The efficient and continuous operation of industrial motors is crucial to the success of many production procedures. Using Support Vector Machines (SVM) and Internet of Things (IoT) connection, this research introduces a novel method for smart condition monitoring of industrial motors. To create a system capable of real-time motor defect detection and prediction, facilitating preventative maintenance and decreasing downtime. The suggested system incorporates IoT sensors to gather data from industrial motors, such as temperature, vibration, current, and voltage. SVM, a potent machine learning technology, is used to analyze and evaluate this sensor information; it excels at classification and regression. Using SVM, a prediction model for spotting motor defect patterns is developed. The system can effectively predict motor defects such as misalignment, bearing wear, and electrical abnormalities. The system uses an IoT connection to provide real-time monitoring and notifications to maintenance workers whenever abnormalities are identified, allowing for preventive maintenance measures. As a result, maintenance costs and unscheduled downtime may be reduced while overall dependability and efficiency in industrial motor operations are improved.
In the digital revolution of agriculture, Internet of Things (IoT) technology and smart Decision Tree (DL) Algorithms may help control soil-borne diseases. This research proposes using cloud-connected technologies to combat soil-borne illnesses in agriculture. IoT devices gather real-time data and cloud computing processes in the suggested system and store it efficiently and a DL algorithm makes intelligent decisions. IoT sensors in the field monitor surrounding factors, pathogen prevalence for soil health. A DL algorithm examines the data on a cloud platform to deliver actionable insights. For soil-borne pathogen prediction and management, the DT algorithm considers soil moisture, temperature, and historical disease trends. Farmers get fast and data-driven decision assistance from the cloud-connected architecture's device connectivity and remote access. Simulation and real-world tests show that the suggested strategy reduces agricultural production lost to soil-borne illnesses. Increased disease prediction accuracy and tailored treatment suggestions increase crop health and sustainable agriculture. The effective and scalable soil-borne pathogen control method presented in this study to smart agriculture. IoT and DT algorithms enable proactive disease management and provide the groundwork for precision agriculture and data-driven farming.
This paper discusses using Internet of Things (IoT) technology to improve workplace safety via real-time monitoring and danger identification, addressing occupational health issues. IoT devices and sensors are used in more sectors to make workplaces safer. A network of sensors may gather real-time data from various workplace locations to monitor ambient conditions, equipment functioning, and personnel actions. The system develops a comprehensive IoT infrastructure for occupational safety. This infrastructure includes environmental sensors for temperature, humidity, air quality, and noise. Wearable sensors are also investigated for worker vital signs and mobility. The data is sent to a central platform where powerful analytics and IoT discover dangers, abnormalities, and hazardous trends. Integration of danger detection techniques with real-time warnings and notifications is vital to the analysis. Hazardous circumstances trigger quick alerts for workers and supervisors. This proactive strategy allows quick risk mitigation and accident prevention. The system also addresses data privacy and security in the IoT framework, suggesting methods for protecting sensitive data while enabling data exchange.
The future of productive indoor farming is approaching us, and this paper investigates the synergistic merging of Internet of Things (IoT) technologies with aquaponics and hydroponics. The water-based technologies and climate control used in these innovative plant and aquatic life production methods allow for simultaneous care. Using IoT capabilities such as real-time monitoring, data analysis, and automated control significantly improves these systems. The technology controls essential conditions, including lighting, climate, humidity, nutrient concentration, and water quality. The ultimate potential is in ecologically friendly settings with dramatically improved resource efficiency, increased agricultural yields, and reduced traditional farming limits. IoT-enabled aquaponics and hydroponics may revolutionize the capacity to address food security issues, foster sustainability, and expand the boundaries of indoor agriculture. Together, these trends point the way to a brighter future in agriculture that can successfully combine science and nature to provide food for the world's growing population.
Personalized suggestions may enhance the use of online food shopping, an already popular and handy service. But data sparsity and scalability problems restrict the current recommendation systems rely on user-based collaborative filtering algorithms. Smart grocery delivery with personalized suggestions is proposed in this system using item-based collaborative filtering algorithms and Internet of Things (IoT) technologies. In the system, smart appliances like fridges and scales track food supplies and consumption, and then, according to the user's preferences, they automatically place purchases on internet platforms. It makes use of item-based collaborative filtering algorithms to examine other users' evaluations and purchase histories in order to provide the user with relevant and varied product recommendations. The method is tested on a real-world dataset and contrasted with collaborative filtering methods rely on user input. Techniques outperform the competition in terms of suggestion accuracy, variety, and coverage while simultaneously decreasing delivery time and costs. By implementing our strategy, not only can online grocery shopping be made more enjoyable for users, but it can also spur the growth of e-commerce and the IoT.
The economic sector includes agriculture as a major player. Around the world, the primary issue and hot topic is agriculture automation. As a result of the population boom, there is a huge increase in the need for both jobs and food. Farmers employed traditional techniques, which included insufficient to meet these needs. This led to the introduction of new automated techniques. These innovative techniques supplied the world's food needs while giving billions of people access to jobs. An agricultural revolution has been sparked by artificial intelligence. With the use of this technology, crop yields are now more resistant to many conditions like population increase, climate change, job troubles, and food security concerns. The primary goal of this study is to evaluate the numerous ways artificial intelligence is being used in agriculture, including the use of sensors and other tools built into drones and robots for weeding, spraying, and irrigation. The excessive use of pesticides, herbicides, water, and other resources is reduced by these technologies, and the soil fertility is maintained. They also aid in the effective use of labor, which raises productivity and quality. In order to provide a concise summary of the current state of agricultural automation, including the use of drones and robots for weeding, this study analyses the research of several researchers. Along with two automated weeding strategies, the various ways of soil water sensing are described. This study discusses the use of drones as well as the numerous spraying and crop monitoring techniques that may be utilized with them.
In this research, a power consumption analysis of wireless devices for Internet of Things applications is described. The research analyzes and contrasts a variety of tiny wireless communication techniques and their modules, including Zig-Bee, Energy Saver Wi-Fi, Six-Low-PAN, and LPWA, all of which aim to conserve energy and lengthen the lifespan of the devices that make up an IoT network. This focuses on the significance of employing small wireless techniques and components in IoT applications. The study's methodology is defined by the individual module used to implement the protocol. According to the degree of communication between sensor nodes, the proposed protocols are categorized. ZigBee, 6LoWPAN, and low power Wi-Fi are the candidate protocols for connectivity over short distances. The LoRaWAN protocol is a possibility for long-distance connectivity. Given the wide variation in power consumption between modules and protocols, the results of this study demonstrate how carefully selecting units for every protocol can greatly affect the duration of its use. Accordingly, protocols are compared with one another in various ways based on the module in question.
Nowadays, people often withdraw money from Automated Teller Machines (ATMs). Every user receives a unique card and personal identification code to perform all transactions secretly and anonymously. Developing an ATM crime prevention system is crucial to avoid theft. The proposed solution uses an embedded system using a Raspberry Pi to process the real-time data collected by the vibration sensor. In this instance, robberies are detected using a vibration sensor that hears buzzer sounds and senses vibration. The sensor provides information to a police station through the Internet of Things, and the main doors corresponding to the ATM close on their own so that the thief cannot be escaped. An IoT transmits data to a Wi-Fi module through a cloud server, which displays it in real-time. The mechanism alerts the bank staff automatically when an ATM is misplaced. Also, the proposed system uses cameras since they help us find theft suspects.
It is crucial to provide ultrareliable and low-latency communication (URLLC) for 5G wireless networks and above, this topic is now attracting a lot of interest in both business and academia. The fundamental requirement of URLLC is a change from conventional utility-based network design methods, where reliance on average numbers is no longer a choice but a requirement. Rather, there is a need for a comprehensive and systematic paradigm that considers factors like variability, packet size, network design, and topology. This research's main objective is the first stage toward bridging this gap. After defining latency and dependability, we carefully explore potential URLLC enablers and the trade-offs they entail in order to go toward this objective. Then, we concentrate on a wide range of processes and approaches related to URLLC's specifications and also how they apply to certain use cases. These findings offer clear guidance for the development of wireless networks with minimal latency and great dependability.
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