ABSTRACT
Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.
ABSTRACT
The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.
ABSTRACT
Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The Distributed Denial of Service (DDoS) and Distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research. © 2023 IEEE.
ABSTRACT
Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.
ABSTRACT
Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In this COVID-19 pandemic situation, health care is on the priority of every human being. The recent development in the miniaturization of intelligent devices has opened many opportunities and played a crucial role in the healthcare industry. The amalgamation of wireless sensor network and Internet of Things is the best example of wireless body area network. These tiny sensor devices have two essential evaluation parameters named as energy efficiency and stability while performing in a group. This paper focuses on various issues of the healthcare system and their solutions. An energy-efficient routing protocol that can provide sensed data to the collection centre or data hub for further processing and treatment of the patients is proposed. Here, we fixed zones for sending data to zone head using distance aware routing, and then zone head send the aggregated data to the data hub. It is better than the low energy adaptive clustering hierarchy (LEACH) by 42% and distance-based residual energy-efficient protocol (DREEP) by 30% in energy efficiency and stability 58% more by LEACH and 39% by DREEP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node.With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RGMCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57\muJ. © 2022 IEEE.
ABSTRACT
The Covid-19 pandemic caused a lot of dramatic changes to the international education system. Regardless of the advantages of the online-based education system, the conventional on-campus education system has a lot of benefits that cannot be ignored, especially for lab-based courses. The main goal of this proposed paper is to either mitigate or eliminate the hazards of the Covid-19 virus's spreading between students and staff, so they can attend on-campus events and activities safely. In this paper, we proposed an Arduino-based solution to automate the detection of the Covid-19 symptoms of students and staff within a campus. The proposed solution consists of two separate sub-systems. The first sub-system is the Covid-19 Symptoms Detection System (Covid-19 SDS), which is responsible for detecting Covid-19 symptoms by measuring the temperature and heart rate. The second sub-system is the Campus Authenticator Sign-on System (CASS), which is responsible for checking whether the person is authorized to have access or not to the campus. We used Wireless Sensor Network (WSN) and Near Field Communication (NFC) for the data exchange between the nodes. The experiments showed an acceptable reading precision with an error margin of around ± 1.35. © 2022 IEEE.
ABSTRACT
Significant topic of interest in many European countries is monitoring the air pollution, especially particulate matter (PM) concentrations, mostly because of its harmful effects on the human health. Measurement of the particulate matter concentrations can be done in a different ways, one of the possible solutions is by using low-cost and energy-efficient monitoring system using sensor network. The main goal of this paper is to analyze the influence of the green areas on particulate matter mitigation, analyzing the period of pandemic COVID-19 restrictions. The paper analyze the connection among the impact of the location of the sensor nodes and green areas and other objects to the particulate matter concentrations using various statistical tools and hypothesis testing. The tests are based on the data collected during summer 2020 at the technical campus of the Ss Cyril and Methodius University. This is the period when the World Health Organization (WHO) declared COVID-19 pandemic, and the universities were closed. In this research it can be confirmed that green areas at the Faculty pacio, reduced traffic vehicles and not having presence of the faculty staff in this period have a high impact in the reduction of particulate matter. © 2022 IEEE.
ABSTRACT
The main purpose of this study is to track down corona virus interactions using the Internet of Things. The sickness is reported to be very contagious when it comes into touch with sick people. High fever, cough, and trouble breathing are the most common signs of COVID19. They've demonstrated how the sickness has evolved to conceal its signs. Because this sickness is highly contagious, it has the potential to spread rapidly, killing thousands of people. And the transmission chain must be identified as a top concern. The Internet of Things are collection that work together to accomplish a goal. Every object has its own identity, which will be used to record main Occurrences serve as a springboard for future learning and judgments. In the medical industry, IoT plays an indisputable role in disease identification and surveillance. A new epidemic is spreading across the globe. Amid a slew of other life-threatening illnesses Despite tight lockdown procedures, COVID-19, a respiratory syndrome virus discovered in 2019, is now posing a significant threat to countries. Conclusions - The authors of this study created a design for an IoT system that collects data from individuals via sensors and sends it to clinicians via mobile phones, computers, and other devices to predict the Covid-19 sickness. The main goal is to predict COVID-19 so that early health surveillance may be provided. Therefore, the writers are able to distinguish between the two. © 2022 IEEE.
ABSTRACT
Wireless Body Area Network (WBAN) is a wireless sensor network composed of sensors implanted under the skin or wearable sensors. These sensors are small and battery powered, making power efficiency an important and critical consideration. Data transmission is one of the most power consuming functions in the sensor node. This paper analyzes reducing data transmission, and hence power consumption, by predicting vital signs data instead of transmitting them all the time. We have focused on predicting the body vital signs like the temperature from other vital signs like the heart rate and the respiration rate. It is shown that the percentage of energy reduction depends on the rate of the prediction. Also, sending critical data in the alternating modes consumes more energy compared with the critical and the alternative prediction modes. It is shown that the critical alternating and critical transmission modes consumes more energy in Covid-19 patient compared to healthy person with MAE does not exceed 0.24. Finally, the multivariant model shows a great advantage in accuracy over univariant model. © 2022 IEEE.
ABSTRACT
During the Covid-19 pandemic, teaching and learning activities were carried out virtually. It has been running for more than one year. When the trend of Covid-19 cases decreased, onsite learning began to be trialed by implementing strict health protocols. One of the important parameters for the first screening is body temperature because 99% of Covid-19 patients have fever. Therefore, a student temperature measurement mechanism is needed before entering the school area. A number of temperature detectors should be located to prevent queues. A distributed real-time monitoring system as well as data records are required for daily evaluations. Therefore, in this study, a distributed system for measuring body temperature was designed and implemented with data recording. This system runs online real-time on an internet network client server application. This system consists of four temperature detectors connected to a mini-computer as data control and an access point to a dedicated network. All sensor nodes can send data simultaneously. A web server application is provided for data storage and access to the client. From testing the proposed system, it is known that the system can send real-time data with a delay of <150 ms on several measurements and other measurements >150 ms because it really depends on the quality of internet service. The application can run an alarm function if it finds a temperature exceeding the threshold. This system has been implemented in one of a private school in the city of Bandung. With this system, it is hoped that it can support onsite learning activities in schools. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In this era of technological revolution, we are familiar with many scientific terminologies and gadgets. Today, internet is the backbone of the whole world as internet connectivity plays a vital role in our routine life and makes our life much easier. Wireless sensors are implemented in many applications like agricultural sector, military, home automation and health care sector. These wireless sensors are easy to operate and handle. Their performance varies according to the application. By connecting internet with these smart wireless sensors they act like Internet of Things. In the present scenario, whole world is suffering from Covid-19 pandemic. This is very strenuous situation for mankind. It is enigmatic to recognize a person with Covid-19 symptoms. For the identification of affected patient, some models are coined with the aid of wireless sensors and internet of things. The principle goal of this survey is to demonstrate the critical role of wireless sensor networks with internet of things for Covid-19 health care purposes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Technological developments are now growing faster, one of which is IoT technology that can facilitate human activities. In the conditions of the Covid-19 pandemic, an innovation is needed that can take advantage of technological developments to reduce physical contact in order to slow the spread of the corona virus. This research aims to create a monitoring system for infusion volume in patients who are monitored through the website. The detected sine volume will be displayed in units of weight, namely grams. The data obtained from the sensor nodes will be sent to the MySQL database via wireless communication and displayed on the website. The error result obtained from the infusion weight test is 0.9% which indicates the system can detect the weight well. Data from sensor nodes sent to the website has an error of 0.35%. When the infusion rate is less than 80 grams, a notification will appear and the system is 100% able to display a notification when it reaches that state. © 2021 IEEE.
ABSTRACT
During this pandemic time while finding the (SARS-CoV-2) infected person many of the applications got active where various nations participated actively. The main device involved in the whole process is Smartphone. The existing applications are focusing on the use of Bluetooth technology. Bluetooth is limited with the area it can cover and noise it produces to broadcast the messages to the neighbors. Also, while searching the position of the infected person one concern could be that is Position of the smartphone accurate? or for how long the tracing will happen? While tracing the position of the person whether infected or not infected, the compromise cannot be done. At the pandemic situation, the little mistake of the position will cost the life of a person and the growing number of infected persons will yield to an exponential cost. Also, the life of the smartphone to keep working needs energy through battery. Continuous localization will need continuous flow of the energy for that device. Thus, the smartphone needs to be charged after a period of time. So, when a person is in a public place, he will need his smartphone to be active. Our main concern with the whole paper is to find the solution through the simulation for the position accuracy of the smartphone as well as to manage the energy consumption. © 2022 IEEE.
ABSTRACT
The increase in the frequency of Covid-19 is the latest concern to world health, and there is a vast quantity of unimaginably horrific Covid-19 disinformation spreading online. The proposed paper measures vital signs like heart rate, temperature and blood pressure without direct contact with patients. It is very important, to maintain contactless visits to avoid the spread. As a result, it is easier to keep track of the needed parameters. Brought the notion of telemedicine or e-medicine. Wireless rechargeable sensor networks have widely utilised wireless power transfer systems to transfer energy (WRSNs). These methods try to extend the life of a network by transferring electricity to end devices. The incurred charging delay to replenish the sensor nodes is considered one of the major issues in wireless sensor networks using these wireless techniques (WSNs). This sensor for the parameter exchangers regulates the body temperature, which is monitored using a simple device, the vital priority is given to tracking the heart rate and respiratory rate of the patients. Patient data are monitored from the doctor's side;our proposed system is also provided to show the same with nearby Covid-19 patient's identity for a self-safeguard. The main objective of our work is to self-guard unaffected and also the Covid-19 patients, with the help of a proposed system, and also share the details with the doctor. © 2022 IEEE.
ABSTRACT
This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.
ABSTRACT
Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Cluster-based hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs. The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy (LEACH), which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation. This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process, thus increasing the network lifespan. This paper proposes the LEACH-CHIO, a centralized cluster-based energy-aware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO) algorithm. CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process. LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios, which consist of a variable number of nodes ranging from 20 to 100. To evaluate the algorithm performances, three evaluation indicators have been examined, namely, power consumption, number of live nodes, and number of incoming packets. The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators. © 2022 Tech Science Press. All rights reserved.
ABSTRACT
Due to the COVID-19 virus infections that have occurred recently, the development of an intelligent healthcare protocol that considers emergent heart cases becomes indispensable. This protocol is based on the method that aims to monitor patients remotely by using Internet of Thing (IoT) devices, which do not select the nodes that are nearby the patient's or in the room to choose as a Clusters Head (CH). So on, the energy consumption of these devices will be reduced, because of their highest importance than the other non-medical ones. Accordingly, this paper proposes a method called High Importance Healthcare-Internet of Things (HIHC-IoT), which is suitable for the emergent healthcare conditions of the patient and the caregiver. Furthermore, WSNs have some issues that reduce system performance, such as resource limits for sensors that may affect power supply, memory, communication capacity, and processing units. In the proposed work, the optimum set of CHs has been selected depending on the residual energy, the distance between the nodes, and the HI nodes. In addition, cloud technology, SDN architecture, and an efficient intelligent algorithm called High Importance-Future Search Algorithm (HI-FSA) have been used. Finally, the compered result of normal protocols with the proposed intelligent protocol, showed an increase in network life by about 40% and about 22% for an optimized routing protocol and increasing the number of packets delivered between nodes. © 2021 IEEE.
ABSTRACT
The World Health Organization (WHO) describes COVID-19 as a pandemic that is causing a worldwide health disaster. Wearing a face mask in public places is the most effective method to curb the spread of the virus. The Internet of Things is emerging as one of the most significant innovations and playing a vital role during the pandemic. Affordable remote health monitoring devices help doctors to track quarantined patients. Our government is trying its best to control the spread of the virus. Citizens who do not follow the protocols serve as the reason for these widespread infections. Our work proposes a system to identify protocol violators in real-time. Our system consists of a face mask detection module, a social distance monitoring module, and a non-contact temperature monitoring module. We intend to deploy our proposed approach in public places such as airports, schools, and hospitals. These modules depend on video feeds from general security cameras and IoT sensor nodes deployed around public areas. Health care and security officials can subscribe to real-time data feeds to track public behaviour. © 2021 IEEE.