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This IoT case study shows how one Industrial Internet of Things (IIoT) deployment turned a service bottleneck into scalable growth. EmissionGuard Inc. helps companies meet regulatory compliance requirements for emissions monitoring on GenSet generators. That work requires consistent data logging and reliable reporting. If the data is missing, delayed, or inaccurate, customers risk falling out of industrial regulatory compliance. EmissionGuard’s challenge was not whether IoT devices could help. It was how to apply industrial IoT applications in a way that reduced support burden while keeping compliance simple for customers. Before IIoT Device Implementation: Field Service Engineer Installs and Support Strained Growth Compliance is only as strong as the data behind it. For emissions reporting, customers need monitoring that is continuous, verifiable, and easy to audit. That typically means: Connected monitoring of the GenSet and supporting sensors and devices Time stamped data generated for reporting and proof Reliable storage and access for audits Minimal gaps caused by hardware failure, PC issues, or missed manual steps This is why industrial IoT applications matter in compliance environments. You are not just collecting data. You are protecting customers from risk. That is exactly where EmissionGuard was feeling pressure, because their original model depended heavily on on site installs and customer owned PCs. Before IIoT Device Implementation: Field Service Engineer Installs and Support Strained Growth Two years ago, EmissionGuard had grown to over 100,000 customers. To support that scale, they relied on a highly skilled network of systems integrators and partners to travel for installations. Each install required: On site hardware installation and configuration On site software installation on a customer PC Ongoing support across many operating systems and local IT setups After installation, support calls often had nothing to do with emissions monitoring. They were classic IT problems: “I replaced my printer and now it doesn’t work with your system.” “A Windows update changed security settings and now it won’t connect.” A significant amount of support time was consumed by PC and software variability. Growth was strong, but scalability was strained. More customers meant more installations, more travel, and more support engineers. That is the moment many service businesses hit a ceiling. So EmissionGuard changed the approach. The Solution: Utilizing IIoT Devices to Auto Update their Remote Equipment This year, EmissionGuard adopted an IoT enabled approach that removed the need to dispatch a field service engineer for each install. Now, when a customer places an order, EmissionGuard ships the product instead of scheduling an onsite engineer. Installation can be completed by a regular electrician. The product is the IoT enabled G3 IoT Edge Controller (an IIoT device designed for remote equipment). The customer installs it, then: Plugs the Modbus port into the GenSet Connects additional sensors and devices Applies power From there, the device handles connectivity and publishing. The unit automatically connects to the internet using a 4G modem. Verizon has guaranteed that modem will work on their network for a minimum of 15 years. Once online, the device begins streaming data to a single server, the same system used by all deployed units. The result is a more consistent remote monitoring and management model, with fewer onsite variables. This also unlocked a major benefit. When EmissionGuard improves the cloud application, the update applies across the installed base. That means new value can be delivered without sending technicians back into the field. In a recent update, EmissionGuard added a new feature that predicts catastrophic failure in GenSet units. They offered it as an add on, and many customers opted in for the peace of mind. That is a real example of how IoT applications can create new revenue, not just reduce costs. Results After IoT Implementation EmissionGuard pay: $4.00 per month to Verizon $0.50 per month to Xively to run and maintain the cloud application EmissionGuard charge their customers: $11.00 per month for their standard service $15.00 per month if customers opt for the additional catastrophic failure protection The model is simple. Predictable costs, predictable recurring revenue, and a smoother customer experience. Lower Support Costs, Higher Profit, and Scalable Growth With the new IIoT approach, EmissionGuard now has only 3 support engineers. They also no longer rely on a network of expensive systems integrators for installations. That reduction in travel and support complexity significantly lowered operating costs. It also kept more profit inside the business, while making it easier to scale the next 100,000 installations. Even better, EmissionGuard found a new niche: replacing competitor non connected units with connected, remotely managed alternatives. How Does the Future of Business Look With the IoT? Most companies adopt IoT devices for one reason: fewer surprises. When equipment is connected, you can see issues earlier, respond faster, and build services that feel proactive. That is where customer trust increases and churn decreases. The same pattern shows up across industrial IoT applications: Centralized visibility across fleets of remote equipment Fewer truck rolls and fewer emergency calls Faster troubleshooting based on live data generated Better compliance reporting with fewer gaps And as organizations mature, many expand into adjacent use cases like Energy management systems, where monitoring power usage and energy efficiency can create cost savings and support a sustainable approach. The Future of Remote Business Operations with IIoT As more operations move to the cloud, businesses can offload server maintenance, backups, and security tooling to a cloud service provider. That frees internal teams to focus on service delivery and product improvement. As IIoT adoption grows: Cloud service costs tend to fall PC and on site software issues become less common New installations require less time and staff resources Support becomes simpler because the stack is standardized That is the real story behind this case study. One IIoT device reduced install friction, lowered support load, improved compliance reliability, and created a path to scalable growth.

Predictive customer service is the end goal. Not more dashboards. Not more data. The goal is a better customer experience that keeps customers loyal, reduces service chaos, and protects your reputation. That is why interest in IIoT solutions keeps growing. When sensors and devices send data generated to a cloud platform, cloud computing can turn it into meaningful insights your team can act on through a user friendly interface. That is the foundation of IIoT remote monitoring and management. And it directly impacts how customers experience your brand. Customer Acquisition vs Loyalty: Why Customer Experience Strategy Matters Customer acquisition is expensive. It takes marketing spend, sales time, onboarding, and support. Then one bad service moment can erase months of effort. That is why a customer experience strategy matters. Loyalty is built through consistency. Customers stay when you reduce surprises and remove friction. They spend more when they trust you. They refer others when you make their life easier. So the question becomes: Where do customers feel the most pain, most often? In many industries, it is when service is late, reactive, or last minute. And the clearest example of that pain is a run-to-empty event. "... one bad service moment can erase months of effort." Avoiding Run-to-Empty Scenarios That Destroy Customer Loyalty Run-to-empty events are simple, and brutal. A customer runs out of something they rely on. Ink. Chemicals. Fuel. Filters. Solutions. Heat-sealing tape. The result is predictable: Their operation stops. Their team gets frustrated. Your company gets blamed. Even if the customer forgot to reorder, the experience still feels like your failure. That is what damages loyalty. This is where IIoT becomes practical. If you can monitor levels, usage rates, and operating conditions remotely, you can predict when “empty” is coming and act before downtime hits. That is the bridge between traditional service and predictive customer service. Reshaping Customer Service to be Predictive Most customer service is built around the customer raising their hand. They notice a problem. They call. Your team reacts. Everyone hopes it gets fixed fast. Predictive customer service flips that model. You see risk early. You act early. The customer experiences fewer interruptions. That only works when you have the right system in place. What is remote monitoring and management? Remote monitoring and management is the ability to collect equipment data from a customer site, view it in a cloud based dashboard, and take action from anywhere. That can include alerts, reporting, workflows, and remote control where appropriate. Now let’s break down what changes when you move from reactive service to proactive service. From Reactive to Proactive via IIoT Remote Monitoring and Management Reactive service is event-driven. Something fails. A customer complains. Your team starts chasing details. Proactive service is data-driven. Sensors and devices detect conditions before failure. The data generated flows through the cloud platform. Your team sees meaningful insights and takes action before the customer is impacted. This shift is not a small improvement. It changes how customers judge your reliability. To see why, it helps to look at the pain points in reactive service. Reactive Service Challenges Reactive service creates friction for everyone. Customers do not always know they are running low until it is too late Customers rarely track consumption habits accurately Reorders get delayed because people forget or get busy Minimum delivery quantities and scheduling constraints create more risk Your service team spends time firefighting instead of planning Costs rise through urgent shipments, urgent dispatch, and missed routing efficiency This is also why customer experience breaks down. The customer feels the disruption. They do not see the internal scramble that caused it. So what does proactive service change? Proactive Service Benefits Proactive service removes the guessing. Levels and usage are visible without manual checks Trends reveal true consumption patterns Alerts trigger before a run-to-empty event happens Workflows can schedule replenishment automatically Your team can plan service, not chase emergencies Customers feel taken care of, even when nothing is “wrong” This is where remote monitoring and management becomes a competitive advantage. Because the customer is not calling you for help. They are seeing you prevent problems. Once you can prevent run-to-empty events, the next step is delivering even more value beyond consumables. Delivering Higher Value With Predictive Servicing The first win is protecting the customer from disruption. The next win is improving their operation. Predictive servicing can expand into: Predictive maintenance solutions that reduce breakdowns and downtime Better scheduling and fewer truck rolls Faster diagnosis because you can see the equipment state before arriving Remote control for approved actions like resets, setpoints, and mode changes Energy management systems that reduce power usage and improve energy efficiency Energy is a strong example. Power usage data can reveal inefficiencies quickly. Power outages can be detected instantly. You can optimize energy usage and drive cost savings while supporting a sustainable approach. At that point, you are not just delivering service. You are delivering a better customer experience that is hard to copy. The remaining challenge is doing it in a structured way, so it scales cleanly. That is where strategy matters. Build a Predictive Service Strategy in 4 Steps Predictive customer service does not start with hardware. It starts with clarity. Here is the 4 step process we use in our Data Strategy to design IIoT solutions that actually work. Step 1: Define the outcome We align on what you want to prevent or improve. Run-to-empty events, downtime, response time, service cost, customer retention, energy efficiency. Step 2: Identify the right data We determine what data to collect and what to ignore. Sensors and devices, thresholds, data logging needs, and what should be processed at the edge vs sent to the cloud. Step 3: Design how your team will use it We map the user friendly interface, alerts, dashboards, and workflows so the data sparks action. We also define where remote monitoring and management should include remote control, and where it should not. Step 4: Present a solution with options You get a clear plan and multiple system options. Connectivity, cloud platform requirements, rollout phases, and integration paths. If you want to do this with our team, book an IoT Data Strategy session . It includes intensive interviews, research, and a solution plan with options for your IIoT remote monitoring system. Prefer to DIY first? Download our free guid e that outlines the exact process we use.

Energy Management Systems start with visibility. If you cannot see power usage in real time, you cannot reduce it. And in many industrial facilities, energy is a major operating cost that hides inside the monthly bill. The fastest way to reduce energy consumption is to measure first, then make changes based on what the data proves. Benchmark Usage With Real-Time Energy Monitoring Most facilities do not have a clean baseline. They might know the monthly bill. They might know production volume. But they do not know which line spikes demand at 10:15am. Or which machine keeps drawing power during lunch. Or what happens when a shift change hits. That is why real time energy monitoring matters. It turns energy from a monthly surprise into a daily data point. Start by installing energy meters or an energy consumption monitor on the assets that matter most. Focus on current, voltage, kWh, and power factor. Pair that with context from sensors and devices like runtime, cycle count, and temperature. Now you can answer simple questions that drive savings: What equipment uses the most energy per part What times of day create peak demand What loads should never be running idle Where energy spikes correlate to downtime, scrap, or rework Once you have the data generated, you need a reliable path to move it. That usually looks like this: Sensors and devices to measure. A cloud edge gateway to collect and normalize signals. A cloud platform to store and visualize. A user friendly interface so your team actually uses it. Real-Time Data Logging at the Clouds Edge Most companies swing between two extremes. They collect too much data and drown in it. Or they collect the wrong data and it never sparks action. Edge data logging helps you avoid both problems. A cloud edge gateway can process signals at the source. It can filter noise. It can calculate key metrics. It can trigger alarms without waiting on a cloud round trip. That gives you a few big benefits: Faster response, because action happens close to the machine Better security posture, because sensitive data can stay inside your network Lower bandwidth, because you are not sending every raw point upstream Cleaner dashboards, because you are pushing meaningful insights, not a data avalanche Then the cloud computing layer does what it is best at. It stores history. It compares trends. It makes dashboards easy to access. This is where you start spotting patterns like: Peak demand windows Time-of-day spikes Seasonal trends Consumption patterns by line, machine, or location That clarity usually shows savings quickly. Not because the system is fancy. Because your team can finally see what is happening. Start an IoT Energy Management Pilot Test Do not try to monitor everything on day one. Pick one process. One line. One high-usage machine. Keep it small so it stays focused. A pilot makes it easier to prove ROI and get buy-in. It also keeps the system simple while you learn what matters in your facility. Be specific about the goal. “Cost savings” is vague. Instead, define the outcome: Reduce peak demand during the highest rate window Cut idle draw during non-production hours Improve kWh per part on one line Catch abnormal power usage before it becomes downtime Clear goals create clear dashboards. And clear dashboards create action. Connectivity for Remote Monitoring: Choosing Where to Start Your equipment might already have signals available. Or it might need “sensoring” first. Either way, you need two answers: What signals do we need. How do we get them out. Signals might include TC, RTD, mA, V, mV, frequency, pulse, counter, and digital states. Connectivity might include RS232, RS485, Ethernet, WiFi, cellular, or Bluetooth. If you only have a few signals, the system is straightforward. If you have many machines and many signals, use a gateway. It keeps connections organized and makes remote monitoring simpler to manage. Also consider remote temperature monitoring. Temperature drift can drive energy waste, quality issues, and unplanned stoppages. If temperature affects your process, it belongs in the first pilot. Turn Data into Action to Improve Industrial Energy Efficiency Monitoring is not the goal. Decisions are. Once you have a baseline, you can: Identify idle loads and eliminate waste Sequence equipment startup to reduce demand spikes Catch abnormal power usage that signals wear or failure Connect energy events to downtime and maintenance This is how you improve industrial energy efficiency without guessing. From Monitoring to Managing: Develop Energy Policies and Control Once your pilot is running, you will notice something. You need more data than you thought. Or you need different context. Or you realize one machine impacts three others. That is normal. Do not restart the project every time you find a gap. Capture what you can now, then build Phase 2. As you move from monitoring to managing, focus on three things. 1) Policies that reduce waste Policies are how you standardize savings. Examples include: Shutdown rules for idle equipment Start-up sequencing to reduce demand spikes Alerts for abnormal draw and power usage Standards for setpoints and temperature stability 2) Remote monitoring and management that supports action Remote monitoring shows you the problem. Remote monitoring and management helps you fix it. That can include remote control capabilities where appropriate, like setpoint adjustments, staged start-up, or controlled shutdown of non-critical loads. 3) A system your team will actually use If your dashboard looks like a spreadsheet, it will not spark action. If it is clear, visual, and simple, it will. That is the difference between data that sits and data that drives cost savings. Data Checklist for an IoT Energy Monitoring Deployment Energy Management Systems work when the foundation is strong. Use this checklist to plan a clean deployment: Sensors and devices for context (runtime, temperature, flow, pressure, vibration, status) Energy meters or an energy consumption monitor at the right points Edge gateway to consolidate signals and standardize connectivity Edge processing and data logging to filter noise and create meaningful insights Cloud service provider to store and secure the data generated Data visualization interface that is easy for humans to act on What is energy management system An energy management system is a combination of measurement, software, and workflows that help you track energy efficiency and optimize energy usage over time. In industrial settings, it often includes real time energy monitoring, data logging, dashboards, alerts, and policies that guide how equipment should run. How to reduce energy consumption in industry Start with benchmarking using real time energy monitoring. Then target the biggest sources of waste like idle loads, compressed air issues, poor start-up sequencing, and unstable temperature control. Verify the impact with data logging, then turn the best changes into policies and automation. Turn Monitoring Into a Smart Energy Management System with Our Data Strategy Most teams get stuck after the pilot. They have data, but not a plan. They are unsure what to measure next, how to structure alerts, and what the full remote monitoring system should look like. That is what our Data Strategy is for. Book a Data Strategy Session for $2,500 We run the interviews, ask the right questions, research your equipment, and present a solution with options for your IoT remote monitoring system. Book here: www.defineinstruments.com/iot-data-strategy Prefer to DIY it first? Download our free guide that outlines the exact process we use with clients: www.defineinstruments.com/oem-data-strategy-guide

As the world continues to fight the coronavirus pandemic there is a growing body of evidence that shows air quality has played a significant role in both the transmission and health outcomes of those affected by COVID-19. The outbreak which began in Wuhan, China quickly spread around the world and spiked in countries including Iran, Italy and Spain. The medical evidence showed that people with pre-existing respiratory conditions were most at risk and many of the reported deaths were individuals with a previous history of respiratory disease. But what caused these individuals to have respiratory diseases in the first place and what linked the spikes in Wuhan, Iran, Italy and Spain? Mounting evidence and the results of the latest studies point to air pollution. The overlap of highly polluted spaces such as northern Italy and pandemic hotspots is stark, additionally a link between air quality and the 2003 Sars outbreak is already known. Air pollution may be important in 3 ways: Higher death rates due to lungs and hearts weakened by dirty air. Pollutants inflame lungs, potentially increasing the probability of catching the virus. Particles of pollution may carry the virus further than first thought Although still preliminary, the hypothesis is that high levels of air pollution may be one of the most important contributors to deaths from Covid-19. The Air Quality Index [AQI] of the countries are as follows: Wuhan 147, Iran 77, Italy 78, Spain 75 (AQI of 50 or below represents good air quality) In a Germany study, analysis showed that of the coronavirus deaths across 66 administrative regions in Italy, Spain, France and Germany, 78% of them occurred in just 5 regions – and these were the most polluted. Areas of the world with highest air pollution seem to correlate to those with highest incidences of coronavirus cases. The research examined levels of nitrogen dioxide (a pollutant produced from diesel vehicles) and weather conditions that can prevent dirty air from dispersing from around a city. Out of 4,443 deaths over three-quarters were in 4 regions in northern Italy and one around Madrid in Spain. These 5 regions had the worst combination of NO2 levels and airflow conditions that prevented dispersal of air pollution. The study noted the Po Valley in Italy and Madrid are surrounded by mountains which helps trap pollution, an indication there might be a correlation between the level of air pollution, air movement and the severity of coronavirus outbreaks. Historic studies link NO2 exposure to respiratory health issues, particularly lung disease which could make people more likely to die if they contracted Covid-19. A separate study published on 7 April looked at fine particle pollution (PM2.5) in the US and found even small increases in levels in the years preceding the pandemic were associated with far higher Covid-19 death rates. Researchers at the Harvard TH Chan School of Public Health in Boston analyzed air pollutants and Covid-19 deaths up to 4 April in 3,000 US counties covering 98% of the population and found an increase of only 1μg/m3 in PM2.5 particles was associated with a 15% increase in the Covid-19 death rate. The authors said the results highlighted the need to keep enforcing existing air pollution regulations, and that failure to do so could potentially increase the Covid-19 death toll. Ironically, the US Environmental Protection Agency suspended its enforcement of environmental laws on 26 March. Another preliminary study from the UK showed London, the Midlands and the north-west England had the highest levels of nitrogen oxides and higher number of coronavirus deaths. It is not yet known whether coronavirus remains viable on pollution particles and in sufficient quantity to cause the disease In another important development, coronavirus was detected on particles of air pollution by scientists investigating whether this could enable it to be carried over longer distances – thereby increasing the number of people infected. Italian scientists collected outdoor air samples at one urban and one industrial site in Bergamo province and identified a gene specific to Covid-19 in multiple samples. Lead scientist Leonardo Setti at the University of Bologna said it was important to explore whether the virus could be carried more widely by air pollution. Two other research groups have suggested air pollution particles could help the coronavirus travel further in the air. It is not yet known whether the virus remains viable on pollution particles and in sufficient quantity to cause the disease. However, previous studies have shown that air pollution particles do harbor microbes and that pollution is likely to have carried the viruses causing bird flu, measles and foot-and-mouth disease over considerable distances. The potential role of air pollution particles is linked to the broader question of how the coronavirus is transmitted. Large virus-laden droplets from infected people’s coughs and sneezes fall to the ground within a meter or two. But much smaller droplets, less than 5 microns in diameter, can remain in the air for minutes to hours and travel further. Researchers say the importance of potential airborne transmission, and the possible boosting role of pollution particles, mean it must not be ruled out without evidence. Take control of the air you breathe Monitoring and measuring the atmosphere using sensors to sample the air quality means you receive data to make decisions about what you breathe.

Industrial service is changing, whether manufacturers are ready for it or not. Customers expect fewer surprises, less downtime, and faster fixes, all without more calls, emails, or site visits. That shift is being driven by real-time data. Companies that can see what their equipment is doing, as it’s doing it, make better decisions, provide better service, and operate with less friction. Companies that can’t are left reacting after something breaks. IoT Remote monitoring is the foundation of that future. It’s how industrial businesses move from guessing to knowing, from reactive support to planned action, and from scaling headcount to scaling systems. To understand why IoT remote monitoring is no longer optional, it helps to start with the basics. What Is IoT and IIoT Remote Monitoring? IoT (Internet of Things) remote monitoring is a system that connects physical machines to the internet and enables them to send data automatically, without a person physically present to check readings or complete a form. This is accomplished by embedding sensors, software, and connectivity in the machine to collect and exchange data over the internet. Think about any WiFi-enabled device — a stove, refrigerator, or car that you can turn on and off, read temperatures, or get alerts about an open door or ignition failure. Those devices are powered by IoT. How Different Is IIoT From IoT? Industrial Internet of Things (IIoT) is a subcategory of IoT used in industrial settings. It’s not a new or different technology. IIoT is a term used to distinguish industrial IoT applications from consumer applications. IIoT involves connecting industrial machines, such as those that live on a factory floor, in the field, or at a store, to the internet. Think manufacturing machines, vending machines, spraying applications, waste water treatment facilities, and more. IIoT isn’t different from IoT. It’s IoT applied to the industrial space. Competitive Advantage Turned Expectation in Remote Equipment A decade ago, IIoT was a competitive advantage. Few companies offered real-time equipment monitoring and support of their industrial equipment. Meaning, if you did, you offered a level of service that couldn’t be matched. Fast forward ten years, and remote monitoring is no longer a competitive advantage. It’s an expectation. Customers now expect manufacturers and service providers to offer real-time asset condition monitoring, support, and more. Lacking these capabilities can lead to market share loss to more technologically advanced competitors. Just take this conversation a client of ours had before working with us: Their Customer: “Your machine has broken down. My operator said he heard some funny noises, then noticed smoke coming out of the sump. You know this machine is critical to our business, and we need it fixed tomorrow!” The Manufacturer: Oh… They sold a potato chip machine to a customer on the other side of the world and had no idea what was happening with it. That’s a bad customer experience — one that can drive your customers straight to your competitors. A better customer experience would have looked like this: The Manufacturer: “We have noticed your machine has broken down. The fault is showing a motor stall that the machine can’t recover from. This is usually caused by either a misaligned shaft or a failed motor. We have scheduled a Technician to call tomorrow with the required tools and spare parts, if needed, to fix the issue.” Customer: “Thanks so much for the call.” Now, that level of service is not possible without an IoT remote monitoring system. Customer Expectations and Service Differentiation  Industrial customers expect a higher level of service. An IIoT remote monitoring system allows you to do two things that level up the customer experience: Proactive Calls IIoT remote monitoring enables you to proactively contact customers when an issue arises. You aren’t expecting them to call with a problem. You can call them, explain the issue, and provide a plan to fix their machine before they even notice anything is wrong. That level of customer service is one that most companies simply don’t provide, but if you do, you’ll be the company everyone wants to buy from. Consumable Replenishment IIoT remote monitoring also enables automated consumable replacement. Instead of requiring the customer to: Keep track of consumables manually Determine when they might need to be replaced Visit each machine to check consumable levels Hop on a call or online to place an order Make sure they’ve ordered with enough lead time You and your customer can know demand in real time. A remote monitoring system allows you to alert your customers in advance when they need to place orders, or send the product to them when and where they need it. Why “No Remote Monitoring” is Now a Competitive Risk Imagine not being able to provide that level of service to your customers. No automated replenishment. No real-time monitoring of equipment. No proactive calls to customers before issues arise. That not only hurts your client relationships, but it also makes it hard for you to scale your business. Lack of IIoT Hurts Customer Relationships Customers care about support. If you can’t see issues before they arise, provide automated replenishment, and update their machine remotely, friction sets in. You are no longer making their business easier to run. You’re causing issues, downtime, and delays in orders, and damaging their reputation with clients. That friction is noticed—and often replaced. Remote monitoring isn’t about being innovative anymore. It’s about staying relevant. Lack of IIoT Prevents Scaling Most service providers that use OEM equipment need IIoT to scale their operations. Just look at their typical structure for support: Service techs have a set monthly visit schedule Issues arise and the customer needs to call in Service techs need to make a special visit to diagnose the issue Service techs need to visit again with the right part to fix the issue This structure requires more technicians, more trucks, more visits, and more fuel to serve more clients.  If you don’t have an IIoT remote monitoring system, you’ll eventually reach the point where taking on more clients is too costly to manage. What Makes Up an Effective IoT Monitoring System? An effective IoT remote monitoring system focuses on doing three things right: Simple to Set Up Most IIoT systems fail because they are too complex to set up.We’ve seen it multiple times. A business is ready to implement IIoT across its machines, but overreaches in the data it wants to capture. You don’t need to capture every bit of data possible on a machine — funneling data that doesn’t matter into a dashboard no one uses. A simple system that captures the right  data to drive business decisions is all you need. If it’s too complex, it’ll never be implemented and utilized correctly.  Shows Data in the Right Way Collecting data with IIoT is easy. Technology makes it possible to collect anything and everything about your machines. Knowing what data your team actually  needs is essential. A dashboard that dumps raw numbers on a screen without priority, context, or importance doesn’t help anyone. An effective IIoT remote monitoring system doesn’t just collect data; it displays the right data in the right way to help you make business decisions. Sparks Action This is the part that most systems miss. An effective IoT remote monitoring system should tell your team when to act, what to do, what is needed, and who should do it. That’s why our IoT remote monitoring systems focus on creating a prioritized to-do list: Service schedules Pick lists Reorder alerts Maintenance tasks Consummable restocking alert Turning your data into a prioritized to-do list makes your IoT remote monitoring system a business asset. Design an IoT Monitoring System Around Your Operation With Define Instruments Data Strategy Generic IoT remote monitoring systems struggle because every business operation is different. Different equipment. Different service models. Different customer needs. That’s why an effective IoT remote equipment monitoring system starts with a data strategy. Answering the questions: What decisions should this data support? What actions should it trigger? What problems should it remove? What data actually needs to be collected? What to-do list needs to be created? We approach industrial IoT solutions from that angle— starting by understanding how the business runs, how service teams work, and where time and money leak out. Only then do we design the remote monitoring system. If you want to see what your equipment could tell you, book a data strategy today . We’ll develop a strategy to build an effective IIoT remote monitoring system for your business.

The RTD (more commonly known as PT100) is one of the most used temperature sensors in industry. It is known to be the most accurate and repeatable sensor for low to medium temperatures (-300 to + 600 ° F.) RTD stands for Resistance Temperature Device. Quite simply, the sensor comprises of a resistor that changes value with temperature. The most common RTD by far is the PT100 385. This element measures 100 Ohms @ 0 degrees C (32 °F) and 138.5 Ohms @ 100 °C (212.0 °F). One of the greatest challenges for instrument engineers is dealing with the relatively low resistance of the device. This is because any stray resistance (in particular lead resistance) of RTD assembly can add a significant error to the measured resistance. To combat this, different lead compensation schemes were invented and have come to be known as 2 wire, 3 wire and 4 wire. The 2 wire technique The two wire RTD is the simplest form. The Lead R is the lead resistance of the wire connecting the RTD to the instrument. In this scenario the instrument is going to read a higher temperature than the true RTD temperature because the instrument measures: RTD + 2x Lead R For example if the lead resistance was 0.5 Ohms then the instrument would read 2.6˚C (4.7F) higher than it should. The only way to compensate this error is to manually adjust the offset of the instrument. This of course becomes tedious and prone to human error. Automatic lead compensation instruments were invented to address this problem. The compensation techniques use additional wires connected to the sensor to measure the lead resistance and negate its effects. The 3 wire technique The three wire lead scheme requires two measurements, the first measurement is V1 which gives a result for RTD + Lead R. The second measurement gives a result V2 for R Lead. Hence to get the true RTD measurement we simply subtract V lead from V lead + RTD leaving RTD. Hence for any Lead R value this scheme will automatically compensate out the lead resistance and give you the correct temperature. The assumption this technique makes is that the lead resistance is the same in each of the three wires. This is a very safe assumption to make in particular with modern manufacturing techniques used in wire production. In the practical examples section you will get more of a feeling how these errors stack up. The 4 wire technique This technique relies on a very high input impedance of the modern instrument so that in the sensor wire there is practically no current flow: this is a very valid assumption today. The RTD is sensed in the scheme with no error by measuring VRTD in one measurement. The advantage of this scheme is that it also compensates out any lead wire imbalances. Historically the 4 wire technique has been popular in Europe led by the German influence for absolute precision. In the North American market the 3 wire technique has been much more widely deployed in the past and even today outsell the 4 wire sensors by 3 to 1. This has been led by cost and practicality. Practical examples Headmount transmitter using 24 AWG wire to connect the RTD sensor to the transmitter with a probe length of 12” Headmount transmitter using 24 AWG wire to connect the RTD sensor to the transmitter with a probe length of 12” From the results above for headmount applications both 3 and 4 wire are excellent techniques for eliminating lead resistance effects. Furthermore, the long cable test also shows 3 to 4 wires to be perfectly adequate. Even if there is some wire imbalance, the calculated error puts it firmly in the uncertainty band of almost all industrial applications. Another question that is sometimes asked is: “ If I have a 4 wire RTD can it be used as a 3 wire RTD?” The answer is yes, leaving one wire disconnected from the 4 wire sensor will not add any error to the 3 wire system of lead compensation. However the opposite is not true. You cannot use a 3 wire RTD with a 4 wire instrument by simply shorting the 3rd and 4th wire together at the instrument. This will result in substantial lead wire resistance error. In conclusion 2 wire RTD inputs should be avoided altogether unless the wire lengths are short and you are using a low gauge wire to reduce the lead resistance. 3 and 4 wire compensation techniques have been proven over many years to provide an excellent means to automatically compensate lead wire resistance in RTDs. You would choose 4 wire if you are concerned with absolute precision over long lead lengths. Whereas it has been shown that the 3 wire technique is accurate for all practical industrial purposes and that it saves around 20% in wire cost over the 4 wire technique.

The white paper " Trends in Remote Services & Monitoring ," published by PMMI in January 2024, takes a closer look at evolving landscape of remote services in the packaging and processing industry. Drawing insights from 144 end-user companies and 36 Original Equipment Manufacturers (OEMs) in the USA, the report highlights the increasing adoption of remote technologies and the factors influencing this shift to this technology. Adoption of Remote Services End-users are progressively integrating various remote services into their operations, including: Virtual Factory Acceptance Tests (FATs):   Utilizing video conferencing and streaming, equipment suppliers conduct hybrid FATs, combining remote streaming with a minimal on-site presence to ensure machinery meets specifications. Remote Support:   When internal teams encounter challenges, they seek assistance from equipment suppliers through phone calls, video conferencing, or augmented reality, enabling detailed remote technical support. Remote Commissioning:  Employing video conferencing, end-user plants perform commissioning with remote guidance from equipment suppliers, sometimes using a hybrid model with limited on-site representation. Remote Training:  Equipment suppliers provide training to end-user technicians and operators remotely, eliminating the need for on-site visits. Remote IoT Monitoring Services:  Leveraging IoT technology and cloud computing, both end-users and equipment suppliers monitor machine performance, status, and behavior from a distance. Predictive Maintenance:  An advanced monitoring approach that assesses machines or components to predict potential failures, allowing for proactive maintenance. 4 Reasons to Invest in Remote Monitoring Systems Here are some things to consider when investing in remote services and monitoring: (1) Operational Efficiency:  Remote services reduce downtime and enhance productivity by enabling swift issue resolution without the delays associated with on-site visits. (2) Cost Reduction:  Minimizing the need for travel and on-site interventions leads to significant cost savings for both end-users and equipment suppliers. (3) Access to Expertise:  Remote services provide immediate access to specialized knowledge, ensuring that complex issues are addressed promptly. (4) Data-Driven Decision Making:  Remote monitoring and predictive maintenance offer valuable data insights, facilitating informed decisions and strategic planning. Adoption Barriers of IoT Monitoring System Despite the advantages, certain challenges hinder the widespread adoption of remote services: IoT Security Threats:  Allowing remote access to plant operations raises cybersecurity issues, necessitating robust measures to protect sensitive data. Infrastructure Limitations:  Some facilities may lack the necessary infrastructure or face compatibility issues with existing systems. Resistance to Change:  Cultural resistance and a preference for traditional methods can impede the acceptance of remote services. Addressing Skills Gaps Remote services play a crucial role in mitigating skills shortages by: Providing Remote Expertise:  Enabling access to specialized knowledge without the need for on-site presence. Facilitating Training:  Offering remote training programs to upskill existing staff efficiently. What These Trends Mean for Your Data Strategy The white paper suggests a continued shift towards remote services, with a hybrid approach combining remote and in-person interactions becoming the norm. This evolution is expected to enhance operational efficiency, reduce costs, and improve access to expertise across the industry. In other words, the "Trends in Remote Services & Monitoring" white paper underscores the transformative impact of remote technologies in the packaging and processing sector. By embracing these trends, companies can navigate the challenges of modern manufacturing and position themselves for sustained success. Start Your Data Strategy and Build a Remote IoT Device That Pays Off

If you need to get multiple signals from various types of sensors into a PLC or SCADA system  and you’re pushed for space , specify the new Zen RTU Mini from Define Instruments. The Zen RTU Mini goes where others cannot The  Zen RTU Mini  measures just 3.98 x 1.38 x 4.42″ fitting into tighter spaces, smaller enclosures and saving you DIN rail space! No need to run cables The Zen RTU Mini has a wireless option so you retain portability, and don’t need to spring for the cost of cabling. Universal input for maximum flexibility The Zen RTU Mini accepts TC, RTD, mA, mV, V, Frequency and Pulse. This reduces the number of separate instruments required for your application, further reducing costs and keeping things simple for troubleshooting and maintenance. Per-channel isolation for high reliability Each channel of the Zen RTU Mini is galvanically isolated ensuring a clean and steady signal in even the harshest of industrial environments. Don’t pay for channels you don’t need The Zen RTU Mini comes in 4, 12 and 16 channel options, so you only pay for what your application requires. Get the  full specs  »

Understanding your WiFi network and how to successfully get your IoT device on to your network can be tricky for the uninitiated. In this guide you’ll learn some networking basics to help you along. WiFi and Wireless, what’s the difference? Wireless is a generic term that just means there’s no wires. The term “wireless” tends to be used to describe a requirement i.e. “it needs to be wireless”, whereas WiFi is a particular standard, and one of several others like Bluetooth or Zigbee, for example. Access Point Mode or Station Mode? Typically devices can run in one of 2 modes: Access Point Mode or Station Mode (often called Client Mode). Station Mode (STA) is what most people would consider the normal mode for a WiFi device. A device uses Station Mode to join a network that already exists, exactly like your smartphone does when its connects to your WiFi network at home. In this instance your phone is running in Station Mode. In Access Point Mode (AP)  the device is the Access Point  and so becomes an entity that everything else can connect to, rather than it connecting to a network. In an industrial IoT context, Access Point Mode is generally for set up and then once configured the unit will exit AP mode and run in Station Mode for the rest of the  IoT application. Demystifying IP addresses An IP address is a string of digits that define the location of a device on a network. The address comprises of 4 groups of numbers separated by dots and it is very much like a street address in that it must be unique. IP addresses can be static or dynamic (more on this later). In a domestic scenario, typically when a device connects to your home network it is dynamically assigned one by the router from the addresses currently not in use. IP addresses have banded designations: a numerical range of addresses that have been reserved for specific uses. What does DHCP mean? DHCP stands for Dynamic Host Configuration Protocol. It is a network protocol that allows a server to automatically assign an IP address to a device from a defined range of addresses. Basically, when a device logs on to your network and requests an IP address from the Access Point, it will be assigned one automatically from the remaining free addresses. At some point this IP address may get reassigned to another device, depending on network traffic – it’s not fixed. It may last for a couple of days or a couple of weeks depending on your network. However, if the device is being used every day it will likely keep the same IP address. Most networks have DHCP enabled and so during setup of Define Instruments  products with Wifi modules , if the “Use DHCP” checkbox in the WorkBench configuration software is ticked then your device can automatically be assigned a unique IP address. What are Port numbers? If your IP address is like a street address, a port is like a cubbyhole at that address where incoming mail is sorted: one for bills, another for letters, another for junk mail, etc. Different ports for different types of communications. For example: for Modbus, generally port 502 is used. That’s because this port number has been purchased by the Modbus Organization for exclusive use with devices using Modbus communications. Port numbers range from 1 to 65,535. A list of commonly known and registered ports numbers  can be found on Wikipedia . Where do I find which port number to use? Generally, this is determined by the protocol you’re using, e.g. MQTT has a specific port number (port 1883) and so do websites (port 80 for http) refer to the above link for guidance on the standard port assignations. Otherwise, talk to your System Admin and they can help you identify a port that is not in use by any other protocol on your network and can be dedicated to your IoT application.

In an emerging market initially there tends to be a glut of vendors, a plethora of early adopters who have spotted the opportunity to get in on the ground floor. But as the market matures, vendor numbers dwindle until typically only a handful remain. This natural attrition comes in many guises but one of the most the powerful is action from a major player. And this is what we have seen recently from communications giant Verizon and their launch of web-based IoT development platform, ThingSpace. Verizon says “ThingSpace is a gateway to a simplified IoT workspace and machine-to-machine (M2M) management center for prototype through production…to bring your IoT solutions to life, and to market.” While news of another IoT development platform isn’t much of an eyebrow-raiser  what is,  is the decision by Verizon to offer CAT-M1 modules from USD 6.50 each, and to allow free certification on their network and additionally provide 100 hours of expert help. The axe has fallen By its actions, Verizon has clearly indicated its intent to capitalize on the billions of IoT devices predicted to come online in the next few years, and by subsidizing hardware connections, its service becomes a very attractive option. Furthermore Verizon is one of the few companies who already have the infrastructure in place to support this. This latest move will likely secure substantial market penetration for Verizon while at the same time potentially culling several existing players from the market. This “market cleansing” action has significant impact for Sigfox and LoRa and may signal the death knell for these LPWA technologies in the U.S. market. READ:  The completely overlooked but drastic cost savings municipal water departments can achieve with this simple IIoT application The advantage is clear CAT-M1 has huge advantage over both SigFox and Lora in the type of applications that can be achieved with it. With a direct TCP connection from the sensor and no third-party servers, gateways or services required to connect to the internet, CAT-M1 is in a strong position to significantly reduce cost, complication and latency whilst still allowing for complete flexibility. The introduction of CAT-M1 modules at this price point means Verizon can now also compete in the simple, low-frequency (time), low-data applications that Sigfox and LoRa were invented for e.g. daily automatic meter reading. In my opinion any advantages that Sigfox or LoRa thought they had are now dwindling fast in the light of this power play by Verizon. The comparison table below summarizes some important points in determining which direction device manufacturers should head. Comparing Sigfox, LoRa and CAT-M1 Verizon CAT-M1 Sigfox LoRa Network operated by a Fortune 500 company? U.S. nation-wide coverage complete? Device operating in protected licensed frequency? Latency between web and device? (very good/good/poor) Battery life for simple applications (very good/good/poor) Hardware cost for simple applications* (very good/good/poor) * Simple application defined as obtaining a meter reading for an existing meter and posting to the internet once per day.

Compressed air is widely used in manufacturing plants across the United States. And, if your company relies on it, you’ll know that it’s as vital as electricity to ensure the continued running of your operations. For example, in an electronics manufacturing facility compressed air is used to power automatic assembly equipment and continuous air flow is critical to maintain production. Without reliable air delivery, manufacturing will cease and the resultant downtime could lead to tens of thousands of dollars in lost revenue. Given the “lifeblood” status of compressed air it is interesting to note that maintenance and monitoring of the system as a whole is, for the most part, overlooked. The servicing that does occur is usually focused on the hardware in the compressor room, which does need attention but is only a part of the overall system. Most problems occur in the piping distribution system. Typical issues are things like rust, leaks or incorrect pipe size and are typically straightforward to fix but without the monitoring of this piping, troubleshooting can be time-consuming and expensive. For full visibility of the entire compressed air system, sensors must be deployed in key locations inside your facility. These sensors should measure pressure, vibration, flow, temperature, humidity and power. The cost of lost pressure Around 2psi of air pressure equals 1% of a compressed air systems total energy cost. This means a system with less than optimum air pressure is wasting energy and wasting money. Pressure loss is commonly caused by: distribution pipe corrosion incorrectly sized piping incorrectly sized compressor capacity lack of compressed air storage Just one of these can result in your compressor working far harder than it should and will contribute to shortening the working life of the unit. Operational oversight also prevents time and money being wasted on taking action that appears to be the solution but in actuality is not. For example, one may conclude loss of pressure is due to your compressor capacity not being large enough for the task. But only after investing in and installing a newer, larger compressor, it is discovered that this does nothing to alleviate the pressure issues. Full visibility of the system could identify the real culprit – a corroded pipe – without such expense and also indicate exactly where pressure starts to drop. Of course, you could take pressure measurements manually with a pressure gauge but these readings only provide information for that one point in time. Publishing sensor data to the Cloud provides rich and detailed data giving you solid business intelligence to make better decisions. Dealing to the problems caused by humidity Excess moisture causes corrosion in pipes and damage to internal components impacting maintenance costs and raising the risk of downtime. It also causes problems in certain finishing applications and, in food and beverage applications, can breed harmful bacteria that spoils or contaminates ingredients or finished foods. All the more reason to get clear on the impact humidity is having on your facility. Measuring humidity in your plant allows you to take necessary action to mitigate its effects, reduce your costs and comply with any applicable health and safely regulations. Monitoring flow for first indications of issues As already stated, a common causes of pressure loss are incorrectly sized piping or corroded piping which restricts air flow in the pipe. Undersized piping is often overlooked as what was appropriate and correct at time of installation is in many cases now too small to keep up with current demand. Leaks can also cause your air system to run inefficiently, this can be caused by threaded connections during a less-than-careful installation. Over time a threaded connection will begin to separate, opening a space for air to pass through and escape. Monitoring flow data acts as an early warning mechanism beyond visual inspection because the inside of pipes corrode without noticeable decay of the pipe exterior. Poor flow readings indicate corrosion, threading or pipe size issues long before they become critical. Temperature monitoring as an overall indicator Monitoring the temperature of various components in your compressed air system provides an indication their overall health. By comparing current component temperatures to the manufacturers documented optimal working temperatures, one can see if the system is being over-worked or under is performing. Power consumption monitoring With the above sensors installed, the last metric to monitor is power consumption – do this by installing a current sensor. Used in conjunction with the data from the flow sensors the overall health of the compressed air system can be determined. And from this it is easy to calculate the cost per unit of your compressed air set-up. Increases in costs can reveal issues such as faulty controls, short cycling and unregulated spiking. Compressed air is the lifeblood of many production facilities Once the sensors and cloud monitoring is deployed, you finally achieve operational oversight of your system. With this comes insights on the idiosyncrasies and behavior of the system, component lifespan, real servicing requirements, usage profiles and whole lot more. Compressed is air is vital to so many manufacturers, what are you doing to safeguard your supply?

This article explains LTE CAT-M1 in relation to its competing technologies and examines some of the pros and cons of each. The telecommunications industry now has a new, IoT-friendly standard: CAT-M1. CAT-M1, sometimes referred to as LTE-M1, LTE CAT M1 or CAT M, it is a technology that enables connection directly to a 4G network without a gateway, connecting IoT devices to the internet via the cellular network. The first advantage One of several technologies known collectively as an LPWA (Low Power Wide Area), the CAT-M1 network is operated by cellular network providers utilizing their own frequency bands. It is this ownership that is the first notable advantage over competitors such as SigFox and LoRa WAN. By controlling the devices that are able to use their network, telco’s have secured the long-term quality of this service. Meanwhile, competitors Sigfox and LoRa have opted to use unlicensed ISM (Industrial, Scientific, Medical) bands. The blessing and curse of unlicensed bands is that they are free for everyone to use. The blessing and curse of unlicensed bands is that they are free for everyone to use. This may jeopardize quality of service in years to come as neither Sigfox nor LoRa have any influence should the behavior of other users of these frequencies become harmful or disruptive to their customers. The use cases are different for different regions and some rules in some countries are not kind to LPWA networks. For more information on this please  see article by David Castells-Rufas, Adria Galin-Pons and Jordi Carrabina No need to shout loud Cellular network operators also have another advantage: they use cell technology. As a rebuttal competitors claim that their tech needs fewer “tower points” to provide coverage compared to conventional cell operators because it can transmit over longer distances. On paper, this would seem like an enormous benefit with regard to infrastructure costs. However, this is achieved by the devices transmitting on full power at all times. To get an idea of the implications of this, let’s relate it to human interactions. Firstly Sigfox and LoRa: Imagine a room half full of people. Certain individuals are permitted to shout messages to others across the room. They do so by following these rules: Messages can only be yelled once every 10 minutes Messages must be repeated 3 times (in case someone else is also yelling at that moment) As the room fills up the amount of messages that everyone is allowed to share has to decrease to accommodate the growing number of shouters. So this type of messaging works best with short one-way message payloads. But with cell technology human interactions would be more like the following: People in the room are split into small groups. They huddle together so there’s no need for shouting and relevant messages can be heard above the low volume of chatter from the other groups. Much more information can be shared this way. LTE CAT-M1 best positioned for the coming IoT revolution Until now the primary driver for cell network providers has been to service the insatiable human demand for live streaming HD video and music directly to mobile devices. But it has been determined that most IoT devices do not require this kind of bandwidth and so CAT-M1 has been optimized for this lower bandwidth IoT world. While the looming 5G promises bandwidth speeds of 1Gbit per second, CAT-M1 is happy to play at around 256Kbits per second. One of the by-products of this is a significant increase in coverage as by reducing RF bandwidth, signal-to-noise ratio increases. A report by AT&T  suggests that this technology can increase coverage by a factor of 7. In practical terms this means that in locations where 4G fizzles out CAT-M1 continues to work just fine. While 5G promises bandwidth speeds of 1Gbit per sec, CAT-M1 is happy to play at 256Kbits per sec. Consider the benefits of this coverage to devices in remote country areas or deep in the basements of buildings. Where once it was virtually impossible for signals to reach these locations CAT-M1 technology now makes it possible, and best of all there is no wait time while networks are built – they already exist! Some of the other advantages that CAT-M1 provides: Supports native TCP  which features TLS (Transport Layer Security), encryption and security certificates. Direct connection to leading cloud providers  like AWS, Microsoft Azure, Google cloud, et al without routing through third party servers. (Sigfox sends all data from Sigfox devices to their servers in France before redistributing it to other cloud providers.) Supports bi-directional data and always connected states . With latencies of less than a second this is perfect for alarm monitoring and remote-control applications. Bandwidth is enough to support voice calls and still photos . Also great for alarm systems Supports OTAU (Over The Air Updates)  essential for future-proofing IoT applications. By enabling software updates to be deployed remotely, it removes the need to visit the (possibly far removed) location. Initial and ongoing cost considerations Because CAT-M1 is a more complex unit than the likes of Sigfox, the amount of silicon required is greater and costs should therefore be higher. However some prices sighted have been as little as $6.50 US. It’s likely that this pricing is the result of telecoms subsidizing traffic to enter their network. In the free market the price is a more realistic $20 US per module but these prices are expected to fall over time. Some plans on offer start at $0.85 US per month for a limited data plan and $1.50 US per month unlimited data at 256Kbits per second. With such affordable choices an application with 150 sensors updating to the Cloud every minute could cost as little as 1 penny per month per sensor. Into the future CAT-M1 has been a valuable and welcome addition to the LPWA IoT landscape. Although it has arrived late to the party, it is well thought out and brings numerous advantages. It promises to be a reliable and well-maintained option. More akin to a Toyota Corolla than a pre-war Volkswagen Beetle.