IoT has its application in the Events. Events can be of many types and there is a need of man power to handle the events efficiently. People gather in huge numbers if there is a political event, whereas there is limited audience in a cultural show or less people in a marriage function. Any of such events, if handled smartly, can ease the tasks of humans, as well as provide speed and accuracy and ensure proper event management and organization. This project demonstrates a hardware for the entry-exit of people for any event, through the technology of Radio Frequency Identification (RFID), Wireless Fidelity (Wi-Fi), and main heart as ESP 8266 Controller. The software simulation in Cisco Packet Tracer shows a general event organization related to a hotel or government-based area, where different sections are integrated to control and handle the event in a smart way. The use of RFID indicates the contactless operation for monitoring the attendee entry-exit, due to the current COVID-19 protocols. So, such systems are safe and smart to execute. © 2022 IEEE.

Designing for Privacy and Technology Adoption by Older Adults

Older adults (65+) are becoming primary users of emerging smart systems, especially homecare technologies. The current COVID-19 pandemic has created increased demand and pressure to speed up innovation with healthcare increasingly shifting from the clinic to the home. This acceleration in digital health has also given rise to increased potential risks related to privacy and security. This paper presents highlights of a literature review focused on privacy research involving older adults to inform research and development of home healthcare technologies as part of the National Research Council of Canada's Aging in Place Program. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Smart Health Care System for Early Detection of COVID-19 Using X-ray Scans

The novel Coronavirus spread in the world in December 2019. Millions of people are infected due to this disease. Due to viral illness, daily life routines and the economy are affected in many countries. According to a clinical study, the disease directly attacks the lungs and disturbs the respiratory system. X-ray and CT scans are the main imaging techniques to discover that disease. However, X-ray scans cost is low as comparatively CT scans. In the limited resources, deep learning plays a key role in diagnosing the COVID-19 with the help of X-ray scans. This study proposed a new transfer learning approach based on the convolutional neural network (CNN). We used the four different classes during the experimental process: COVID-19, pneumonia, lung opacity, and viral pneumonia. We also compared our proposed model with other transfer learning-based techniques. Our proposed COVID-TL model attained the best results in terms of classification. The proposed model is a beneficial tool for radiologists to get the early diagnosis results and help the patients in their early stages. © 2022 IEEE.

IoT Based System for Detecting the Number of People and their Distribution in Classroom

This paper proposes a smart system for detecting the number of people in the classroom and their distribution over the available seats. The system is based on Arduino nodes used as the Internet of Things (IoT) modules and Raspberry PI as the central unit for data collection, evaluation, and storage. The system's primary purpose is to evaluate the number of people in the classroom because of the COVID-19 restrictions and automatically check the distance between sitting people. During the system design, we put personal privacy in the first place, and therefore we do not use any cameras. © 2022 IEEE.

CoviMon - Pandemic Healthcare Monitoring System

Pandemic crisis are a serious concern among the masses as it affects their physical, social and mental lives. Thousands of healthcare workers have been put over a tremendous load for managing any tremendous outbreak of any contagious disease. Furthermore, even their lives are put at a risk when they must monitor and isolate these patients. Prevention is always better than cure. We aim to build an IOT based smart system that can help health care personals monitor the general public for factors like social distancing offender identification and mask checks. Also, some other physical characters like temperature, heart rate can be measured by our system. While first functionality can help us identify the offenders at public or even private spaces, the second system will help in getting a detailed view of actual condition of a person. All these facilities will be accessible by a web portal and analyzed for prediction of possible hotspot areas where there might be chance for infection. © 2022 IEEE.

Multi-features Extraction for Automating Covid-19 Detection from Cough Sound using Deep Neural Networks

This research paper proposed a smart system based on deep learning to detect COVID-19 patient's using the cough sound. The deep neural networks are used to distinguish between different types of cough COVID-19 positive or negative coughs. The proposed system is segmented into three stages: Audio pre-processing by noise reduction, segmentation, feature extraction, classification, and model deployment. Eight features have been extracted from 1635 sound subjects: 573 COVID-19 positive and 1062 negative coughs. The feature's extracted data have trained using two models;first model Cough detection based on ANN used to distinguish if there is cough or not, the second model to detect the covid-19 using Convolutional Neural Network. The overall accuracy for both models is 98.1% for the Cough model and 98.5% for the Covid-19 model. The models were compiled after deployment to work together as a web service based on flask. Cough model receives cough sound from the mobile app or web interface and discriminates if there is cough then passe it coivd1-9 model that will analyze if cough is positive or negative.and send the result back to the mobile app. © 2022 IEEE

Cough Sound based COVID-19 Detection with Stacked Ensemble Model

COVID-19 pandeamic has affected people all over the world. COVID-19 may manifest with different severity in different people, however, it predominantly affects respiratory system. Symptoms may vary from sore throat and cough to shortness of breath and damaged lungs. This work focusses on developing a smart system for early detection of COVID-19 based on cough sounds and machine learning algorithms. Such a system would be easily accessible and may provide initial screening for detection of COVID-19. Moreover, cough sounds may be recorded by the person on smartphone avoiding the need for visiting a hospital or testing facility and getting exposed to the disease during the pandeamic. First, the duration of cough sound is determined in the recorded audio signal using thresholding. Then, statistical features are extracted for cough sound and normalized. Finally, the performance of 10 different machine learning algorithms are compared for automatic detection of COVID-19. The proposed stacked ensemble of machine learning models yields the best performance, with an accuracy of 79.86% and area under region of convergence curve of 0.797 for cough sounds of new patients. © 2022 IEEE

Smart Home: Household Appliance usage Recommender and Monitoring System (HARMS)

The demand for energy sources such as electricity is increasing as the population is increasing, which results in high billing costs and more energy consumption. More factors are resulting from these issues. For example, the decreased awareness from residents about how to save energy, especially kids and elderly people who forget about turning off home appliances and lights when they are not needed to be on. HARMS provide a smart solution through the concept of machine learning (ML) and recommendations, it will monitor power consumption, show recommendations and control home appliances based on the resident's behaviors, when they are willing to turn on the room light or any other home appliance and when to turn them off in order to enhance energy saving. HARMS will also track the inhabitant's usual and unusual behavior to take an action. We must note that due to this exceptional situation (Covid-19 Pandemic), HARMS may be done either using actual hardware, simulation, or both. The hardware parts will consist of microcomputer, motion, light, and current transformer sensors. The software parts will consist of a control system that collects data from sensors and monitors the power consumption, a database to store the collected data, appropriate algorithms for the recommender system, and an android application to interact with the residents. Regarding the simulation will consist of a web-based application to represent the home environment and the appliances, including the control and the recommender systems. This project will experiment at the College of Computer Sciences and Information Technology (CCSIT) at King Faisal University (KFU). © 2022 IEEE.

A Novel Smart Hospital for Isolation of COVID-19 Patients

In this paper we describe a smart and innovative system which provides prime contribution to our society in this pandemic situation. We propose a novel smart hospital with multi featured equipments along with fashionable isolation ward for COVID-19 patients. A website interface has been proposed under anyone can visit and after knowing the status of the beds either for the isolation or emergency ward they can easily book from remote location. A QR code with registration number will be provided to the users. These users may reach to the hospital and have to pass through the QR code scanner for verification and sanitization process. A preevaluation section has been defined where proper mask at face will be detected with thermal scanner facility. All the designed section is controlled and monitor by control unit through server. Well featured with multiple sensors a unique ward is designed where oxygen level sensor is used to sense the level of oxygen and if the level of oxygen of patient is less than defined value supply of oxygen will switch on the supply automatically. Doctor will monitor and take care of patients through CCTV and video conferencing. The entire sub design unit is controlled and monitored via hybrid network and IoT. © 2021 IEEE.

A Smart System to Detect Cheating in the Online Exam

The COVID-19 pandemic, caused the whole globe some serious downfall in terms of economy, even the day-to-day activities are being restricted due to enforcements like lockdown. Wish made restrictions on the exam process. In this paper we aim to make a fully automated online cheat detection system capable of detecting whether a student is attempting to cheat or not during the process of the exam. This system will use deep learning techniques (AI) such as face recognition, sound detection, active-window detection. To note that the face recognition process will use a CNN based module for its high accuracy and stability. © 2021 IEEE.

Smart System for Early Detection of Severe COVID-19 Cases

This contribution describes a medical monitoring system that enables early intervention in the event that the condition of a patient suffering from COVID-19 and other diseases suddenly starts to deteriorate. It will be a modular, multimodal and mobile system, and will be suitable for use in the treatment of COVID-19 patients. By facilitating the required intervention at an early stage, the system helps to lessen the effects of disease, shorten the duration of therapy and make flexible use of intensive care wards. © VDE VERLAG GMBH · Berlin · Offenbach.

Embedded Smart Glove using Ultrasonic and Flame Sensors for Helping Visually Impaired People

Visually impaired is someone's condition of lacking visual observation due to neurological and physiological factors. Also, wildfire and coronavirus cases made the situation worst for visually impaired people. A helping tool is very needed to overcome these problems. This paper proposes a prototype of an embedded glove using ultrasonic and flame sensors to provide a convenient and safe method for visually impaired people to overcome difficulties in daily life. Smart glove system prototype based on Arduino UNO microcontroller, ultrasonic and flame sensors, SIM800L GSM module for communication, and buzzer for alarm notification. Then, the complete device prototype was tested using both the acrylic box and embedded at one's hand as a proof of concept for emphasizing the reliability and usefulness of our work. The results show that the ultrasonic sensor works well for detecting an object with a coverage of 40 cm from the blind. The fire/flame sensor also works well when using fire detection mode for detecting the small fire from matches or lighter at a distance of about 15 cm or big fire from a burnt paper at a distance of about 20 cm and will be sending a short message to the respected person through SIM800L GSM. The only drawback for this device is a bit heavy when used on hand and still lacks an IoT system. However, further research will be conducted to overcome those problems soon. © 2021 IEEE.

An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items.

In the pandemic of COVID-19, it is crucial to consider the hygiene of the edible and nonedible things as it could be dangerous for our health to consume infected things. Furthermore, everything cannot be boiled before eating as it can destroy fruits and essential minerals and proteins. So, there is a dire need for a smart device that could sanitize edible items. The Germicidal Ultraviolet C (UVC) has proved the capabilities of destroying viruses and pathogens found on the surface of any objects. Although, a few minutes exposure to the UVC can destroy or inactivate the viruses and the pathogens, few doses of UVC light may damage the proteins of edible items and can affect the fruits and vegetables. To this end, we have proposed a novel design of a device that is employed with Artificial Intelligence along with UVC to auto detect the edible items and act accordingly. This causes limited UVC doses to be applied on different items as detected by proposed model according to their permissible limit. Additionally, the device is employed with a smart architecture which leads to consistent distribution of UVC light on the complete surface of the edible items. This results in saving the health as well as nutrition of edible items.

Are smart contracts too smart for Supply Chain 4.0? A blockchain framework to mitigate challenges

Purpose: Smart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon. Design/methodology/approach: A game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes. Findings: The numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation. Research limitations/implications: This work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights. Practical implications: By using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis. Originality/value: This work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game. © 2021, Emerald Publishing Limited.

IoT-Based Horticulture Monitoring System

With climate change and global warming in mind, vertical farms, hydroponics and urban greenhouses can now be found in many cities worldwide as we transform the ways we produce food. Additionally, recent implications of the COVID-19 pandemic prove that as a society we can harness the benefit of remote monitoring and automation for controlled-environment agriculture and horticulture. The subject matter of this paper is implementation of a solar-powered, Internet of Things (IoT)-based Real-time Autonomous Horticulture Monitoring System (RAHMS). The RAHMS integrates a mobile application for viewing the greenhouse crop data and camera feed of plants, and interacts with cloud databases such as Firebase and MATLAB ThingSpeak for the scalability. In particular, a simple and distinctive design of a solar-powered, low energy consuming, and inexpensive greenhouse monitoring system is presented. The paper outlines RAHMS design methodology and showcases a proof-of-concept prototype with its core hardware and software components. The proposed system has a potential to further advance the practical aspects of the remote solutions for the cultivation and monitoring of horticulture and controlled-environment agriculture. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.