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Distinctions Between Industrial IoT and IoT
The industrial IoT notion predates the concept of the Internet of Things. However, how gadgets operate in a smart home or workplace differs greatly from how they operate in an industrial setting, such as, for instance, in the assembly.
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Applied Technology Review | Monday, January 16, 2023
Industrial IoT varies from typical IoT is essential when designing, installing, or running these systems.
FREMONT, CA: The industrial IoT notion predates the concept of the Internet of Things. However, how gadgets operate in a smart home or workplace differs greatly from how they operate in an industrial setting, such as, for instance, in the assembly line of an intelligent car. Statista predicts 29.4 billion IoT devices will be worldwide by 2030. Accordingly, there will be more than three devices for each individual in the world at present.
Consumers are not the largest group of IoT device users in terms of proportion. The major sectors of the energy, water, industry, government, transportation, and natural resources industries use countless gadgets.
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IIoT
IIoT is a system of systems powered by AI that can curate, manage, and analyse data throughout an industrial process. The system comprises machinery, sensors, and other interconnected, real-time systems and devices. When machine learning and AI applications are used to harness the data produced by connected IIoT infrastructure components, industries may improve productivity, learn from failures, and much more.
Machine-to-machine communication is used by IIoT networks to speak between devices. Additionally, these devices routinely send and receive data to and from a centralised system that unifies and controls all IIoT devices. The main system might run in data centres, on edge, or in the cloud. Near-field communication (NFC), Bluetooth Low Energy (BLE), Wi-Fi, and 5G are typically used to connect IIoT devices. The advantages of IIoT include more effective machinery, cleverer administration, and improved worker security. Industrial operations can be made safer for employees by automating them, which also lowers labour costs and improves productivity.
IoT
The Internet of Things (IoT) is a term used to refer to a network of physical things that have sensors, software, and other technologies built in. This network's main goal is to connect to other internet systems and devices and exchange data with them. Different IoT devices exist: They could be complex industrial tools or home appliances.
IIoT and IoT have certain things in common. Users use a single platform to manage connected and communicating systems and devices. IoT also makes use of edge and cloud computing, as well as analytical features. Their intended usage distinguishes them most from one another. The IoT's end users are consumers, businesses, and other workplaces like the healthcare industry.
IoT aims to integrate systems for better accessibility and automate a variety of formerly manual operations. For instance, people can control all of their smart devices in their homes by utilising a voice-activated smartphone or central hub. IoT settings are made to be simpler, smarter, and more open to everyone.
Differences between IoT and IIoT
IIoT can be viewed as an IoT with much improved capabilities. It's crucial to comprehend the distinctions between the two, especially if they perform in fields or settings that demand a lot of machine collaboration, cooperation, and connectivity.
The End-use
The end user is the primary distinction, as was already mentioned. In both situations, the capabilities and functionality of the devices and network are determined by the end user. In offices, buildings, houses, and other places of business, IoT is built and used. Although health IoT can be very sophisticated, it is still true that it is more closely tied to consumers than industrial equipment. In comparison, the scale of the IIoT end user is greater. Different instruments, integrated systems, and networks are needed for industrial work.
Machine learning and AI: Optimising Operations
The way both groups employ AI and machine learning is another significant difference. Applications powered by analytics and AI will be used by home and business IoT devices. They do not, however, utilise data to the same extent as IIoT.
For instance, IIoT-enabled companies can use AI algorithms to analyse the data each device produces and modify the unique procedures for each unit to boost output. IIoT systems can therefore learn and improve their efficiency. Consumer-facing IoT solutions do not use these advanced analytics. IIoT AI systems can automate a variety of tasks, including security, redundancy, and maintenance.
Power, Performance, and Durability
Although IIoT systems and devices vary in size, they are all made to withstand harsh environments. Industrial industries need to withstand extreme heat and cold, as well as weather, water, dust, friction, and extended life cycles. IIoT is more enduring and resilient than IoT gadgets and networks. Additionally, they are made to be fixed and maintained. Furthermore, IIoT performance is high; thus, it is necessary to build both software and hardware suitably.
Durability is crucial for IIoT systems because they are made for mission-critical procedures. Industries cannot afford system outages or disruptions. Backup solutions are typically built as backup plans in case an IIoT infrastructure component fails or needs maintenance.
Precision, Scalability, Data Flow, and Connectivity
Industries that use robotics, sensors, and systems need degrees of precision above and beyond what domestic IoT devices can provide. IIoT also requires scalability. Enterprises can have hundreds or thousands of devices linked to a network, whereas work or home contexts may only connect a few dozen. As a result, industries need to be able to expand their IIoT systems if demand rises.
Additionally, compared to other IoT domains, the volume of data generated in IIoT infrastructures is significantly higher. The IIoT presents a special set of difficulties in real-time data transport and data security. Similar to how private 5G networks are becoming the new standard, industries typically employ private networks to manage their data flows.
To use big data from IIoT to optimise operations, all of the data must be combined and analysed. Top suppliers specifically create the main software and platforms utilised in IIoT for industrial applications. Big data from devices, employees, communications, and outside elements like supply chains, partners, or market changes can all be managed by them. Once the elements have been calculated and analysed, these systems use AI to automatically alter processes without any human involvement.
FREMONT CA,: Researchers at the Nano Life Science Institute (WPI-NanoLSI) of Kanazawa University have unveiled an innovative biosensor that significantly enhances the detection of 1-methyl nicotinamide (1-MNA), a metabolite linked to various diseases, including cancer, metabolic disorders, and liver conditions. This innovation, published in Analytical Chemistry , represents a leap forward in disease diagnosis by offering improved sensitivity and accessibility compared to traditional methods like mass spectrometry and nuclear magnetic resonance (NMR).
Addressing Limitations of Traditional Detection Methods
Metabolites such as 1-MNA, by-products of bodily processes, serve as critical biomarkers for diagnosing and monitoring diseases. Elevated levels of 1-MNA are associated with increased nicotinamide N-methyltransferase (NNMT) activity, a process linked to the progression of aggressive cancers and metabolic disorders. While effective, current detection methods rely on expensive equipment and complex sample preparation, limiting their use in routine clinical diagnostics.
Seeking a more efficient solution, researchers Masaya Ueno, Tomoki Ogoshi, and Atsushi Hirao explored pillararenes—a class of macrocyclic molecules known for their versatility in chemical sensing. By leveraging these molecules, the team developed a novel biosensor capable of detecting 1-MNA with significantly higher sensitivity and reduced complexity.
Enhanced Detection with Sulfonated Pillar [6]arene (P6AS)
In their latest work, the research team introduced a sulfonate-functionalized variant of pillar [6]arene (P6AS), which exhibits a binding affinity for 1-MNA that is 700 times greater than its predecessor, carboxylated pillar[6]arene (P6AC). This dramatic increase in sensitivity allows for detecting sub-micromolar concentrations of 1-MNA directly in unpurified urine samples.
Unlike earlier iterations, which required extensive sample purification and could not detect micromolar concentrations of 1-MNA in cell culture supernatants, the P6AS biosensor circumvents these limitations. It delivers precise results without complex preparation steps, making it a practical option for real-world diagnostic applications.
Potential Applications in Disease Diagnosis and Drug Development
The ability to measure 1-MNA levels accurately has profound clinical implications. Elevated NNMT activity is a hallmark of various cancers, and its suppression has shown promise in alleviating certain disease symptoms. By quantifying 1-MNA levels, healthcare providers can gain insights into NNMT activity, aiding in disease diagnosis and monitoring.
Moreover, the P6AS biosensor's high-throughput capabilities open new doors for drug discovery. The device's efficiency could expedite the screening of potential NNMT inhibitors, paving the way for novel treatments for diseases such as liver disease and cancer.
Despite its advantages, the biosensor does have limitations. While it performs well with urine samples, autofluorescence in human serum poses challenges for accurate detection. Researchers are optimistic that further refinements will address these issues, broadening the sensor’s applicability to more sample types.
Implications for Broader Healthcare Innovation
The improved sensitivity of the P6AS biosensor stems from the stronger acidity of its sulfonate groups compared to the carboxylate groups in earlier versions. This breakthrough not only enhances diagnostic precision but also has the potential to transform how diseases linked to NNMT activity are understood and treated.
“Monitoring NNMT expression and activity by quantifying 1-MNA is critical for elucidating patient pathology,” the researchers emphasized. They envision their biosensor as a tool for clinical diagnostics and advanced research, including in vivo imaging of cancer cells and high-throughput drug screening.
Future Directions and Optimism
While traditional methods like mass spectrometry remain the gold standard for detecting nanomolar concentrations, the accessibility and efficiency of the P6AS biosensor position it as a game-changer in diagnostic technology. The team believes that further refinement could unlock even greater potential, including applications in advanced imaging and real-time monitoring of disease progression.
“Further improvement of our strategy will contribute to high-throughput screening of NNMT inhibitors, diagnosis of liver diseases, and imaging of human cancer cells in vivo,” the researchers concluded.
This cutting-edge biosensor not only simplifies disease detection but also marks a significant step toward making diagnostic technologies more accessible. With its potential to improve diagnostics and treatment options, the P6AS biosensor promises to be a vital tool in the fight against cancer, liver disease, and other conditions linked to elevated 1-MNA levels. ...Read more
Sensor technology plays a crucial role in various industries, detecting environmental changes, converting physical phenomena into measurable signals, and transforming various sectors through diverse applications.
At the core of sensor technology is the ability to detect specific physical properties such as temperature, pressure, light, motion, and chemical composition; most sensors operate on a fundamentally simple principle: they measure a physical phenomenon and translate that into an output that can be read, such as an electrical signal. A great example is the temperature sensor using a thermocouple, measuring changes in temperature through voltage changes, which a microcontroller or computer system can interpret. Such simple operation applies to many types of sensors, such as resistive, capacitive, inductive, or piezoelectric.
Sensor technology has innumerable applications. In the healthcare sector, monitoring patient conditions is a significant application. Equipped biosensors in wearable devices can trace inner physiological functions like heart rate, blood pressure, and glucose levels and transfer real-time data to patients and healthcare providers. This technology empowers individuals to oversee their health, and timely medical interventions help improve general patient care.
Sensors are the essential tools used in industrial automation to automate monitoring. Examples include using pressure sensors to monitor levels and pressure within pipelines; this allows the pressure to be kept within predetermined safety limits. Temperature is another parameter whose monitoring is considered critical in manufacturing processes. An optimal temperature range ensures that equipment does not fail and supports overall quality. Today, sensors in the Internet of Things only make this effect more profound because data collection and analyses can happen in many systems in real-time.
Environmental monitoring is another critical application of sensor technology. Sensors can monitor air and water quality, which means authorities can keep track of pollution levels and, accordingly, reduce them so that they do not threaten public health. Intelligent sensors installed in cities can monitor the patterned flow of traffic and help in planning intelligent cities and efficient transport systems. Soil moisture sensors in agriculture manage the irrigation for the farmers with responsible practices and efficient use of resources.
Advanced sensor technology is changing the face of the automotive industry. Today's automobiles carry sensors supporting various safety features, such as collision detection, lane-keeping assistance, and adaptive cruise control. These features are much safer for the driver and contribute to the greater goal of autonomous vehicles.
Advances in sensor technology will continue to open the door to more innovative applications driven by emerging trends such as miniaturization, wireless connectivity, and enhanced accuracy. Some examples of what is out there for sensor technology include smart homes with automatically adjusted lighting and temperature depending on occupancy and advanced manufacturing systems utilizing real-time data for decision-making. ...Read more
A wearable bioelectronics lab at the Georgia Institute of Technology at Northwestern University is developing innovative haptic patches, termed epidermal VR, to help people with neurological conditions, especially those with early-onset vision impairments. These patches use sensors to transmit information to haptic devices, much like VR goggles replicate visual experiences.
The patches utilize actuators that operate at frequencies between 50 and 200 Hz, where the skin is most sensitive. These actuators can vibrate and apply pressure, requiring more force than typical vibration mechanisms. This small, battery-powered device achieves both functions using bistable magnetic materials and the skin's natural spring-like properties, making it more efficient than traditional, energy-heavy tethered devices. The bistable mechanism flips between states with a small burst of energy, similar to a light switch.
The actuator uses a combination of vibration, pressing and rotation to convey information to the skin. Researchers are exploring the optimal designs for these channels. For instance, in a visual sensory replacement system, indentation patterns created by the actuators can alert users to the presence of objects, warn of potential collisions and indicate the distance to obstacles, helping them navigate their surroundings. By integrating LiDAR systems and related APIs that identify objects like chairs, walls and doors, vibration can also guide users toward specific locations.
This epidermal VR system maps the environment and detects obstructions using LiDAR technology found in smartphones. This information is transmitted via Bluetooth to the haptic device for non-visual perception. Utilizing Apple's LiDAR APIs simplifies app development, with the phone handling image categorization and 3D reconstruction. Cloud processing may be incorporated to enhance the system's capabilities.
A key innovation is using kirigami, a Japanese paper-cutting technique, to convert the actuator's linear and rotational motions. This allows for creating intricate mechanical stimuli on the skin, like sub-pixels, by positioning multiple actuators near each other. This enables the delivery of more complex tactile information.
The research team is also exploring using neuromorphic computers and edge computing to further enhance the device's capabilities in the future. Currently, it uses a commercial System-on-a-Chip (SoC) with an ARM processor, Bluetooth stack and communication antenna.
The lab makes the stimuli intuitive by linking them to natural sensory experiences. This lets users quickly learn the system, often within a couple of hours, by associating specific stimuli with visual locations. With practice, users can automatically identify an object's location based solely on the sensation.
The lab aims to aid individuals who have lost sensation in their feet due to neurological conditions like stroke or spinal cord injuries. The haptic patches could assist gait and balance by enhancing sensory feedback, making walking easier and safer. This is achieved by delivering precise tactile cues to the feet, helping users regain awareness of their foot placement and improve their balance. ...Read more
One urgent worldwide issue that calls for creative solutions is climate change. One important instrument for overcoming this obstacle is technology, which is sometimes viewed as a double-edged sword. It improves resilience and sustainable development by providing data-driven analytics and renewable energy solutions. Driven by technical developments, the renewable energy revolution reduces reliance on finite resources and greenhouse gas emissions by offering clean alternatives to fossil fuels.
Technological innovations in energy storage, such as lithium-ion batteries and pumped hydro storage, address intermittency issues, enabling a reliable transition to renewable sources. Smart grids and demand-side management technologies optimize energy distribution, enhancing efficiency and resilience in the face of climate-induced disruptions. Agriculture, vulnerable to climate variability, transforms technology-driven solutions. Precision agriculture employs sensors, drones, and satellite imagery to accurately monitor soil health, crop growth, and water usage. Genetic engineering and biotechnology offer drought-resistant crops and disease-resistant strains, bolstering food security amidst changing climatic conditions.
Infrastructure resilience lies at the heart of climate change adaptation, with technology playing a pivotal role in its enhancement. Advanced modelling techniques and simulation software enable engineers to design infrastructure capable of withstanding extreme weather events, from fortified coastal defences to resilient urban infrastructure. Sensor networks and Internet of Things (IoT) devices facilitate real-time infrastructure integrity monitoring, enabling proactive maintenance and rapid response to climate-induced disruptions, minimizing downtime and economic losses. Nature-based solutions harness the inherent resilience of ecosystems to mitigate climate risks and enhance adaptation efforts.
Green infrastructure, encompassing green roofs, urban forests, and permeable pavements, reduces the urban heat island effect, mitigates flood risks, and enhances biodiversity. Technological innovations such as bioengineering and ecological restoration techniques amplify the effectiveness of nature-based solutions, offering scalable and cost-effective strategies for climate resilience in urban and rural landscapes. In the era of big data, harnessing vast amounts of information becomes instrumental in climate change adaptation. Remote sensing technologies, artificial intelligence, and machine learning algorithms analyze complex environmental data to predict and mitigate climate-related risks.
From early warning systems for weather events to predictive models for disease outbreaks, data-driven decision-making empowers policymakers and stakeholders to formulate adaptive strategies with precision and foresight, enhancing resilience across sectors and communities. Technology catalyzes community engagement and climate education, fostering collective action and behavioral change. Social media platforms, augmented reality, mobile applications, and virtual reality experiences raise awareness about climate change impacts and adaptation strategies, empowering individuals to take proactive measures in their daily lives. Digital platforms facilitate peer-to-peer knowledge exchange and collaboration, enabling communities to co-create innovative solutions tailored to local contexts and needs.
Technology emerges as a tough ally in the quest for adaptation and resilience. From renewable energy solutions to precision agriculture and climate-resilient infrastructure, technological innovations offer various opportunities to mitigate weather shift impacts and foster sustainable development. By harnessing the power of data-driven decision-making and promoting community engagement, we can navigate the complexities of climate change with resilience and ingenuity, forging a path toward a more sustainable and resilient future for future generations. Collaboration, innovation, and collective action remain imperative in addressing the defining challenge of our time. ...Read more
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