IoT Monitoring Research Articles

IoT monitoring in healthcare: A review of systems

IoT monitoring in healthcare: A review of systems

Remote Data Acquisition and Management Technology of Power Equipment Based on Internet of Things

This paper proposes an architecture scheme for realizing the power equipment data acquisition IoT monitoring network using the latest Internet of Things technology. At the same time, a power equipment data acquisition and monitoring system based on Narrowband Internet of Things (NB-IoT) and exclusive-OR (XOR) encryption algorithms is designed. The system is mainly researched and designed from the aspects of monitoring terminal software and hardware design, data encryption processing, and remote data management cloud platform. This paper installs a big data analysis processor and a network routing processor based on the Internet of Things technology and modifies the system memory, display, and connection circuit to optimize the system hardware equipment. Finally, the system conducts experimental tests to monitor the state of power equipment, such as transmission and distribution towers, power manhole covers, cable tunnels, and distribution line knife gate temperature. The experimental results show that the system can realize low power consumption, comprehensive coverage, low-cost unified access, and centralized management of power equipment status information in different power scenarios. The application of this system dramatically improves the reliability and efficiency of the operation and maintenance of terminal equipment of the power system. The promotion of this system has a great application value.

Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion.

Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.

Imperative Role of Automation and Wireless Technologies in Aquaponics Farming

Food and agriculture are significant aspects that can meet the food demand estimated by the Food Agriculture Organization (FAO) by 2050. In addition to this, the United Nations sustainable development goals recommended implementing sustainable practices to meet food demand to achieve sustainability. Currently, aquaponics is one of the sustainable practices that require less land and water and has a low environmental impact. Aquaponics is a closed-loop and soil-less method of farming, where it requires intensive monitoring, control, and management. The advancement of wireless sensors and communication protocols empowered to implementation of an Internet of Things- (IoT-) based system for real-time monitoring, control, and management in aquaponics. This study presents a review of the wireless technology implementation and progress in aquaponics. Based on the review, the study discusses the significant water and environmental parameters of aquaponics. Followed by this, the study presents the implementation of remote, IoT, and ML-based monitoring of aquaponics. Finally, the review presents the recommendations such as edge and fog-based vision nodes, machine learning models for prediction, LoRa-based sensor nodes, and gateway-based architecture that are beneficial for the enhancement of wireless aquaponics and also for real-time prediction in the future.

Deep transfer learning-based vehicle classification by asphalt pavement vibration

Deep transfer learning (TL) has great potential for a wide range of applications in civil engineering. This work aims to propose a deep transfer learning-based method for vehicle classification by asphalt pavement vibration. This work first used the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. The vibration signals were then represented in two different ways, including the time-domain graph and the time-frequency graph. Finally, two deep transfer learning-based methods, namely Method Ⅰ (Time-domain & TL) and Method Ⅱ (Time-frequency & TL), were applied for vehicle classification according to the two different representations of vibration signals. The results show that the CNN model had a satisfactory performance in both methods with the accuracy of Method Ⅰ exceeding 0.94 and Method Ⅱ exceeding 0.95. The CNN model in Method Ⅱ performed better in the accuracy metrics with considering label imbalance, but worse in the accuracy metrics without considering label imbalance than that in Method Ⅰ. The differences between these two methods have been investigated and discussed in detail in terms of input types, accuracy metrics, and application prospects. The CNN model with deep transfer learning could be an effective, accurate, and reliable technique for vehicle classification based on asphalt pavement vibration.

IOT IMPLEMENTATION FOR CALCULATING POWER CONSUMPTION BASED ON ANDROID

Electrical power consumption monitoring, mainly at business properties in urban areas that have one line power supply distribution at many area rent, had the problem of the bill electric consumption on the tenant. The places like dormitories, food court, futsal field, pets day care etc. had problems, where the node had more electrical consumption operationally, so we implemented IoT monitoring and control at electrical device and electrical consumption report. The method to implement this system is using IoT protocol communication and firebase cloud data as a third party database application that is accessed by an android mobile phone. The systems of our research is infrastructure IoT mobile communication that can be controlled and adjusted by firebase cloud and can activate the device by MCU microcontroller ESP wifi that Based to monitor and record activity that had controlled by mobile phone. All data activity has to be processed and saved to the firebase data cloud, so the system depends on internet communication and an active firebase server. So the result of our research is to report activity and bill of electrical consumption every node that had rented.

Obtaining daily catches of the beet armyworm, <i>Spodoptera exigua</i>, by using a sticky pheromone trap equipped with an IoT monitoring camera: verification in a commercial chrysanthemum field

Obtaining daily catches of the beet armyworm, <i>Spodoptera exigua</i>, by using a sticky pheromone trap equipped with an IoT monitoring camera: verification in a commercial chrysanthemum field

AI Based Smart High Way with Green Energy Resources Using IoT Devices

Abstract: The Worldwide concern to mitigate the soaring energy crisis introduces us to the small-scale renewable energy generation as a preferred enabling source for highway lighting. Production of renewable energy from highways using solar tracking panel can be utilized in various applications. In this project we use solar panels to generate renewable energy which can be stored in lithium batteries. The IOT monitoring system in interfaced with Adafruit software which is used to check the pollution and stores the daily utilization in cloud. The SOS alarm is used along with the device to indicate the accident occurred in remote areas to the nearby toll gate. SOS message will send IFTTT to the domain mail ID toll gate. This prototype uses Node MCU for controlling application and database monitoring. In future the project can be extended by implementing other renewable resources like wind energy. Keywords: Sos alarm, Solar tracking panel, Adafruit, Mcu , Renewable enegry.

An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions

Diabetes is considered among the major critical health conditions (chronic disease) around the world. This is due the fact that Glucose level could change drastically and lead to critical conditions reaching to death in some advance cases. To prevent this issues, diabetes patient are always advised to monitor their glucose level at least three times a day. Fingertip pricking - as the traditional method for glucose level tracking - leads patients to be distress and it might infect the skin. In some cases, tracking the glucose level might be a hard job especially if the patient is a child, senior, or even have several other health issues. In this paper, an optimum solution to this drawback by adopting the Wireless Sensor Network (WSN)-based non-invasive strategies has been proposed. Near-Infrared (NIR) -as an optical method of the non-invasive technique - has been adopted to help diabetic patients in continuously monitoring their blood without pain. The proposed solution will alert the patients’ parents or guardians of their situation when they about to reach critical conditions specially at night by sending alarms and notifications by Short Messages (SMS) along with the patients current location to up to three people. Moreover, a Machine Learning (ML) model is implemented to predict future events where the patient might have serious issues. This model prediction is best practice in this chronic health domain as it has never been implemented to predicted a future forecast of the patient chart. Multivariate Time-Series data set (i.e. AIM ’94) has been used to train the proposed ML model. The collected data shows a high level of accuracy when predicting serious critical conditions in Glucose levels.

IoT for Residential Monitoring Using ESP8266 and ESP-NOW Protocol

This research aims to create a cluster housing monitoring system. This system combines IoT with the ESP-NOW protocol. In this system, the head of security and homeowners can unify the state of the house through a web application and an android application. The system will send notifications via the web and the LINE messaging application if there is an intrusion or gas leak or no fire is detected. The contribution of this research is to shorten the path and time of data transmission and make it easier for homeowners and heads of security to monitor and take action as soon as possible when something goes wrong in the residential area. The method used in this research is experimental. There are 4 main parts to this system, namely ESP-NOW sender, ESP-NOW receiver, gateway module, and server. All microcontrollers in this system use the NodeMCU ESP8266 because the NodeMCU, apart from being a controller, also functions as a WiFi module. By using the ESP-NOW protocol, it is possible for every microcontroller, both the sender and the receiver, to communicate without using WiFi WAP. In this study, the ESP-NOW sender collected 10 installed in each house to unify the conditions of the house. The ESP-NOW receiver worked to receive data from all senders. Any data received by the receiver will be sent to the gateway serially. The gateway module functions to send all messages to the server online. Application on the server-side built using PHP and MySQL. The display of the system can be opened through the android application or web application. The results of this test indicate that the system is running well with a 100% success proportion where every message from the sender is stored on the server-side, and the application can provide notifications according to the scenario of the system.

IoT Social Distancing and Monitoring Robot for Queue

Social distancing is of key importance during the current pandemic. It helps limit the spread of covid by observing distance between disease spreading individuals. Now it is not possible to station a person 24×7 at each queue to monitor social distancing violations. Banks, Public Offices, Malls, Schools, Theatres etc. usually see long queues for hours every day. To ensure social distancing in queues we hereby design a social distancing monitoring robot.

An idea on Smart Farming: IoT monitoring of water production from dihydrogen combustion

Smart Farming is a concept developing rapidly and gaining momentum. The management of livestock and farm products is done in an automated way thanks to IoT technology. The large field of data at hand offers the possibility of analysis for a better understanding of issues and more efficient decision-making. The management of water consumption is one of the most relevent Smart Farming use cases. In the event of drought, the pressure on water resources becomes increasingly strong. What if we produced water then? The idea of not worrying about the consequence of drought on agricultural production would be interesting. One of the first experiences you learn in a chemistry class is that the combustion of dihydrogen produces water. However, it is necessary to follow this experience closely because of the risk of explosion. Dihydrogen can be produced by the gasification of (agricultural) biomass. Here, the technology takes over, by the means of a supervising IoT system. This system will manage the overall process from biomass production, then dihydrogen production (biomass-to-hydrogen), to water production (dihydrogen-to-water). If the idea proves to be viable on a large scale, the result would be valuable in reducing the issue of water scarcity, in times of drought, in agricultural areas, and even in allowing energy autonomy on farms.

Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere

A smartphone plummeted from a stratospheric height of 36 km, providing a near-real-time record of its rapid descent and ground impact. An app recorded and streamed useful internal multi-sensor data at high sample rates. Signal fusion with external and internal sensor systems permitted a more detailed reconstruction of the Skyfall chronology, including its descent speed, rotation rate, and impact deceleration. Our results reinforce the potential of smartphones as an agile and versatile geophysical data collection system for environmental and disaster monitoring IoT applications. We discuss mobile environmental sensing capabilities and present a flexible data model to record and stream signals of interest. The Skyfall case study can be used as a guide to smartphone signal processing methods that are transportable to other hardware platforms and operating systems.

A dual synchronization prediction-based data aggregation model for an event monitoring IoT network

The abrupt changes in the sensor measurements indicating the occurrence of an event are the major factors in some monitoring applications of IoT networks. The prediction-based approach for data aggregation in wireless sensor networks plays a significant role in detecting such events. This paper introduces a prediction-based aggregation model for sensor selection named the Grey prediction model and the Kalman filter-based data aggregation model with rank-based mutual information (GMKFDA-MI) that has a dual synchronization mechanism for aggregating the data and selecting the nodes based on prediction and cumulative error thresholds. Furthermore, the nodes after deployment are clustered using K-medoids clustering along with the Salp swarm optimization algorithm to obtain an optimized aggregator position concerning the base station. An efficient clustering promises energy efficiency and better connectivity. The experiments are accomplished on real-time datasets of air pollution monitoring applications and the results for the proposed method are compared with other similar state-of-the-art techniques. The proposed method promises high prediction accuracy, low energy consumption and enhances the throughput of the network. The energy-saving is recorded to be more than 10 to 30% for the proposed model when compared with other similar approaches. Also, the proposed method achieves 97.8% accuracy as compared to other methods. The method proves its best working efficiency in the applications like event reporting, target detection, and event monitoring.

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.

An An implementation of smart agriculture for optimizing growth using sonic bloom and IoT integrated

This paper proposes the implementation of IoT-based agriculture monitoring with audio growth (sonic bloom) to optimize the growth of plants and harvest. Sonic bloom is a technology that combines high-frequency sound waves from living things, nature or music, and organic nutrients, which aims to make plants grow more optimally so that they can increase productivity. The method’s main advantage is implementing an innovative IoT monitoring hybrid with audio growth systems to boost the plantation growth and maximize the yield. Our experiment in 10 planters using chilies, has proven the proposed procedure. This work is backed up with literature studies of the audio growth (sonic bloom) in IoT technologies. To validate our findings, four parameters were measured through different sensors such as light sensors, temperature, humidity, and soil moisture. It was found that the proposed method can achieve significant results against the comparison in terms of plant heights and new sprouts for the harvest.

IoT-Enabled IEEE 802.15.4 WSN Monitoring Infrastructure-Driven Fuzzy-Logic-Based Crop Pest Prediction

Precision agriculture, as the future of farming technology, addresses challenges faced by farmers by data mining of information collected through IoT-enabled crop monitoring infrastructures. The identification of crop disease is one of the widely studied challenges. Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. This proposal presents a fuzzy-logic-based pest prediction mechanism assisting in beforehand preparedness for potential pest prevention. The experiments are performed for pests in rice and millet crops. The data mining over samples collected in a cropping cycle revealed the plausible correlation between temperature, relative humidity, and rainfall with pest breeding. The data collected through IoT monitoring infrastructure is analyzed for the ambient breeding condition of the pest. These conditions are then employed to design the knowledge base of the fuzzy system. More specifically, the linguistic variables of the fuzzy membership function are optimized using a genetic algorithm for close prediction of pest breeding in given environmental conditions. The proposal verified that the weather factors have a strong impact on the occurrence of pests and diseases, and the fuzzy-logic-based pest prediction through IoT application development services will help farmers to take precautionary measures beforehand.

Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

Remote IoT Education Laboratory for Microcontrollers Based on the STM32 Chips.

The article describes the implementation of IoT technology in the teaching of microprocessor technology. The method presented in the article combines the reality and virtualization of the microprocessor technology laboratory. A created IoT monitoring device monitors the students’ microcontroller pins and sends the data to the server to which the teacher is connected via the control application. The teacher has the opportunity to monitor the development of tasks and student code of the program, where the functionality of these tasks can be verified. Thanks to the IoT remote laboratory implementation, students’ tasks during the lesson were improved. As many as 53% (n = 8) of those students who could improve their results achieved an improvement of one or up to two tasks during class. Before the IoT remote laboratory application, up to 30% (n = 6) of students could not solve any task and only 25% (n = 5) solved two tasks (full number of tasks) during the class. Before implementation, 45% (n = 9) solved one problem. After applying the IoT remote laboratory, these numbers increased significantly and up to 50% (n = 10) of students solved the full number of tasks. In contrast, only 10% (n = 2) of students did not solve any task.

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.