Lo-Ra Based Covid Patient Health Detecting System

Coronavirus is an authentic WHO-proclaimed pandemic disease. Qualified professionals and medical care organizations experienced significant conditions across the world. In addition, the governmental authority develops a significant technique to alleviate the disease, to lock the nations. In this article, we proposed the covid detection system among the people by using several sensors embedded in a device to detect the people's disease. This system uses the Lora module for communication for the doctor's unit. IoT-based health networks attempt to improve health services quality through real-time data collecting and analysis. However, there are significant disadvantages with standard IoT systems. For example, they cannot function efficiently in locations with poor or unstable Internet. The solution to the question of internet services is a feasible option with technology like LPWAN (Low Power Wider Network), such as full-scale order elements, viz. Lora. The monitoring system plays a crucial role in diagnostic procedures and treatment. © 2023 American Institute of Physics Inc.. All rights reserved.

All-Fabric Triboelectric Nanogenerator (AF-TENG) Smart Face Mask: Remote Long-Rate Breathing Monitoring and Apnea Alarm.

Since the beginning of the COVID-19 pandemic, the use of face masks has become not only mandatory in several countries but also an acceptable approach for combating the pandemic. In the quest for designing an effective and useful face mask, triboelectric nanogenerators (TENGs) have been recently proposed. Novel functionalities are provided with the use of TENGs in face masks due to the induced triboelectrification generated by the exhaled and inhaled breath, allowing their use as an energy sensor. Nonetheless, within the face mask, the presence of nontextile plastics or other common triboelectric (TE) materials can be undesired. Herein, we propose the use of an all-fabric TENG (AF-TENG) with the use of high molecular weight polyethylene (UHMWPE) and cotton fabric as negative and positive triboelectric layers, respectively. With these materials, it is possible to detect the breathing of the patient, which in the case of not detecting a signal over a few minutes can trigger an alarm locally, providing valuable time. Also, in this article, we have sent breathing signals locally and remotely to distances up to 20 km via Wi-Fi and LoRa, the same as warning signals in the case of detecting anomalies. This work reveals the use of TENGs in smart face masks as an important tool to be used in difficult epidemiological periods to the general public, bringing much more comfort and relaxation to patients and elderly in today's society, and based on pristine eco-friendly materials.

Home Chimney Pinwheels (HCP) as Steh and Remote Monitoring for Smart Building IoT and WSN Applications.

Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present Home Chimney Pinwheels (HCP) as the Smart Turbine Energy Harvester (STEH) for wind; and Cloud-based remote monitoring of its output data. The HCP commonly serves as an external cap to home chimney exhaust outlets; they have very low inertia to wind; and are available on the rooftops of some buildings. Here, an electromagnetic converter adapted from a brushless DC motor was mechanically fastened to the circular base of an 18-blade HCP. In simulated wind, and rooftop experiments, an output voltage of 0.3 V to 16 V was realised for a wind speed between 0.6 to 16 km/h. This is sufficient to operate low-power IoT devices deployed around a smart city. The harvester was connected to a power management unit and its output data was remotely monitored via the IoT analytic Cloud platform "ThingSpeak" by means of LoRa transceivers, serving as sensors; while also obtaining supply from the harvester. The HCP can be a battery-less "stand-alone" low-cost STEH, with no grid connection, and can be installed as attachments to IoT or wireless sensors nodes in smart buildings and cities.

Distributed localization using normalized quadlateration in LoRa networks: application for tourist position estimation

The tourism sector has been growing rapidly along with the decline of COVID-19. This sector should pay attention to safety and security, which is by tracking the position of tourists. In this work, we propose a tourist positioning tracking system. A node containing a microcontroller with LoRa wireless communication called unknown node (UN) is assembled as a wearable device. We use the received signal strength indicator (RSSI)-based localization technique with an additional normalization method to correct the error rate in position estimation. This method estimates the distance between the anchor node (AN) and UN to approximate the actual distance value. Three-dimensional position tracking (longitude, latitude, altitude) of five UN surrounded by four AN was performed using the quadlateration method. The estimation process was carried out by the UN whereas receiving information is transmitted from the four AN. The communication between AN and UN used a scheduling algorithm on all AN. The position estimation error of the five UNs was measured using mean square error (MSE) yields 20.73 m, 50.32 m, 32.92 m, 21.40 m, and 16.89 m respectively. The accuracy of the estimated distance obtained by the proposed normalization quadlateration method is increased up to 15.22%. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Azolla Quantity Prediction using WSN and Machine Learning

Due to COVID-19 pandemic, the expenditures on pellets and feeds in broiler and fish industries increase every year, leading to price overshoots in various agricultural products. Azolla is an emerging protein source alternative for tilapia and other livestock breeders that is known for its fast reproduction. This study aims to enhance the yield production of Azolla ponds in Nevalga Farm, Brgy. Sala, City of Cabuyao, Laguna by employing wireless sensor network (WSN) technology and predictive machine-learning (ML) methods. LoRa-based WSN was designed to measure the parameters that affect the growth and reproduction of Azolla. Throughout the 24-day monitoring period, the average received signal strength indication (RSSI) and signal-to-noise ratio (SNR) of the packets from the three sensing nodes ranged from -50.86 dBm to -71.39 dBm and 8.92 dB to 9.81 dB, respectively. A total of 3582 data sets were obtained during the observation. Among the three regression ML models used, K-Nearest Neighbor algorithm outperformed Linear Regression and Support Vector Machine in predicting Azolla quantity parameters on both training and validation datasets by yielding the smallest values of root mean square error (RMSE) and absolute error on the seven quantity indicators and achieving squared correlation that varied from 0.935 to 0.997. © 2022 IEEE.

Monitoring Social Distancing in Queues Using Infrared Array Sensor

Since the emergence of the coronavirus disease (COVID-19), more than 165 million people have been infected all around the world (as of May 2021). The COVID-19 virus is highly contagious, especially in large crowded spaces. To prevent the infection and slow the transmission of the virus, the World Health Organization recommends wearing a mask, washing hands regularly, and maintaining a distance of at least one meter between people. To support this prevention, we propose a connected object based on LoRa technology to monitor the social distancing of one meter between people in queues. The proposed system uses infrared array sensor MLX60940 to detect people through their body temperature. Based on this output, we developed an algorithm to verify social distancing. The main advantage of this node is its ability to preserve the privacy of detected people and consume less energy compared to camera-based systems. Experimental results show that the proposed node can detect up to six persons per queue with an accuracy of 93% when the distance between people is below 80 cm.

Fabrication of nanostructured lipid carriers ocugel for enhancing Loratadine used in treatment of COVID-19 related symptoms: statistical optimization, in-vitro, ex-vivo, and in-vivo studies evaluation.

Loratadine (LORA), is a topical antihistamine utilized in the treatment of ocular symptoms of COVID-19. The study aimed to develop a Loratadine Nanostructured Lipid Carriers Ocugel (LORA-NLCs Ocugel), enhance its solubility, trans-corneal penetrability, and bioavailability. full-factorial design was established with 24 trials to investigate the impact of several variables upon NLCs properties. LORA-NLCs were fabricated by using hot melt emulsification combined with high-speed stirring and ultrasonication methods. All obtained formulae were assessed in terms of percent of entrapment efficiency (EE%), size of the particle (PS), zeta potential (ZP), as well as in-vitro release. Via using Design Expert® software the optimum formula was selected, characterized using FTIR, Raman spectroscopy, and stability studies. Gel-based of optimized LORA-NLCs was prepared using 4% HPMC k100m which was further evaluated in terms of physicochemical properties, Ex-vivo, and In-vivo studies. The optimized LORA-NLCs, comprising Compritol 888 ATO®, Labrasol®, and Span® 60 showed EE% of 95.78 ± 0.67%, PS of 156.11 ± 0.54 nm, ZP of -40.10 ± 0.55 Mv, and Qh6% of 99.67 ± 1.09%, respectively. Additionally, it illustrated a spherical morphology and compatibility of LORA with other excipients. Consequently, gel-based on optimized LORA-NLCs showed pH (7.11 ± 0.52), drug content (98.62%± 1.31%), viscosity 2736 cp, and Q12% (90.49 ± 1.32%). LORA-NLCs and LORA-NLCs Ocugel exhibited higher ex-vivo trans-corneal penetrability compared with the aqueous drug dispersion. Confocal laser scanning showed valuable penetration of fluoro-labeled optimized formula and LORA-NLCs Ocugel through corneal. The optimized formula was subjected to an ocular irritation test (Draize Test) that showed the absence of any signs of inflammation in rabbits, and histological analysis showed no effect or damage to rabbit eyeballs. Cmax and the AUC0-24 were higher in LORA-NLCs Ocugel compared with pure Lora dispersion-loaded gel The research findings confirmed that NLCs could enhance solubility, trans-corneal penetrability, and the bioavailability of LORA.

An IoT-enabled Electronic Textile-based Flexible Body Sensor Network for Real-time Health Monitoring in Assisted Living during Pandemic

This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.

WBMS: Waste Bin Management System for densely populated urban areas

Management of solid waste is a major challenge in densely populated urban areas. Such areas also have other predominant health management issues and improper waste management contributes to that. With the rise of COVID in the last two years hygiene has become the first priority of the Government to control the pandemic. Traditionally, municipality vehicles come as their routine to collect solid waste from commonly collected garbage bins placed by local authorities in residential and commercial areas. These bins are usually over-full and the waste roll-out of these bins is due to botching actions of the local occupants. These openly overflowing waste bins add to the contaminated environmental conditions leading to the spread of numerous diseases. Waste-bin management is a challenge in highly populated areas for municipal corporations. Internet of Things (IoT) can be used to monitor such waste bins and notify the municipal corporations about the waste level so that the bins can be cleared on time avoiding the spilling of garbage around them. In this paper, we have proposed an approach to waste bin management using IoT in urban areas. The model is built using LoRa waste-bin surveil units that will monitor and locate waste-bins in residential/commercial areas and notify the authority about their level. © 2022 IEEE.

All-terrain mobile robot desinfectant sprayer to decrease the spread of COVID-19 in open area

The application of disinfection is becoming popular in recent months due to the COVID-19. Usually, the disinfection is used by spraying the liquid into an object. However, the disinfection process for humans and objects in the human environment is still done manually and takes time and increases exposure to viruses. Robotic technology can be a solution to handle that problem. Following that problem, robot design is proposed with many abilities and features. The robot can operate in remote conditions and full function for approximately 56 minutes and spray the liquid for more than 1 meter. This research can effectively be applied in COVID-19 handlings.

Implementation of LoRa in River Water Quality Monitoring

Emergence of Long Range (LoRa) in network technologies become game changer for Internet of Things (IoT) application. Deployment of LoRa enable IoT application of environment monitoring to cover wide area while maintain at low energy and low cost. Water quality monitoring program was developed to maintain and protect quality of water resources for daily purpose. Also, to prevent pollution and disease epidemic peculiarly during Covid19. This research aimed to build autonomous water quality monitoring prototype implemented with LoRa network for support decision system. The Wireless Sensor Nodes (WSN) that embedded with five type of water quality sensors of pH, turbidity, total dissolved solid (TDS), dissolved oxygen (DO) and temperature linked to single gateway. Water environmentalist able to view the result of timely water quality from mobile application dashboard. Though the performance not severely affected, acquired results revealed non-line of sight condition, transmission power and Spread Factor (SF) value influenced LoRa performance in urban environment. In conclusion, a few improvements on the system grant LoRa high capabilities to be integrated with IoT environment application in urban environment. © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022).

IoT Architecture Based Water Resources Conservation Management Using LoRa

Urbanization and anthropogenic activities are impacting the biodiversity of the water stream. Consequently, it interrupts water supply for daily purpose and sanitization requirement during pandemic Covid19. Water quality monitoring program designed to control water pollution. Development of IoT contributed to environment conservation including monitoring purpose for support decision system. This paper aims to study the integration of LoRa network with automatic water quality monitoring system. The proposed prototype of the system built with one gateway as base station and wireless sensor nodes (WSN) that embedded with water quality sensor of pH, turbidity, total dissolved solid (TDS), dissolved oxygen (DO) and temperature. The daily water quality status can be viewed in developed mobile application dashboard. Result shows that LoRa capabilities were affected from non-line of sight condition, transmission power and Spread Factor (SF) value. In conclusion, LoRa is compatible to be integrated with water quality monitoring system in urban environment. © 2021 IEEE.

A Novel Smart Healthcare System Design for Internet of Health Things

COVID-19 might be devastatingly affecting our enterprises, public activities and individual prepping norms and principles but it has also sparked a digital revolution of innovation in different fields. The objective of this paper is to understand the in-depth role of the Internet of Things (IoT) in eHealth to mitigate the impact of COVID-19. This paper covers numerous applications of IoT in healthcare starting from research, telemedicine, teleconsultation via chatbots and virtual assistants providing instantaneous medical help online. Telemedicine and remote patient monitoring is the need of the hour to avoid direct contact with the patients which have been made possible via IoT and its associated tools like Artificial Intelligence, Machine Learning, Blockchain technology and Cloud Computing. With such high volumes and diversity of data being generated from IoT there is a strong need for connectivity and streaming analytics thus 5G technology and its applications have been discussed like smart 5G connected ambulances and smart 5G based hospitals. Long Range Radio is another promising technology which due to its low power operation and long-distance data transmission at higher speeds is turning out to be the defacto technology for IoT networks across the globe especially in areas with poor network coverage. Seeing the demand for both ventilators and skilled medical professionals due to lack of proper medical infrastructure worldwide, a review of IoT-based smart ventilators has also been carried out. The paper concludes with possible solutions to IoT challenges in healthcare by proposing a smart healthcare model design. Moreover keeping in mind the situation of Covid-19 Pandemic the module also comprises a UVC Disinfection box that would help in eliminating the risk of the virus entering our homes. © 2021 IEEE.

Cost-effective vital signs monitoring system for COVID-19 patients in smart hospital.

The lack of staffing during COVID-19 pandemic drives hospitals to expand their facilities in non-traditional settings to include centralized communication systems to monitor the vital signs of patients and predictive models to identify their health conditions. In this research, we have developed a microcontroller-based wireless vital signs monitoring system, which is able to measure the body temperature, heart rate, blood oxygen level, respiratory rate and Electrocardiogram of the patients. We managed to obtain a reliable but more affordable vital signs monitor with high mobility that can be implemented in large hospitals. The system satisfies the design considerations of medical centers in terms of size, cost, power consumption and simplicity in implementation. The developed system consists of a set of wearable sensor nodes, wireless communications infrastructure with multiple communications techniques to carry vital data from the patients to the management system that handles the patient's medical data, and a graphical user interface with a control system that enables the hospital staff to observe the status of all the patients and take the appropriate actions. The system was implemented using 40 sensor nodes, 4 distribution points and one gateway covering a hospital area of approximately 2500 m2. The system was tested and the measured percentage of lost packets is found to be less than 3.3% of those sent. During transmission, the current measured from the sensor node was 10.5 mA with a 3.3 V input voltage, which prolonged the operating time of the battery used.

Smart Face Mask with an Integrated Heat Flux Sensor for Fast and Remote People's Healthcare Monitoring.

The COVID-19 pandemic has highlighted a large amount of challenges to address. To combat the spread of the virus, several safety measures, such as wearing face masks, have been taken. Temperature controls at the entrance of public places to prevent the entry of virus carriers have been shown to be inefficient and inaccurate. This paper presents a smart mask that allows to monitor body temperature and breathing rate. Body temperature is measured by a non-invasive dual-heat-flux system, consisting of four sensors separated from each other with an insulating material. Breathing rate is obtained from the temperature changes within the mask, measured with a thermistor located near the nose. The system communicates by means of long-range (LoRa) backscattering, leading to a reduction in average power consumption. It is designed to establish the relative location of the smart mask from the signal received at two LoRa receivers installed inside and outside an access door. Low-cost LoRa transceivers with WiFi capabilities are used in the prototype to collect information and upload it to a server. Accuracy in body temperature measurements is consistent with measurements made with a thermistor located in the armpit. The system allows checking the correct placement of the mask based on the recorded temperatures and the breathing rate measurements. Besides, episodes of cough can be detected by sudden changes in thermistor temperature.

Early Warning Systems for COVID-19 Infections Based on Low-Cost Indoor Air-Quality Sensors and LPWANs.

During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture.

Optimizing the Performance of Pure ALOHA for LoRa-Based ESL.

(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 1~51 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 1~2% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).

Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications.

In hostile and remote environments, such as mountains, forests or suburban areas, traditional communications may not be available, especially after a disaster, such as a flood, a forest fire or an earthquake. In these situations, the wireless networks may become congested or completely disrupted and may not be adequate to support the traffic generated by rescuers. It is also considered as the key tool in Corona Virus (COVID-19) battle. Moreover, the conventional approaches with fixed gateways may not work either, and this might lead to decoding errors due to the large distance between mobile nodes and the gateway. To avoid the decoding errors and improve the reliability of the messages, we propose to use intermediate Unmanned Aerial Vehicles (UAVs) to transfer messages from ground-based Long Range (LoRa) nodes to the remote base station (BS). Specifically, this UAV-enabled LoRa architecture is based on the ad hoc WiFi network, wherein, UAVs act as relays for the traffic generated between LoRa nodes and BS. To make the architecture more efficient, a distributed topology control algorithm is also proposed for UAVs. The algorithm is based on virtual spring forces and movement prediction technique that periodically updates the UAV topology to adapt to the movement of the ground-based LoRa nodes that move on the surface. The simulation results show the feasibility of the proposed approach for packet reception rate and average delay quality of service (QoS) metrics. It is observed that the mechanisms implemented in a UAV-enabled LoRa network effectively help to improve the packet reception rate with nominal buffer delays.