Help Of IoT Research Articles

IoT-based Smart Pill Reminding System

Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display “Welcome Patient” and “You will be updated”. Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message “Medicine Taken”. If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the message “Med1 not Taken”. The Blynk will also be updated and will display the same messages. The green LED shows the status of the consumption of the pill. The email will sent to the caretaker in this case. Conclusion: In this work, all the problems related to the system are overcome in a systematic manner with the help of IoT and basic electronic applications. Thus, the system will help the user take all the prescribed medicines on time and will also update the user’s caretaker via application and email services. This will largely help the working class as well as the senior citizens. Thus, this proposed system can be commercialised as a handy and cost-effective device.

Automated smart waste segregation system using IoT technology

One of the major challenges faced not just by India but globally as well is waste management. India is the second most populous country and naturally generates tonnes of garbage daily. It is common to find dustbins overflowing with junk scattered everywhere, not to mention that segregation of waste is conveniently neglected. Many-a-times it is observed that despite providing separate bins for dry and wet trash, the convention is carelessly ignored. With the traditional methods of periodic garbage collection and manual segregation of wet, metallic, non-recyclable, and recyclable waste proving to be inefficient, there is a need for a better solution. With the help of IoT, a more effective solution is proposed to improve the current system and take a step forward toward the vision of smart cities. The proposed system uses smart garbage tracking, segregation, and collection systems, interfaced with ultrasonic sensors, rainfall/moisture sensors, inductive proximity sensors, and servo motors, along with the necessary hardware, with Arduino as the main controlling unit. The Arduino platform is used as a software platform for the working of the model and obtaining the expected results.

A comprehensive and systematic study in smart drip and sprinkler irrigation systems

Many regions of the world have unpredictable rainfall, making agriculture impossible to sustain without diverse irrigation technologies. Irrigation has a huge impact on crop productivity, but traditional irrigation systems can be replaced with Advanced and Smart irrigation techniques, which can enhance production yield even more. In arid regions where water is scarce, drip irrigation is a good option because water is supplied at a low rate and it also saves a lot of water, whereas, in horticulture, sprinkler systems are widely used because they do not require large pipelines all over large lands and instead spray water droplets all over the area like rain. The purpose of this article is to analyze the various types of smart drip and sprinkler irrigation systems with the help of IoT and also find solution for the various challenges found in the various methods. Various irrigation technologies which include IoT are reviewed, with a focus on smart drip and sprinkler systems. Various comparisons have been conducted between various methods to find the most efficient one in both irrigation techniques. A table summarizing the similarities between the various methods is also presented. Finally, all of this leads to saving water and making farmers' work easier.

Electrical energy estimation of 3D printing jobs for industrial internet of things (IIoT) applications

PurposeThis paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates.Design/methodology/approachAn IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs.FindingsThe proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates.Practical implicationsProposed algorithm is validated with limited number of experiments.Originality/valueIIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff.

An Efficient Self-Organized Detection System for Algae.

Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.

Development Of Smart Medidoc System For Monitoring Healthcare In Iot Environment

This paper focuses on the design and development of a system to monitor health parameters of the patients using IoT Technology. This system helps to measure patient’s health parameters such as body temperature, pulse rate, oxygen saturation level, weight and height. The design of MEDIDOC system is performed using SOLIDWORKS software. The static structural analysis is performed on the base structure of MEDIDOC system using ANSYS software to evaluate the total deformation, equivalent stress and factor of safety. The results suggested that the structural effect on the base structure due to load is under permissible range. Then, the electronic integration with the system is carried out. The non-contact type sensors are used for all kind of measurements and controlling of various sensors are performed through NodeMCU. The collected data of human health parameters such as height, weight, body temperature, pulse rate and oxygen saturation level are displayed in the LCD screen. Hand dispenser is also incorporated in the system to avoid any kind of cross infection. The measurement data of human can be transmitted to Google sheet with the help of IoT. Hence, physician can access and validate the measurement data at any point of time. The proposed system offers efficient way of examining human health parameters and facilitates the physician to minimize the checkup time compared to conventional approach.

Development Of Smart Medidoc System For Monitoring Healthcare In Iot Environment

This paper focuses on the design and development of a system to monitor health parameters of the patients using IoT Technology. This system helps to measure patient’s health parameters such as body temperature, pulse rate, oxygen saturation level, weight and height. The design of MEDIDOC system is performed using SOLIDWORKS software. The static structural analysis is performed on the base structure of MEDIDOC system using ANSYS software to evaluate the total deformation, equivalent stress and factor of safety. The results suggested that the structural effect on the base structure due to load is under permissible range. Then, the electronic integration with the system is carried out. The non-contact type sensors are used for all kind of measurements and controlling of various sensors are performed through NodeMCU. The collected data of human health parameters such as height, weight, body temperature, pulse rate and oxygen saturation level are displayed in the LCD screen. Hand dispenser is also incorporated in the system to avoid any kind of cross infection. The measurement data of human can be transmitted to Google sheet with the help of IoT. Hence, physician can access and validate the measurement data at any point of time. The proposed system offers efficient way of examining human health parameters and facilitates the physician to minimize the checkup time compared to conventional approach.

Smart load balancing system for 3-phase 4 wire distribution system

In 3-phase 4 wire distribution system, imbalance phenomena arise because of disproportionate distribution of single-phase loads among 3-phases. To address this issue, distribution system requires equitable load sharing in each phase. In this paper, we proposed an intelligent system which can sense the loading in each phase and distribute the load proportionately in each phase of 3-phase 4-wire distribution system. In our proposed hardware, AI based micro-controller is used to sense the least loaded phase and gives command to relay module to connect the load to the least loaded phase whenever any new load is connected to the line. The load on each phase can be monitored in real-time with the help of IoT based data communication. The results of the proposed work shows that whenever any new load is connected to any phase, it automatically gets connected to the least loaded phase. This work is effective in balancing of load in each phase of 3-phase 4-wire distribution system.

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

Wheat Disease Detection for Yield Management Using IoT and Deep Learning Techniques

Our economy is mostly based on agriculture. One of the difficult problems in the agriculture sector is crop yield predictions. Crop yield prediction using a machine learning algorithm with the help of IoT increases the production of wheat yield and improves the quality of yield. Today's low agricultural production is a problem for farmers. Low crop output is mainly caused by a lack of information regarding soil fertility and crop selection, and proper crop selection is the key to maximizing crop yield. One of the interesting agricultural research areas where deep learning (DL) algorithm concepts can be used is the identification of wheat disease from images. We consider two leaf diseases septoria and stripe rust and also take a healthy leaf and then do a comparison between the leaves using CNN. As a contribution, we developed a system ML with a neural network mobilenet and efficient net-b3 that detects wheat leaf disease and improves accuracy gradually. Moreover, we do a complete review of yield management in which IoT sensors are used with machine learning algorithms. This study aims to create a system that can correctly choose a crop for maximum yield utilizing IoT devices and machine learning (ML) algorithms. We achieve 97% accuracy using mobilenet which is better than the efficient net. The presented work also applied different image augmentation techniques to remove the problem of overfitting. The presented work is compared with the state-of-the-art method in terms of accuracy and precision score.

Result Paper on IOT Based Induction Motor Speed Control and Parameter Monitoring

Abstract: Today’s application of single phase induction motor is increased due to it simple operation and availability of single phase supply in every place. The single phase motor available in different rating, Different speed and used for different application. The single induction motor is used for particular operation need to protect motor and need to control parameters of single phase induction motor and monitor this data to user operator. This is possible with help of IOT in this project single phase induction motor parameter monitoring and speed control is done through IOT base system. In this project the parameters of the motor is continuously recorded and send to the user operator through the cloud internet to end user application module. Keywords: Induction Motor, IOT, Speed Control, Monitoring etc.

Design and Development of Floor Cleaning Robot

Abstract: Robots are being extensively used in various fields of work. They have taken over dangerous, boring, and monotonous jobs. While cleaning is still done with the help of maids and janitors in institutes and big building complexes which strains and tires them. Hence the need for an autonomous floor cleaning robot. The existing floor cleaning robots are bulky and are not very user-friendly. They tend to be on the expensive end. Our project aims at providing a solution at lower cost, better integration with the help of IoT and better overall user-friendliness. Keywords: robots, Autonomous, IoT, user-friendliness, integration

Fog computing based ultrasound nerve segmentation system using deep learning for mIoT

Internet of Things is an ever expanding field and applications can be used for medical field. Patient monitoring and diagnosis can be done with the help of IoT and the problems of storing large amount of data can be solved by using cloud computing. However, when transmitting large amount of data through the network, the latency will be impacted. This can be eliminated by introducing a fog layer for the processing of data and processed data later can be stored in the cloud. This study proposes a novel architecture for a hospital ultrasound system and deep learning algorithm is used for the nerve segmentation and a good accuracy is achieved.

Blood Bank Management and Inventory Control Database Management System

This paper presents a detailed approach for an efficient blood bank database management system. The database is the single most useful setting for caching data, and it is also an ideal tool for contriving, managing, updating, and modifying data from different angles. The benefits of a well-structured blood bank database are limitless and yield the benefits of improving efficiency and saving time. Here, our motive is centred on this area. India faces a shortage when it comes to the amount of blood donated. The gap in demand and supply in widened due to mismanagement and inefficient databases. We have modelled a well-organized database to try and reduce this gap. Alongside, we have developed an application that reminds donors when they become eligible again, gives locations of nearby blood donation camps, makes requesting blood easier for blood recipients etc. as well as promoting a healthy community. IOT is used for interlinking the application to the server as well as for inter-application communication. With the help of IOT this collection and exchange of data becomes more efficient.

Design and development of IoT enabled gas sensing system for remote monitoring of air quality in borewell rescue operations

The children demise inside the borewell is expanded in nowadays, with uncovered borewell they fallen without knowing and lost their lives due to asphyxiant inside, likewise without oxygen, food and so forth The harmful gases like carbon monoxide, Methane, LPG, hydrogen sulphide inside the bore-well it will influence the children breathing and furthermore this may prompt unconsciousness, and without oxygen it might influence the brain functioning of child and child may die and furthermore explicit distance of the child at what distance child stuck isn’t know. To overcome these, we need to detect the various gases with different multiple gas sensors additionally to get the temperature and humidity condition. Alongside this ultrasonic sensor is utilized to get the distance of child at what distance child got stuck. We have utilized two Arduino Uno microcontrollers which is at the transmitter side and other at Receiver side also utilized two ZigBee’s as the communication devices. With the help of IoT involved in the proposed system. Every one of these information are sent to the cloud and we can monitor the data in the thing speak dashboard through PC or from our smart phone through Android App Usage and also, through LCD at Receiver end. We can utilize this proposed framework inside the borewell for up to 80-meter depth. Thus, we came up with this Design and Development of IoT enabled Gas sensing system for remote monitoring of Air quality in borewell Rescue operations. Based on proposed system results of real time data the Rescue specialists can make a further move by providing of oxygen, food, and so forth Save their lives.

IoT Based Novel Smart Blind Guidance System

In this research paper the system which is proposed that can be used for safe walking for blinds. This system consist of wireless sensor within the stick which provide the information of the obstacle between the way. The main advantage of this system is the safe for the blind people walking on the road, and make them independent while walking. When obstacle is detected an alert will be given to user with the help of buzzer an vibration . The unique feature of the system is to detect the temperature of a person who passes within the range of 6 feet of which helps in maintaining the social distancing in COVID situation. The system contains a wireless sensor that integrates temporary networks that can be made within the navigation stick, which can provide group communication between them, where roaming information and networks can be provided. With the help of IOT the location and alert message shared with family members in case of emergency. The system proposed in this research study is 60% more efficient then conventional system. The information is included in table1 to validate the result.

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods.

Smart Parking System

In the modern age, many people have vehicles. Vehicle is now a primary need. Every place is under process of urbanization. There are many supermarkets and shopping centers etc. There are many creative places where people used to go for refreshing and relaxation. All these places are full of with people so they need a parking space where people can park their vehicles safely and easily. Every parking area needs a website or system that records the detail of vehicles to give the parking facility. With the help of iot based system we can deliver a good service to users/people who wants to park their vehicles into organization’s premises. Present days in parking areas they just maintain the vehicles just with tokens and they have records of vehicle details in books so that during some critical situations like police enquiry of terrorist car or vehicle missing that case it is difficult to find the details of particular vehicle. But with our parking management system it is easy to find within 1 to 2 seconds. By parking the vehicle in public place the vehicle can be claimed by other person but in this case there is no such problem and no need to give fine for anything we can park our vehicle with securely.

IOT Early Flood Detection & Alerting System using Arduino

The technical and scientific advancements in the current industrial age have revolutionized our lives and provided us with plenty of comforts and conveniences. However, this industrial progress has come at a hefty cost of global warming and environmental disasters. The increasing carbon footprints and greenhouse gas emissions have severely disturbed the natural cycle of rains and floods. Hence, now we are facing the dangers of unwarned floods more than ever before. Flooding is typically brought on by an increased quantity of water during a water system, sort of a lake, river overflowing. On occasion a dam fractures, abruptly releasing a huge quantity of water. The outcome is that a number of the water travels into soil, and ‘flooding’ the region. In order to detect and reduce damages caused by floods in a timely manner, technology plays a crucial role. With the help of technology, we can reduce natural disasters caused by floods. In this system we make use of a Arduino Uno interfaced with 4 different sensors, named as Ultrasonic sensor for measuring water levels, float sensor detect full water, Flow sensor for knowing speed of water and humidity sensor. These combinations of sensor are used to predict flood and alert respective authorities with help of IOT and sound instant alarm in nearby villages to instantly transmit information about possible floods. These sensors provide information over the IOT using Wi-Fi module. On detection of conditions of flooding the system predicts the quantity of your time it migh take to arrive a specific area and alerts the villages/areas that would be affected by it.

IOT Framework for Heart Diseases Prediction Using Machine Learning

latest evolution in the domain of internet of things and technology of sensors can be carrier for online healthcare services. The modern rectification in IOT sector enhance the employed rate in online healthcare control. So according to in this paper we proposed a framework for heart diseases prediction with the help of IOT and machine learning. Basically IOT sensors attached with patient body and that sensors transfers the patient data into dataset form. Then help with the machine learning classification algorithms predict the data for patient having anormal or abnormal form of data