IoT-based Monitoring System Research Articles

IoT-based system to measure thermal insulation efficiency

The main purpose of thermal insulation is to reduce the effect of weather conditions, which can reduce energy consumption and help preserve the natural environment. Due to increasing demand for thermal insulation in modern buildings, it is necessary to monitor the effects of weather condition on the efficiency of thermal insulation. To measure this effect, Internet of Things (IoT)-based sensor networks play an important role. This paper proposes an IoT-based monitoring system that measures and analyses the effect of weather conditions on thermal insulation efficiency. The proposed system includes an Arduino as a central unit that interfaces at the input with temperature and relatively humidity sensors, and at the output with a WiFi module that transmit the collected data via the Internet to an IoT analytics platform to aggregate, analyze, and visualize the data. The proposed system is low cost and provides insight into the design and implementation of a complete application with important IoT features, e.g., sensing and transmitting data to the cloud, data processing, and data retrieval through the ThingSpeak open-source API. In a proof of concept, the proposed system was implemented and tested in different environments and at different altitudes, and the results were analyzed and compared to standard weather data. The sensed data can be further analyzed and leveraged by end users to mitigate the effects of weather conditions on thermal insulation.

Development of IoT based Monitoring System for Home Quarantine Person during Covid-19

Development of IoT based Monitoring System for Home Quarantine Person during Covid-19

The use of LSTM networks in the detection of outliers in IoTbased air quality monitoring systems

The use of LSTM networks in the detection of outliers in IoTbased air quality monitoring systems

An IoT-Based Cyber-Physical Framework for Turbine Assembly Systems

Turbines are typical engineering-to-order products which can be highly customized. This paper considers the final assembly control under uncertainty in turbine assembly workstations. A cyber-physical framework based on Internet of Things (IoT) is proposed for the turbine assembly, which consists of physical components, cyber components and an IoT-based monitoring system. The IoT-based monitoring modules capture real-time data of the physical components, including the workers, tools, parts and the actual assembly process. These cyber modules can generate the original scheduling for the assembly tasks, re-sequence the assembly tasks, re-assign the workers and control the logistics when an unexpected event occurs. Physical activities are undertaken in a turbine assembly workstation based on assembly instructions and guidance from the cyber components. The proposed framework can be implemented in the large turbine assembly system, which can facilitate real-time information driven assembly process monitoring and collaborative control of the task sequence, the worker assignments and logistics in a closed-loop environment. The experimental results show that our method can significantly improve the quality and the efficiency of a turbine assembly system.

Fault Tolerance Mechanism Combining Static Backup and Dynamic Timing Monitoring for Cluster Heads

Internet of things (IoT) monitoring systems have been extensively applied in smart homes, underwater monitoring, volcano monitoring, and health monitoring. In IoT applications, a wireless sensor network (WSN) is deployed to collect data. Hierarchical routing protocols that effectively maintain the energy consumed by sensor nodes (SNs) are usually employed in WSNs. Cluster heads (CHs) are important in this type of protocol. An effective fault tolerance mechanism for CHs in this system can guarantee reliable data acquisition. In this paper, a fault tolerance mechanism that combines CH static backup and dynamic timing monitoring (SBDTM) is proposed, a CH reliability model based on the Markov model is developed, and the minimum number of CHs necessary to satisfy the given reliability requirement is obtained. The data structures and fault-tolerant operations are described, and the energy consumption and the latency of the recovery of the SBDTM mechanism are quantitatively analysed. Simulations were carried out to compare the total network energy consumption, number of dead nodes, throughput, and packet loss rate of the proposed model with those of other methods presented in the literature. The simulation results show that the proposed SBDTM fault tolerance mechanism is superior to current models. This study presents important theoretical and application-based knowledge that can guarantee reliable data acquisition for IoT-based monitoring systems.

Precision Agriculture: A Remote Sensing Monitoring System Architecture

Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

Design of an Industrial IoT-Based Monitoring System for Power Substations

The Internet of Things (IoT) concept allows objects to share data through wired or wireless connections for communication purposes. The industrial Internet of Things (IIoT) is an extended concept of IoT that refers to an integration of data acquisition, communication, and processing on a real-time network. Currently, IIoT has been involved with the development of smart grids in many applications. As the operation of power systems is extremely time-critical, low-latency communication needs to be considered for most control and monitoring applications. Real-time capability of IoT is considered as a key feature for monitoring and control applications of power systems. Therefore, system operators can use the real-time monitoring system to provide better decisions for both technical- and financial-related matters. In this article, a high-speed IIoT-based monitoring system with recording functions is developed and implemented for a power system substation. Due to the high reliability and processing speed of field-programmable gate arrays (FPGAs), an FPGA-embedded controller is adopted in this system. The IoT platform also provides remote visualization for system operators in real-time. This article mainly aims to provide a practical application that was implemented and tested in a real-power substation. The system incorporates the features of an IoT platform with the needs of high-speed real-time applications while using a single high-resolution time source as the reference for both steady-state and transient conditions.

Smart sewing work measurement system using IoT-based power monitoring device and approximation algorithm

To enable Small and Medium-sized Enterprises (SMEs) level garment manufacturers to measure and monitor the work of individual workers without incurring a large financial burden, a smart sewing work measurement system was developed using an IoT-based power monitoring device and an approximation algorithm. The amount of electric current used in the sewing work was measured, and the measured data was transmitted to a server using Wi-Fi communication. The analysis of the data was conducted through the Symbolic Aggregate approximation (SAX) and Dynamic Time Warping (DTW) methods. The daily workload of each worker derived from the system developed through this study showed an error rate of 8% compared to the actual workload, and the time measured by the sensor was different from the time measured by a manager using a stopwatch. These differences are considered to be due to measurement errors in the stopwatch, human noise from the measurer and the operator, and the relatively few samples relative to the total workload. The IoT-based power monitoring and work measurement system for sewing work developed through this study can be applied to smart garment manufacturing factories at an acceptable cost level, while SMEs can realise high recognition rates and semi-real-time monitoring.

An IoT-based cognitive monitoring system for early plant disease forecast

In this paper, we develop an IoT-based monitoring system for precision agriculture applications such as epidemic disease control. Such an agricultural monitoring system provides environmental monitoring services that maintain the crop growing environment in an optimal status and early predicts the conditions that lead to epidemic disease outbreak. The agricultural monitoring system provides a service to store the environmental and soil information collected from a wireless sensor network installed in the planted area in a database. Furthermore, it allows users to monitor the environmental information about the planted crops in real-time through any Internet-enabled devices. We develop artificial intelligence and prediction algorithms to realize an expert system that allows the system to emulate the decision-making ability of a human expert regarding the diseases and issue warning messages to the users before the outbreak of the disease. Field experiments showed that the proposed system reduces the number of chemical applications, and hence, promotes agriculture products with no (or minimal) chemicals residues and high-quality crops. This platform is designed to be generic enough to be used with multiple plant diseases where the software architecture can handle different plant disease models or other precision agriculture applications.

Design and Implementation of Multi Input Hybrid Solar Three Levels Converter Using IOT

Multi input converters are highly reliable and can be effectively used for hybrid systems. Here the performance of a dual input system is experimentally verified by powering it with a solar and a battery and the whole system is monitored using IOT. A DC to DC converter which can operate with two sources is designed to operate with two input DC sources and the output DC supply is converted to AC by an inverter module. Power IOT based monitoring system is the main scope of this project which is achieved using an ESP8266 Arduino core and GSM technique. High conversion efficiency, soft switching realization and simple architecture are achieved in this system. The simulation results is presented under various values of operating conditions. A scale down model was developed which is powered by a 3Wp solar panel and a 12 V battery. The performance of the system was verified with various input conditions and the system parameters were monitored using IOT and the data samples were obtained.

Self-powered IoT Based Vibration Monitoring of Induction Motor for Diagnostic and Prediction Failure

The vibration of the motor provides information for failure diagnostic and prediction through signal processing. In this paper, we present an IoT-based monitoring system for recording the vibration of the induction motor. A 27mm piezoelectric is used to collect the vibration data from the motor. Each self-powered IoT node is supplied by the motor vibration its self by utilizing two 27mm piezoelectrics in series. We conducted an experiment with 3 scenarios, which aims to collect the vibration data from different positions, they are bottom, top and at the coupling. For each scenario, the speed of the induction motor is changed from low, middle to high speed. Based on our results, the proposed system is a success to monitor the motor vibration clearly. The results can display on the smartphone. Each node can send the recorded vibration to the cloud with an average delay of about 1 s.

IOT Based Monitoring System in Smart Agriculture

IOT Based Monitoring System in Smart Agriculture

Novel node deployment scheme and reliability quantitative analysis for anIoT-based monitoring system

The Internet of things (IoT) is highly suitable for military, environmental, agricultural, and other remote real-time monitoring applications. A reliable topology ensures a stable and dependable monitoring system. Considering the research of an IoT-based air pollution monitoring system for industrial emissions as background, this study proposes a novel dual redundant node deployment scheme. Specifically, hexagonal clustering is proposed for the internal regions. In addition, relationship and quantification formulas for a monitoring area are presented, and the communication range, total number of layers of the topology, and number of cluster headers are determined. Interruptions in a monitoring system may reduce the quality of IoT services. Therefore, sensor nodes are also deployed around the monitoring area (in outer regions). The basic external area is modeled and proposed as a rectangular, 1-covered isosceles triangle deployment plan with the objective of minimizing the number of sensor nodes. A quantitative formula is given using the sensing radius, width of the rectangle, and adjacent distance between nodes. Reliability is a critical index in IoT-based applications. Thus, the reliability of a hexagonal clustering topology is presented using reliability block diagrams. For a reliable remote transmission model, different redundant systems are implemented. Furthermore, the reliability value and mean time to failures are calculated. The results are later compared and analyzed quantitatively. This study presents important theoretical and application-based knowledge that can guarantee reliable service for an IoT-based monitoring system.

Medical Assistant Chat-Bot for Health Care Application

IoT revolution is re-designing modern health care with high technological, economic, and social prospects. Artificial intelligence (AI) aims to substitute human cognitive functions. It gives a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. The internet of things has lots of applications in healthcare, from remote monitoring to smart sensors and medical device integration. It has the ability to not only keep patients safe and healthy but to improve how physicians deliver care as well. Healthcare IoT can also boost patient engagement and satisfaction by allowing patients to spend more time interacting with their doctors. With the combination of both IoT & AI technologies, it can apply chatbots for medical assistance in healthcare. An IoT based monitoring system & AI based analytics system with an interactive chat-robots is the more outstanding application in healthcare. The Medical Assistant recognizes the user voice input and converts the speech into text. Here we concentrate on the different type of fevers, like chickenpox, malaria, septicemia, viral fever etc. Each fever has different symptoms .we finalize the fever by using symptoms. After that text mining, those phrases would be split as a noun and medical terms. From term analysis, the assistant will answer the query from users. It also analyzes the sensor data (body temp, heartbeat) from the cloud and expresses the user health condition.

Soil moisture sensors for sustainable irrigation: comparison and calibration

Soil moisture is a vital component for plant growth. There are many electro-chemical low-cost sensors that sense soil moisture with immediate effect rather than other methods such as gravimetric method and others that are expensive as well as consume lot of time. Some of these electro-chemical sensors work on resistivity whereas some sensors work on capacitance. This paper compares the efficiency of resistivity-based sensors with capacitance-based soil moisture sensors so that farmers and experts can use the better one for designing IoT-based irrigation monitoring and controller system. This comparison is performed with 21 samples having different levels of soil water content. The coefficient of variation is calculated to compare the efficiency of both the sensors. After the comparison, calibration has also been performed that depicts the soil moisture content to LCD display and alert the farmer by a buzzer.

A Design of IoT-based Monitoring System for Intelligence Indoor Micro-Climate Horticulture Farming in Indonesia

Recently, horticulture farming, especially in Indonesia, are facing various obstacles, including the lack of young farmers regeneration, agricultural land decreasing, and climate change. Climate change increase extreme and uncertain weather for horticulture cultivation. Farmers frequently face frequent damage to crops and decreasing in quantity and quality of plants. The development of a monitoring system that can help farmers grow crops is a crucial issue. In this paper, a study for a system for monitoring the performance of indoor micro-climate horticulture is presented. An electronic sensors Internet-of-Things (IoT) board is implemented to monitor the cultivation process. The sensor reading is collected into a database. The database is also integrated with the Indonesian Meteorological Agency data which consists of weather data and daily at the cultivation location. For our future work, the collected data are expected to be used for training machine learning models for the development of intelligence automated indoor micro-climate horticulture.

실시간 감시를 통한 교통신호제어기의 열화 감지

Purpose: A traffic signal controller needs to control and coordinate to ensure that traffic and pedestrians move as smoothly as possible. Since a traffic signal controller has a significant impact on the safety of vehicles and pedestrians, it is important to monitor the failure and deterioration of the traffic signal controller. The purpose of this paper is to propose an IoT (Internet of Things)-based monitoring system for a traffic signal controller.BRMethods: Every traffic signal controller has a nominal system trajectory specified when it is deployed. The proposed IoT-based monitoring system collects the system trajectory information through real-time monitoring. By comparing the nominal system trajectory and the monitored system trajectory, we are able to detect the failure and deterioration of the traffic signal controller.BRConclusion: The proposed IoT-based monitoring system can contribute to the safety of vehicles and pedestrians by maximizing the availability of a traffic signal controller.

Greenhouse gas emission monitoring system for manufacturing prefabricated components

Abstract Excessive greenhouse gas (GHG) emissions from construction stage pose an obvious and emerging challenge. Most previous studies on emissions from the construction phase only focused on emission prediction in advance of the actual construction activities or on quantitative analysis after building construction. A system that enables builders to monitor emissions from construction activities in real time is still lacked, although GHG emission monitoring (GEM) and analysis are considered as the top activities that must be performed to minimize excessive environmental impacts. As an initial exploration, this paper proposes a GEM system based on Internet of things (IoT) technology to real-time monitor emissions when manufacturing prefabricated components. In this system, Radio Frequency Identification (RFID) sensors are adopted to identify the component ID, and the corresponding material usage data are extracted from a database that is preset in the GEM system. Laser sensors are installed in the components production line to measure the running time of equipment so that energy usage can be calculated in real time. In addition, a data service platform was developed to implement wireless data transmission from production line to computing platform, where the monitoring results are visually presented. A production line of real-life prefabricated components in China is adopted to demonstrate that the IoT-based monitoring system can acquire and analyze real-time carbon emission data from the manufacturing process. The results indicate that GEM system can facilitate project teams to timely control irregular emissions, identify potential emission risks and explore possible strategies for minimizing carbon emissions in the construction sector.

IoT based Monitoring System for Drought Prediction

IoT based Monitoring System for Drought Prediction

IoT (Internet of Things) & Big Data Solutions to Boost Yield and Reduce Waste in Farming

“If you ate today, thank a farmer” The food on your table is made possible by farmers. NCRB (National Crime Records Bureau) claims 46 farmers commit suicide every day in India. Today’s farmers are facing a set of difficult challenges — an increasing worldwide demand for food, a changing climate, and a limited supply of water. This is a global problem that will likely require a global, apolitical solution; no single industry is able to unilaterally ensure that no one goes hungry. To reduce these hurdles, the agriculture industry is adopting an array of digital solutions. One of the newest trends in agriculture is using technologies like big data analytics and IoT in order to make smarter and accurate decisions, reduce costs, and boost yield of the crop. The development of an automated system is very useful for any farmer, with alerts and monitoring processes. IoT sensors report weather conditions and monitor soil moisture and acidity via embedded devices. This paper aims to develop the automated system to monitor farming using IoT sensors and other approaches. This paper will aims on designing the IoT-based monitoring system to analyze crop environment, and the method to improve the efficiency of decision making by analyzing harvesting data.