Everything in its way to be computerized and most of the objects are coming to be smart in present days. Modern Internet of Thing (IoT) allows these objects to be on the network by using IoT platforms. IoT is a smart information society that consists of smart devices; these devices can communicate with each other without human's intervention. IoT systems require flexible platforms. Through the use of Field Programmable Gate Array (FPGA), IoT devices can interface with the outside world easily with low power consumption, low latency, and best determinism. FPGAs provide System on Chip (SoC) technique due to FPGAs scalability which enables the designer to implement and integrate large number of hardware clocks at single chip. FPGA can be deemed as a special purpose reprogrammable processor since it can process signals at its input pins, manipulate them, and give off signals on the output pins. In this paper, using FPGA for IoT is the limelight.
The monitoring of COVID-19 patients has been greatly aided by the Internet of Things (IoT). Vital signs, symptoms, and mobility data can be gathered and analyzed by IoT devices, including wearables, sensors, and cameras. This information can be utilized to spot early infection symptoms, monitor the illness’s development, and stop the virus from spreading. It’s critical to take vital signs of hospitalized patients in order to assess their health. Although early warning scores are often calculated three times a day, they might not indicate decompensation symptoms right away. Death rates are higher when deterioration is not properly diagnosed. By employing wearable technology, these ongoing assessments may be able to spot clinical deterioration early and facilitate prompt therapies. This research describes the use of Internet of Things (IoT) to follow fatal events in high-risk COVID-19 patients. These patients’ vital signs, which include blood pressure, heart rate, respiration rate, blood oxygen level, and fever, are taken and fed to a central server on a regular basis so that information may be processed, stored, and published instantly. After processing, the data is utilized to monitor the patients’ condition and send Short Message Service (SMS) alerts when the patients’ vital signs rise above predetermined thresholds. The system’s design, which is based on two ESP32 controllers, sensors for the vital signs listed above, and a gateway, provides real-time reports, high-risk alerts, and patient status information. Clinicians, the patient’s family, or any other authorized person can keep an eye on and follow the patient’s status at any time and from any location. The main contribution in this work is the designed algorithm used in the gateway and the manner in which this gateway collects, analyze, process, and send the patient’s data to the IoT server from one side and the manner in which the gateway deals with the IoT server in the other side. The proposed method leads to reduce the cost and the time the system it takes to get the patient’s status report.
Gas or liquefied petroleum gas (LPG) is a chemical substance resultant from petroleum and could be dangerous in industrial places or those that deal with this substance. Gas leakage causes many health issues. So, to prevent such catastrophes and in order to maintain a clean air environment, the workspace atmosphere should be frequently monitored and controlled. The proposed monitoring gas leakage detector system is based on Internet of Things (IoT) technology. NodeMCU ESP8266 Wi-Fi is used to be the microcontroller for the whole system. The combustible gas sensor (MQ2) is used in order to detect the presence of methane (CH4) and carbon monoxide gas (CO). MQ2 sensor will detect the concentration of the gas according to the voltage output of the sensor and the ESP8266 will send the data reading from the gas sensor to Blynk IoT platform over an IOS phone; data visualization is done using Thingspeak IoT Platform. Besides, a fan will immediately work upon the leakage occurs along with an alarming buzzer.
Nowadays, the Wireless Sensor Network (WSN) has materialized its working areas, including environmental engineering, agriculture sector, industrial, business applications, military, intelligent buildings, etc. Sensor networks emerge as an attractive technology with great promise for the future. Indeed, issues remain to be resolved in the areas of coverage and deployment, scalability, service quality, size, energy consumption and security. The purpose of this paper is to present the integration of WSNs for IoT networks with the intention of exchanging information, applying security and configuration. These aspects are the challenges of network construction in which authentication, confidentiality, availability, integrity, network development. This review sheds some light on the potential integration challenges imposed by the integration of WSNs for IoT, which are reflected in the difference in traffic features.
Over the previous decade, significant research has been conducted in the field of healthcare services and their technological advancement. To be more precise, the Internet of Things (IoT) has demonstrated potential for connecting numerous medical devices, sensors, and healthcare professionals in order to deliver high-quality medical services in remote locations. This has resulted in an increase in patient safety, a decrease in healthcare expenses, an increase in the healthcare services' accessibility, and an increase in the industry's healthcare operational efficiency. This paper provides an overview of the possible healthcare uses of Internet of Things (IoT)-based technologies. The evolution of the HIoT application has been discussed in this article in terms of enabling technology, services of healthcare, and applications for resolving different healthcare challenges. Additionally, effort difficulties and drawbacks with the HIoT system are explored. In summary, this study provides a complete source of information on the many applications of HIoT together the purpose is to help future academics who are interested in working in the field and making advances gain knowledge into the issue.
The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application-specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water- resource utilization in the precision farming landscape. This paper presents an open-source technology-based smart system to predict the irrigation requirements of a field using the sensing of ground parameters like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, air temperature, and relative humidity of the crop field. This mainly focused on wastage of water, which is a major concern of the modern era. It is also time-saving, allows a user to monitor environmental data for agriculture using a web browser and Email, cost-effectiveness, environmental protection, low maintenance and operating cost and efficient irrigation service. The proposed system is made up of two parts: hardware and software. The hardware consists of a Base Station Unit (BSU) and several Terminal Nodes (TNs). The software is made up of the programming of the Wi-Fi network and the system protocol. In this paper, an MQTT (Message Queue Telemetry Transportation) broker was built on the BSU and TU board.
This paper discusses the design and performance of a frequency reconfigurable antenna for Internet of Things (IoT) applications. The antenna is designed to operate on multiple frequency bands and be reconfigurable to adjust to different communication standards and environmental conditions. The antenna design consists of monopole with one PIN diode and 50Ωfeed line. By changing the states of the diode, the antenna can be reconfigured to operate in a dual-band mode and a wideband mode. The performance of the antenna was evaluated through simulation. The antenna demonstrated good impedance matching, acceptable gain, and stable radiation patterns across the different frequency bands. The antenna has compact dimensions of (26×19×1.6) mm3. It covers the frequency range 2.95 GHz -8.2 GHz, while the coverage of the dual- band mode is (2.7-3.8) GHz and (4.57-7.4) GHz. The peak gain is 1.57 dBi for the wideband mode with omnidirectional radiation pattern. On the other hand, the peak gain of the dual-band mode is 0.87 dBi at 3 GHz and 0.47 dBi at 6 GHz with an omnidirectional radiation pattern too.
Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country's population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.
In smart cities, health care, industrial production, and many other fields, the Internet of Things (IoT) have had significant success. Protected agriculture has numerous IoT applications, a highly effective style of modern agriculture development that uses artificial ways to manipulate climatic parameters such as temperature to create ideal circumstances for the growth of animals and plants. Convolutional Neural Networks (CNNs) is a deep learning approach that has made significant progress in image processing. From 2016 to the present, various applications for the automatic diagnosis of agricultural diseases, identifying plant pests, predicting the number of crops, etc., have been developed. This paper involves a presentation of the Internet of Things system in agriculture and its deep learning applications. It summarizes the most essential sensors used and methods of communication between them, in addition to the most important deep learning algorithms devoted to intelligent agriculture.
There are many serious accidents on human life caused by electric current columns, and it is possible for the Internet of Things to find solutions to prevent the risks that occur, as in many fields such as medicine, agriculture, industry and others. In this paper, we will show monitoring and tracking of the current that passes through the electrical poles and the possible leakage, in addition to monitoring the temperature and humidity in the area, and knowing the condition of the light in the column according to morning and evening, this proposed system that will perform a general purpose added to the region. using Open source NODE MCU board, GPS positioning, current sensor, temperature and humidity sensor that provide desired data via open source platforms that we have chosen to be ThingSpeak that easily to handle.
Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.
This article presents a power-efficient low noise amplifier (LNA) with high gain and low noise figure (NF) dedicated to satellite communications at a frequency of 435 MHz. LNAs’ gain and NF play a significant role in the designs for satellite ground terminals seeking high amplification and maintaining a high signal-to-noise ratio (SNR). The proposed design utilized the transistor (BFP840ESD) to achieve a low NF of 0.459 dB and a high-power gain of 26.149 dB. The study carries out the LNA design procedure, from biasing the transistor, testing its stability at the operation frequency, and finally terminating the appropriate matching networks. In addition to the achieved high gain and low NF, the proposed LNA consumes as low power as only 2 mW.
The use of smart network applications based on the Internet of Things is increasing, which increases the attractiveness of malicious activities, leading to the need to increase the adequate security of these networks. In this paper, the latest recent breakthroughs in blockchain for the Internet of Things are examined in the context of electronic health (e-health), smart cities, smart transportation, and other applications in this article. Research gaps and possible solutions are discussed, such as security, connection, transparency, privacy, and the IoT's blockchain regulatory challenges. In addition, the most important consensus algorithms used in the blockchain have been discussed, including Proof of Work, Proof of Stake, and Proof of Authority, each of which operates within certain rules.
in recent years popularity of smart Home has been increasing due to low price and simplicity through tablet and Smartphone connectivity. It is an automation of house or home activity. Raspberry Pi3 is a small computer with digital input output capability and it was introduced in 2016; input/output ability besides the availability of all computer features make this system very suitable to be central unit can for smart home. Smart Home may contain centralize controller which control heating, lightning, ventilation in the home, HAVC( Heating, Ventilation and air conditioning),Safety locks of gates, doors and other system to provide improve comfort, better energy efficiency and security. The aim of this Paper is to develop a smart home application using RPi3, wemose-d1 and GSM. Programming has been developed in C++ in wemose-d1 and Python environment for RPi3 operation. The MQTT (Message Queuing Telemetry Transport protocol) technologic used to connect between raspberry pi3 and nodes.