Things Technology Research Articles

Research on the Impact of Internet of Things (IoT) Technology on Logistics Warehouse Management

As a consequence of the evolution of the times, Internet of Things technology has made remarkable progress in the field of logistics and warehouse management. Its application promotes the intelligence of logistics management. This has the effect of markedly enhancing the efficiency of logistics management, thereby facilitating the accelerated growth of the logistics industry. This paper begins by examining the current state of logistics warehouse management and the fundamental principles of IoT technology and its application in the field of logistics. It then proceeds to discuss the challenges faced by traditional logistics warehouse management in China. This paper analyses how the application of IoT technology can significantly improve the efficiency of logistics management, and provides an in-depth discussion on the development trend of logistics warehouse management. The objective of this paper is to illustrate the pivotal role of IoT technology in logistics warehouse management and to highlight its beneficial influence on the prospective evolution of the logistics industry.

Real-time rainfall projection in hilly areas using ARLSTM model: a case of Dharamshala, India

Abstract Rainfall forecasting is essential in many industries, including agriculture, hydrology, urban planning, disaster management, and renewable energy. However, typical forecasting approaches frequently fail to reflect the complex spatiotemporal dynamics inherent in rainfall patterns, particularly in hilly areas. Since, the mountainous terrain influences wind patterns, moisture flow, and temperature inversions which affect precipitation patterns, adding challenge to meteorologists. So, designing a predictive forecasting model is essential for people and governments to make preventive measures in time. In recent years, the introduction of Internet of Things (IoT) technology and advances in machine learning (ML) techniques have provided a promising route for improving rainfall prediction accuracy. Real-time data on environmental elements such as temperature, wind speed, air pressure, and humidity can be collected using IoT sensor networks. This research paper investigates the efficacy of the Auto-Regressive Integrated Moving Average - Long Short-Term Memory (ARIMA-LSTM) model for real-time rainfall prediction in hilly towns like Dharamshala. Utilizing real-time data collected from sensors and using edge to preprocess it before sending it to the cloud the study explores the integration of traditional time series analysis techniques with deep learning architectures to enhance predictive accuracy. The propounded model is paralleled against alternative forecasting approaches and shows a significant reduction in RMSE, RRMSE, and MSE. Furthermore, it had a 0% Absolute Percentage Bias (APB), a high Nash-Sutcliffe Efficiency (NSE) of 0.99, and a low Theil's coefficient (U2) of 0.01. These results demonstrate that the ARLSTM model outperforms other forecasting models in terms of rainfall prediction.

IoT Innovation and Entrepreneurship Education: Sustainable Tilapia Cultivation Optimization Strategy

Tilapia cultivation in Teratak Village, Central Lombok, faces problems in feed efficiency and limited market access. Lack of automation technology causes feed waste, while limited marketing knowledge limits farmers' competitiveness. This community service activity aims to overcome these problems through the application of Internet of Things (IoT) technology in the form of automatic feeders (smart feeders) and entrepreneurship training focused on digital marketing. The methods applied include the manufacture and installation of smart fish feeders that are controlled via an application to regulate the amount and frequency of feed. In addition, entrepreneurship training focuses on digital marketing strategies through social media and e-commerce platforms to expand market reach. The results of the activity show that smart feeders increase feed efficiency by up to 20%, reduce waste, and support more optimal fish growth. Digital marketing training helps farmers improve their online promotion skills, expand market networks, and significantly increase sales. In conclusion, the application of IoT technology and digital marketing-based entrepreneurship training has proven effective in increasing the efficiency of cultivation and market competitiveness of tilapia farmers in Teratak Village

Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis

The Internet of Things (IoT) technology has emerged as a vital driver across various fields, engaging businesses, platforms, and industries. IoT involves a holistic ecosystem and a value chain that necessitates the evaluation of impactful dimensions for successful implementation. This research employs the TISM method to identify driver and dependent criteria regarding IoT implementation readiness and uses the fuzzy TOPSIS method to rank these criteria. In the initial step, 15 criteria were identified through a review of previous studies. The TISM results reveal five levels reflecting the driver power and dependence of the criteria. Based on these results, “Implementation Knowledge and Expertise (C2)”, “Technical and Infrastructural Readiness (C9)” and “Financial and Investment Readiness (C12)” were placed at level 5, marking them as the most driver criteria. Additionally, “Implementation Roadmap (C8)” was identified as the most dependent criterion at level one. According to the fuzzy TOPSIS results, “Senior Management Support (C6)”, “IoT Usage Culture (C1)”, “Business Model Development Capability (C15)”, “Financial and Investment Readiness (C12)” and “Technical and Infrastructural Readiness (C9)” ranked first to fifth, respectively. The combined results provide valuable insights for decision-makers and stakeholders involved in IoT implementation. By determining driver and dependent levels and ranking the criteria, industries can effectively prepare for the successful implementation of IoT.

Arduino-Based Moringa Cultivation and Innovation in Processed Moringa Products to Prevent Stunting, Cangkring Village, Jenggawah District, Jember Regency

The industrial era 4.0 is a new phase in the development of technology in agriculture. Technologies such as drones, sensors, cloud computing, and agricultural automation are examples of the use of the Internet of Things (IoT) as technological advances in agriculture. Moringa is rich in beneficial nutrients, especially for pregnant and lactating mothers so that it can prevent stunting. Internet of Things (IoT) technology is applied to monitor the growth conditions of Moringa in real time, increasing harvest yields in terms of quality and quantity. This program has proven effective in increasing the understanding of the Cangkring village community and can be used as a way to prevent stunting, as well as improve the quality of life in areas with a high prevalence of stunting

Public Room Door Locking System Using Wireless Technology and Internet of Things

Enhancing the efficiency and security of public spaces has become increasingly important, leading to the development of an automatic door lock system utilizing wireless technology and the Internet of Things (IoT). This study aims to design and implement a smart door lock system that provides automated control and improves convenience in managing public room access. The research methodology involves programming the ESP32 microcontroller, integrating IoT applications, and conducting various testing methods. These include manual control through physical switches, automated control via the Sinric Pro platform, and Wi-Fi hotspot-based control using the ESP32 module. Comprehensive hardware and software testing was carried out to ensure system reliability and functionality. The results indicate that the proposed system enables seamless control of public room doors through multiple methods, including an Android-based application and Wi-Fi connectivity. By leveraging IoT technology, the system offers a user-friendly, efficient, and secure solution for managing public space access, addressing modern requirements for convenience and safety.

Research and design of multiple gas mixing system in modified atmosphere packaging

Despite the abundant production of fruits and vegetables in Xinjiang, there is an increasing trend in waste rates. Gas-controlled packaging techniques are employed post-harvest to enhance freshness and extend shelf life. However, the multi-gas mixing system at the core of this equipment often encounters challenges such as insufficient stability, limited precision, high costs, and inadequate intelligence. In this context, we have developed and designed a multi-gas mixing system for gas-conditioned packaging. The system integrates Internet of Things technology, uses a programmable controller as the core, proposes a ternary gas distribution model based on the ideal gas equation and Dalton’s law of partial pressures, and utilizes RS-485 bus, TCP/IP protocol, among others. This ensures communication between the equipment and data transmission with the cloud server while enabling remote control of the gas mixing and blending process via a mobile phone terminal. The system runs stably through testing, with the average relative deviations of CO2 and O2 gas concentrations being 0.86% and 0.72%, respectively. This fulfills the technical prerequisites for achieving a gas concentration precision of less than 1%, addressing challenges related to inadequate precision in gas blending, unstable performance, and limited visual representation. Moreover, it significantly reduces equipment costs and operational complexities while serving as a valuable reference for advancing multivariate gas mixing and blending technology in Xinjiang’s fruit and vegetable gas-conditioned packaging.

Cobalt Ferrite@Barium titanate core-shell nanoparticles empowered triboelectric electromagnetic coupled nanogenerator for self-powered electronics

In pursuing sustainable energy solutions for wearable technology and Internet of Things (IoT) devices, achieving high power in self-powered electronics is a significant challenge. This study introduces a novel hybrid nanogenerator by integrating a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to achieve both high voltage and high current outputs simultaneously. The cobalt ferrite barium titanate (CoFe2O4@ BaTiO3) core–shell nanoparticles (NPs) were chosen for their unique ability to enhance the system’s performance by combining triboelectric, piezoelectric, and electromagnetic properties. The BaTiO3 shell exhibited robust triboelectric and piezoelectric properties essential for TENG operation, while the CoFe2O4 core, influenced by the EMG’s magnetic field, induces additional static charges, significantly boosting overall energy conversion efficiency. The TENG and EMG in the combined system operate together in a contact-separation mode, where their in-phase output signals are seamlessly combined via a double bridge rectifier, resulting in a robust power output of 37.7 mW and a power density of 1.86 mW/cm2, with cycling stability of over 10,000 cycles. The uniquely designed TENG-EMG combined nanogenerator (C-TENG) exhibited striking adaptability, effectively operating across different low-to-high frequencies and load situations while constantly powering portable electronic devices. This innovative approach meets the high energy demands of modern self-powered electronics with exceptional efficiency, durability, and high-power density, positioning it as a versatile solution for wearable biomechanical energy harvesting.

Urban Small-Scale Hydroponics: A Compact, Smart Home-based Hydroponics System

The increasing population and urbanization have created a massive gap in the demand-supply model of food grains. The world is facing an acute problem with global warming and EI Nino effects, which have affected the equilibrium of the food chain. It is a need of the hour to introduce new reforms in farming to reap increased yields and reduce dependency on natural resources. Hydroponics cultivation is a boon to the agricultural sector, it enhances the cultivation of plants in an organic way by enabling Internet of Things (IoT) technology and combines technology and conventional nutritional mechanisms to enable the co-plant's growth without the strain of nutrient deficiency. This research suggests a system that integrates hardware and software into the traditional hydroponics system that the users can use to have their plantation setup in an urban environment within a small and confined space at home. This system will benefit hobbyist gardeners and small-scale urban farmers seeking an efficient, compact, and smart solution for hydroponic plant cultivation.

Implementation of a Web-Based Master-Slave Architecture for Greenhouse Monitoring Systems in Grape Cultivation

The Internet of Things (IoT) technology enables electronic devices to connect to the Internet for real-time data collection and analysis. In greenhouses, IoT is used to monitor soil moisture and environmental conditions to support grape plant care. This study proposes a grape plant monitoring system using a master-slave architecture and the ESP-NOW protocol to reduce reliance on Wi-Fi networks, thus minimizing delay and packet loss. The system leverages direct communication between master and slave nodes. Testing results show an average delay of 1,546.65 ms, jitter of 120.56 ms, and packet loss of only 0.07% from 88,815 data transmissions in one day. Despite variations in packet loss due to power interruptions, the system consistently demonstrates reliable data transmission. Overall, this system proves to be reliable for real-time monitoring in greenhouses, offering stable performance and high data accuracy.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Water Level and Flow Detection System: An IoT-Based Flood Monitoring Application

In Malaysia, floods have become increasingly prevalent, causing significant damage to various aspects of life including infrastructure, property and the economy. The risk to human lives is particularly high in low-lying areas and areas with poor drainage systems. Water contamination during floods can also lead to health issues and the spread of waterborne diseases. The psychological impact of floods should not be underestimated, as individuals may experience a range of emotions such as frustration, sadness, fear and anger. To address these challenges, this study focuses on developing a flood alert system using Internet of Things (IoT) technology. By utilizing ESP32 microcontroller, flow sensor, float sensors, water flow as well as water level can be accurately measured and users can be alerted through the Blynk application. The system ensures accuracy in measuring flow rates, with recorded values ranging from 2.404 L/min to 2.444 L/min. This device can measure water flow and water level which will be generated through ESP32 microcontroller and notifying the user via the Blynk application as for the IoT part. This project has been carried out in indoor experiments to evaluate the efficiency of the float and flow sensors, respectively. Through indoor experiments, it has been demonstrated that this system is capable of effectively monitoring and alerting users to potential flood incidents, helping to safeguard lives and property.

Trash Tide Collecting System using GSM and GPS - IOT

The accumulation of unmanaged waste poses significant health and environmental risks, especially in rapidly growing urban areas. This paper presents a smart waste monitoring system that leverages Internet of Things (IoT) technology to track waste levels in real-time and improve the efficiency of waste collection. By using ultrasonic sensors connected to IoTenabled devices, this system detects waste levels in garbage bins and sends notifications when bins require servicing. These alerts help in timely waste management, reducing overflowing bins and enhancing public cleanliness. This model’s practical implementation can contribute to a cleaner environment and support smart city initiatives

INVESTIGATION OF TECHNICAL FEASIBILITY AND EFFICIENCY OF Wi-Fi BASED INTERNET OF THINGS REMOTE MONITORING AND CONTROL OF HOME APPLIANCES

The proliferation of Internet of Things (IoT) technology has transformed the way we interact with home appliances. This study investigates the technical feasibility and energy efficiency of Wi-Fi-based IoT remote monitoring and control of home appliances. A prototype system was designed and developed using Wi-Fi-enabled microcontrollers, sensors, and actuators to monitor and control various appliances remotely through a mobile application. Experimental results show that the system achieves an average energy savings of 23.4% and reduces appliance standby power consumption by 91.2%. The system's technical feasibility was evaluated through reliability, scalability, and security assessments. Results indicate high reliability (99.85%), scalability (supporting up to 50 devices), and robust security. Energy efficiency was analyzed using Energy Consumption Analysis (ECA) methods. The study concludes that Wi-Fi-based IoT remote monitoring and control of home appliances is technically feasible and energy-efficient, offering significant potential for reducing energy consumption and carbon emissions.

Review Paper on Transforming Care for Paralysis Patients through IOT Innovation

The integration of Internet of Things (IoT) technology is revolutionizing care for paralysis patients, significantly enhancing their quality of life, autonomy, and independence. By harnessing the power of wearable sensors, smart home automation, Artificial Intelligence (AI)-powered predictive analytics, telemedicine platforms, and mobile apps, this innovative approach provides real-time monitoring of vital signs, movement patterns, and environmental factors. The IoT-based system enables personalized therapy plans, tailored rehabilitation programs, and timely interventions, empowering patients to take control of their condition. Moreover, AI-driven predictive analytics identify potential complications, allowing for proactive measures to prevent hospitalizations and reduce healthcare costs. Telemedicine platforms facilitate seamless communication between patients, caregivers, and healthcare professionals, ensuring continuous support and guidance. Additionally, mobile apps provide patients with accessible resources, educational materials, and community support networks. Preliminary results demonstrate remarkable improvements in patient outcomes, including enhanced mobility, reduced complications, and increased autonomy. Caregivers also benefit from reduced burden, improved quality of life, and enhanced support. With its potential to transform the lives of millions affected by paralysis worldwide, this groundbreaking IoT-based care model is poised to redefine the future of neurological rehabilitation.

Correlation of Vehicle Traffic to Air Quality, Temperature, and Noise in Malang City Through an Internet of Things (IoT) Approach

In response to the issues of air pollution, temperature, and noise, this project attempts to create an air quality, temperature, and noise monitoring system using Internet of Things (IoT) technology. This system will comprise physical components and a web platform that delivers real-time environmental reports. Users can readily obtain information regarding air quality, temperature, and noise levels via this platform, which takes advantage of the internet's accessibility. This Internet of Things-based device monitors environmental quality in six high-traffic areas in Malang, Indonesia. The system uses various sensors to monitor air quality, temperature, humidity, dust levels, carbon monoxide (CO), carbon dioxide (CO2), and noise pollution in real-time. Data was collected during peak traffic hours, demonstrating the direct influence of car emissions on air quality. The findings show that some regions' CO and particulate matter levels surpass safe criteria, notably during peak traffic periods, but CO2, humidity, and noise levels are below acceptable norms. These findings highlight the necessity for urban air pollution reduction initiatives. Additional sensor calibration and communication modifications are recommended to increase system accuracy and dependability. This study gives significant insights for local authorities to manage urban environmental quality and safeguard human health.

A Study on the Automatic IoT Based Multiple Solid Mixing and Product Measurement System

Abstract: This project presents an automated system for mixing multiple solid materials and measuring the final product, using Internet of Things (IoT) technology. The system automatically mixes different solid ingredients in the right proportions, ensuring consistency and accuracy. It uses sensors to monitor the mixing process in real-time, sending data to a central control system that makes adjustments as needed. The system also measures the weight and volume of the final product to ensure it meets the required specifications. With IoT integration, the system allows remote monitoring, data tracking, and performance analysis, reducing human error and improving efficiency. This technology is flexible and can be used in industries like food processing, pharmaceuticals, and chemicals, offering a more precise and automated approach to solid mixing and product measurement.

IoT-Enabled Cross-Platform Applications for Real-Time Logistics Monitoring

Internet of Things (IoT)The integration of Internet of Things (IoT) technology and cross-platform mobile applications has revolutionized real-time logistics monitoring, enabling unprecedented visibility, efficiency, and proactive management of supply chain operations. This article explores the key components of IoT-enabled logistics monitoring, including IoT sensors, data integration, real-time tracking, automated notifications, predictive maintenance, and proactive supply chain management. The benefits of implementing these solutions are highlighted, such as reduced risks, optimized routes, improved operational efficiency, and enhanced customer satisfaction. The article also discusses the challenges associated with IoT implementation, including data integration, connectivity, security, and privacy, and provides strategies for overcoming these obstacles. Through real-world case studies, the tangible impact of IoT-enabled cross-platform applications in logistics is demonstrated, showcasing the potential for significant improvements in cost savings, asset utilization, and overall supply chain performance. As the IoT landscape continues to evolve, the adoption of these technologies will become increasingly critical for logistics providers looking to maintain a competitive edge in the market.

Application of the Internet of Things Technology in the Production and Dissemination of Intangible Cultural Heritage Micro-documentaries

The oceans, as the forthcoming financial frontier, have precipitated disputes at the nexus of resource protection and abuse, a domain where customs and social norms converge. The present research, supported by research reviews, inquiries, and personal assessments, determines the primary policy, socio-economic, ecological, and cultural motorists of conflicts stemming from the interplay of recreational fishing a significant shipping activity in Mediterranean maritime safeguarded areas and other coastal consumers. Intelligent media is a new media form with the comprehensive application of mobile Internet technology, Internet of Things technology, big data technology, and artificial intelligence technology to realize high human-machine interaction and collaboration. The functions of information production, information experience, information push, information storage, and information utilization are realized through intelligent media and intelligent platforms. The application of smart technology builds a new form of communication, making the transmission of Intangible Cultural Heritage (ICH) more efficient and accurate. It can promote ICH's more effective dissemination and higher quality utilization through intelligent media technology by establishing a larger cultural communication platform, stimulating wider cultural consumption, and transforming cultural resources. ICH Fisheries Management (ICHFM) is an effective carrier of national spirit and emotion, representing the nation's past, present, and future, and is the core of social and cultural development. The protection and inheritance of ICH are related to the continuation and development of Chinese culture, and it is an essential cultural project that is urgently needed. As one of the products of human civilization, the ICHFM is facing a complex situation of coexisting challenges and opportunities in contemporary times, which needs to respond to the significant changes in living environment and values, and at the same time, it meets with the awakening of human awareness of cultural diversity and the improvement of cultural heritage protection ability. Therefore, researchers and decision-makers should have a positive and advanced attitude towards technology innovation under the background of intangible protection and inheritance of all kinds of innovation practice, especially intangible knowledge popularization, culture, and social education transmission behavior, which should be encouraged and emerging technology, communication channels, consumption forms, explore the intangible spread in the contemporary digital potential value, focus on intangible digital transmission present "video and experience" "elements of open source and digital potential" "digital survival and virtual community inheritance" trend. The findings indicate that the persistent contradictions resulting from the conflict originate from divergent notions of history, resource adoption, and conservation.

A Multi-scale Patch Mixer Network for Time Series Anomaly Detection

With the development of Internet of Things (IoT) technology, a large amount of data with temporal characteristics is collected and stored. How to efficiently and accurately identify anomalies from these data is a major challenge. At present, there are many problems in the application of anomaly detection, including non-stationary data, complex and difficult-to-collect anomalies, the need for real-time detection and the limitation of computing resources. But few methods can comprehensively consider these issues. To overcome these challenges, we propose a lightweight neural network, Multi-scale Patch Mixer Network (MP-MixerNet). It is mainly composed of a Mixer Block based on fully connected layer design, which contains a Temporal-Mixer and a Spatial-Mixer, and can simultaneously model the intra- and inter-series dependencies of multivariate time series. We also perform multi-scale patch segmentation based on frequency analysis, which helps the model extract robust features from multiple period views. In addition, we design an Input Stabilization module to help the model deal with data distribution shift. Experimental results on a public time series anomaly detection dataset show that we are able to achieve higher comprehensive performance with fewer parameters and inference time.