Browse Topic: Internet of things (IoT)
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
With the rapid advancement of connected vehicle technologies, infotainment Electronic Control Units (ECUs) have become central to user interaction and connectivity within modern vehicles. However, this enhanced functionality has introduced new vulnerabilities to cyberattacks. This paper explores the application of Artificial Intelligence (AI) in enhancing the cybersecurity framework of infotainment ECUs. The study introduces AI-powered modules for threat detection and response, presents an integrated architecture, and validates performance through simulation using MATLAB, CANoe, and NS-3. This approach addresses real-time intrusion detection, anomaly analysis, and voice command security. Key benefits include zero-day exploit resistance, scalability, and continuous protection via OTA updates. The paper references real-world automotive cyberattack cases such as OTA vulnerability patches, Connected Drive exploits, and Uconnect hack, emphasizing the critical need for AI-enabled proactive cybersecurity frameworks.
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT and ML technologies can enhance the efficiency of engine reliability testing. The paper includes the following case studies:
A smart highway tunnels lighting system based on the technology of cloud platform and Internet of Things(IoTs) has been designed to address the common problems of high energy consumption and low level of intelligence in China's highway tunnel lighting system. The highway tunnel lighting system consists of four layers of architecture: platform management layer, local management layer, middle layer and terminal layer. The system collects real-time brightness, lamp brightness, traffic volume and other data outside the tunnel through various sensors deployed on site, and then uploads the collected data to the main controller through LoRa IoTs. The main controller combines the brightness calculation method of the lighting design rules to control the brightness of the tunnel lighting in real time, achieving real-time adjustment of the brightness of the tunnel LED lights and the brightness outside the tunnel, and realizing a safe and energy-saving lighting effect of "lights on when the car comes, lights on when the car goes, and lights follow the car". The experimental results show that the energy-saving rate of the system has reached about 70%, which has achieved good energy-saving and emission reduction effects, and has significant economic, social, and ecological benefits.
TOC
Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding. This paper aims to investigate and determine the volume of material for use in various agricultural operations. This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer. Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field and top layers of residue, correlating this data with elevation maps to compute harvested material volume. This innovation supports operations such as baling, residue decomposition time and thereby contributing to land preparation. This technique offers benefits like reduced operational costs, labor independence, and enhanced soil nutrient planning.
Wearable sensors are devices that can be worn on the body and measure the state of the body. They are part of the Internet of Things (IoT) and show great promise for monitoring health. These sensors generate large amounts of data, and that data must be processed to be understood. The field of computing dealing with processing these data on the sensor or a device that the sensor is connected to — rather than at a remote server on the cloud — is called edge computing. Edge computing is a key element in wearable sensor technology.
As a result of advancements to the Industrial Internet of Things (IIoT), companies across the globe are realizing the potential of smart manufacturing and connected business models. In fact, IoT connections are projected to more than double over the coming years: from 18 billion dollars in 2024 to 39.6 billion by 2033.
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a single source of truth for all stakeholders. This tool enables auto selection of control charts based on sample size, frequency & type of parameters (unilateral, bilateral, GD&T) to accommodate variation in Quality standard of parts & manufacturing Process. Additionally Real-time adjustment of control limits based on time period selection make this tool accurate & reliable for monitoring Quality. Incorporating Industry 4.0 and smart manufacturing principles, this tool represents a significant advancement in Quality control. Integration with the Internet of Things (IoT) enables automatic data collection and monitoring, enhancing the efficiency and accuracy of the Quality control process with the SPC based Analytic model. This IoT integration also supports predictive maintenance of tooling and equipment and early detection of potential issues, reducing downtime and improving overall productivity. By minimizing the incidence of faulty parts on the shop floor, the tool significantly reduces rejections, contributing to more sustainable manufacturing practices. This proactive approach ensures high-Quality standards and aligns with smart manufacturing goals by optimizing resource use and enhancing operational efficiency.
The Defense Advanced Research Projects Agency (DARPA) pioneered satellites, the internet, drones, and human-computer interfaces. Now that work is enabling the next round of revolutionary technologies, including artificial intelligence (AI), edge and cloud computing, and the Internet of Military Things (IoMT) for a wide variety of Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications. Laptops and tablets are beneficiaries of yesterday's DARPA breakthroughs as well as enablers of today's and tomorrow's innovations. For example, ruggedized mobile PCs provide powerful new tools for asymmetric warfare by giving soldiers anytime, anywhere access to biometric information such as fingerprints and facial recognition. That information enables them to quickly determine whether a person in street clothes at a checkpoint is a civilian or combatant. This application also highlights the fundamental role of edge computing and the cloud for securely storing and sharing that biometric information.
Researchers have developed a multifunctional sensor based on semiconductor fibers that emulates the five human senses. Prof. Bonghoon Kim, department of robotics and mechatronics engineering of Daegu Gyeongbuk Institute of Science & Technology (DGIST), conducted the study in collaboration with Prof. Sangwook Kim at KAIST, Prof. Janghwan Kim at Ajou University, and Prof. Jiwoong Kim at Soongsil University. The technology developed in the study is expected to be utilized in fields such as wearables, Internet of Things (IoT), electronic devices, and soft robotics.
Advances in IoT and electronic technology are enabling more personalized, continuous medical care. People with medical conditions that require a high degree of monitoring and continuous medication infusion can now take advantage of wearable medicine injection devices to treat their problems. Wireless communication allows medical personnel to monitor and adjust the amount and flow rate of an individual’s medication. The small size of the injectors enables the individual to be active and not be burdened or limited by a line-powered instrument (see Figure 1).
Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data analytics, and artificial intelligence (AI) and enables data-driven decision-making to meet the evolving needs of urban populations. Integrating AI-powered vehicles with smart city infrastructure can potentially compliment it and can offer numerous advantages like: Traffic Management, Infrastructure Optimization, Parking Optimization, Safety Enhancements, Environmental Sustainability, Emergency Response, various Infrastructure, and business Investment Planning and many more. Moreover, the integration will also have positive impact on environment such as Smart city infrastructure can provide AI-powered vehicles with data on optimal routes and availability of parking slot, resulting in reduced air pollution and energy consumption. In essence, this offers a holistic approach to urban mobility, fostering safer, more efficient, and environmentally sustainable transportation systems. By leveraging advanced technologies and data-driven insights, cities can unlock new opportunities for improving quality of life, enhancing economic competitiveness, and fostering inclusive and resilient communities. In this technical manual, we delve into the futuristic implications of this integration, providing a detailed exploration of the technical aspects and benefits.
If you're just getting comfortable with Industry 4.0, which saw the beginnings of smart manufacturing, digitization and real-time decision-making in factories, a senior leader at Intel says the world is already moving on to Industry 5.0. What's Industry 5.0? A joint study by many researchers (link: Industry 5.0: A Survey on Enabling Technologies and Potential Applications (oulu.fi)) describes 5.0 as merging human creativity with intelligent and efficient machines to deliver customized products quickly. But it will take a lot of change and learning to get there.
Carbon-fiber structural batteries are not entirely new, but now Sinonus, a company spun out of Chalmers Technical University in Gothenburg, Sweden, is further developing the technology with carbon fibers that double as battery electrodes. The technology has already been demonstrated in low-power applications, and Sinonus will now develop it for use in a range of larger applications including, first, IoT devices and then drones, computers, electric vehicles and airplanes. By integrating the battery into carbon-fiber structures, Sinonus believes that an EV's weight could be reduced while the driving range could increase by as much as 70%. The carbon-fiber technology used by Sinonus originated at Oxeon, another Chalmers spin-off.
The industrial internet of things (IIoT) is the nervous system in manufacturing facilities worldwide, with programmable logic controllers (PLCs) serving as its vital synapses. This digital neural network is transforming isolated machines into interconnected ecosystems of unprecedented intelligence and efficiency. PLCs have evolved from simple control devices into sophisticated nodes in a vast, responsive network.
Manually checking the quality of components or products in industry is labor-intensive for employees and error-prone on top of that. The Fraunhofer Institute for Mechatronic Systems Design IEM is unveiling a solution that provides total versatility in this area. In an it’s OWL supported collaboration with Diebold Nixdorf and software specialist verlinked, Fraunhofer IEM has created a combination of collaborative robot (cobot), AI-based image analysis and IoT platform. The system frees employees from having to perform visual inspections and can be incorporated into all kinds of testing scenarios. The Fraunhofer researchers presented a demonstrator of the cobot/IoT platform at the 2024 Hannover Messe Trade Show in February.
The Internet of Military Things (IoMT), sometimes referred to as the Internet of Battlefield Things (IoBT), is gaining momentum for applications that improve defensive and battlefield capabilities. Like its civilian counterpart, the IoMT are networks of sensors, wearables, and imaging devices using edge and cloud computing to improve military operations and safety. However, battery failure in an IoMT device can have serious consequences in applications such as unmanned aerial drones that are used to patrol border areas or secure military bases. Battery life requirements are also high for the sensors and surveillance cameras that can be used to send real-time intelligence back to the command center for strategic decisions. Likewise, predictable battery life for IoMT devices used for vehicle management, battlefield supply chains, and weapon control are critical for efficient operations. Therefore, optimizing the device design and software to reduce power consumption and increase battery life is crucial. One method to do this is to use battery emulation and profiling software, which can imitate the performance and features of different kinds of batteries under various situations. With this software, device designers can calculate battery life accurately. Additionally, emulation software can measure current drain to adjust device designs that can prolong battery life.
In the increasingly connected and digital world, businesses are sprinting to integrate technological advancements into their corporate fabric. This is evident with the emerging concept of “digital twinning.” Digital twins are virtual representations of real-world objects or systems used to digitally model performance, identify inefficiencies, and design solutions. This helps improve the “real world” product, reduces costs, and increases efficiency. However, this replication of a physical entity in the digital space is not without its challenges. One of the challenges that will become increasingly prevalent is the processing, storing, and transmitting of Controlled Unclassified Information (CUI). If CUI is not protected properly, an idea to save time, money, and effort could result in the loss of critical data. The Department of Defense's (DoD) CUI Program website defines CUI as “government-created or owned unclassified information that allows for, or requires, safeguarding and dissemination controls in accordance with laws, regulations, or government-wide policies. It is sensitive information that does not meet the criteria for classification but must still be protected.” In March 2020, DoD published Instruction 5200.48, establishing the official DoD CUI Registry.
Following its annual report detailing the growing cybersecurity threats to vehicles, fleets, and the networks they rely on, Upstream Security announced the launch of a generative AI tool to enhance its ability to reduce the risk posted by global threats. Israel-based Upstream, which has a vehicle security operations center (VSOC) in Ann Arbor, Mich., monitors millions of connected vehicles and Internet of Things (IoT) devices and billions of API transactions monthly. Ocean AI is built into the company's detection and response platform, called M-XDR, enabling its analysts, as well as those from OEMs and IoT vendors, to efficiently detect threat patterns and automate investigations before prioritizing a response.
Scrap collection from any location is handled with mortal interference in several places and companies which may be extremely harmful or even dangerous to humanity. The demand for robotization has risen rapidly in recent years, owing to cutting-edge technologies that minimize manpower and threat-taking training directly or indirectly. The main objective of the paper is to study, analyze, investigate the main contribution of waste collecting by workers while cleaning in the Mechanical Industry. In order to ensure the safety of the workers during cleaning we had implemented the Automatic Trash Collecting Machine in the industry. For Fabricating the Trash collecting Machine first we had analyzed the problem in the industry and then we had started the free hand sketch of Trash Collecting Machine. Then the design work of Automatic Trash Collecting Machine is done in the modeling software Catia V5. Then the material selection for our model has been done. We had taken the mild steel for the frame, 4 motors for the conveyor and the movement of the vehicle and a movable trash bin for the disposal of waste and a karcher brush for collecting the waste. Then for the Automatic motion we had used Node mc, IoT and the coding part has done for the model. Then the fabrication work for our model is done and finally the testing part for our model in the industry is demonstrated. After the implementation of the model in the industry the safety of the workers while cleaning has greatly increased.
Internet of Thing (IoT) is the connecting network for applications like vehicles, smart devices, buildings etc., with the build in sensors to gather and share information for the user specific needs. The IoT platform offers prospects for a broad and direct integration of the manual world and the digital world by enabling things to be sensed and controlled remotely through existing network infrastructure. Self-contained programs can also be executed on it. This work projects on the integration of the IoT concept to the MOR-socket to manage, monitor and control the energy consumption of smart devices in a smart building. To achieve this, a simulation using Proteus 8.0 professional is made to obtain a virtual MOR-socket. This system is modeled with three prioritized loads of different current rating. The first priority load is the lighting load, second is the motor load and the final one is a load of higher current rating than the other two. The control and monitoring of these loads are performed through a IoT open source webpage (ADAFRUIT) by inculcating Demand Response (DR) strategies. The load connected are monitored and controlled in three modes such as automatic, manual and constraint mode. To facilitate the manual mode touch sensors are provided with each load and in automatic mode current consumption patterns are absorbed for remote connection and disconnection of devices using IoT during emergency and non-mandatory scenarios. In constraint mode the loads can be connected or disconnected based on the power consumption pattern of each load using sensors with a comparison made using a defined threshold value as per DR strategy. Thus the energy conservation during peak load duration is also achieved using MOR socket. A hardware setup for the same is made along with a temperature sensor along with power management for monitoring and curbing the building temperature and power usage.
Items per page:
50
1 – 50 of 309