A Starters Guide to the IIoT and Automation in Agriculture
Is your farm smart? Speak to a Freewave Smart Ag expert today. What is automation? How can automation help farmers do more with less? Is automation in agriculture a good idea? How is automation transforming the farming industry? Farmer’s exploring technology tend to ask the same questions. Here are a few of them: What is automation? How can automation transform my farming operation? Is automation in agriculture a sound practice? Why do we need smart agriculture? What is smart farming? There’s a new world of technology available to farmers. But with new technologies come new fears and confusion. It’s understandable. Farmers are afraid they’ll be left behind if they don’t embrace new agricultural technologies. They’re also worried about failure, which means many farmers are unwilling to experiment with new technologies out of a fear of losing it all. Unfortunately, their fears have resulted in an industry that’s resistant to change. Still, many farmers have embraced automation. They’ve invested not only their dollars but also their time. We don’t want to sugarcoat the subject; embracing technology isn’t an easy task. Workforce training is a significant barrier to entry for many farmers exploring automation. A farm’s workforce is diverse, often consisting of temporary, part-time, and permanent employees. The transitory nature of a farm’s employees can make technological training challenging. But the process is less expensive and less time-consuming than you might think. Farmer’s are always looking for larger yields from fewer resources, and for most, automation and the Industrial Internet of Things (IIoT) is the answer. IIoT improves agricultural operations with real-time data insights and control to enable more efficient and precise management of crops, resources, and livestock. You might have a cursory – or perhaps in-depth – knowledge of precision agriculture. For the rest of you, first, let’s talk about automation in general. What is automation in agriculture? Farm automation is an aspect of “smart farming.” It’s a technology that improves farm efficiency and automates the livestock or crop production cycle. More companies are developing agriculture-specific technologies to automate these processes with automatic watering, autonomous tractors, robotics, harvesters, and automated seeding machinery. Smart farm technologies are still relatively new, but we’ve seen growing numbers of traditional agriculture companies embrace farm automation. Even small farming operations use automation. Kyler Laird, a farmer in Indiana with a 1,700-acre farm and an engineering degree, developed autonomous machines to complete tasks like harvesting, drilling, and planting crops. He spoke to agriculture.com writer Laurie Bedford in 2017 and explained that “I’m a one-person operation. I need this technology because I really can’t afford to hire anyone. Besides, finding a skilled operator who is willing to work 24 hours a day for three or four days a year is ludicrous. I can’t hire that, but I can make that very inexpensively.” “I’m a one-person operation. I need this technology because I really can’t afford to hire anyone. Besides, finding a skilled operator who is willing to work 24 hours a day for three or four days a year is ludicrous. I can’t hire that, but I can make that very inexpensively.” – Kyler Laird, Farmer Most farmers can’t design and implement smart technologies, but you don’t have to – that’s our job. By using farm automation technology, however, farmers can drastically improve outcomes and spend far less money and time in the long run. What is “smart farming”? Smart farming, sometimes called a “third green revolution,” applies new information and communication technologies in agriculture. The technological farming revolution includes IIoT (Industrial Internet of Things), precision agriculture equipment, actuators and sensors, geo-positioning systems, big data analytics, robotics, unmanned aerial vehicles (drones), and more. You can transform your operations to deliver more sustainable and effective agricultural production through smart farming. Smart farming also benefits the environment through more efficient water use and optimizing inputs and treatments. “Smart farming can make agriculture more profitable for the farmer. Decreasing resource inputs will save the farmer money and labor, and increased reliability of spatially explicit data will reduce risks. Optimal, site-specific weather forecasts, yield projections, and probability maps for diseases and disasters based on a dense network of weather and climate data will allow cultivation of cops in an optimal way.” – 2017, PNAS Here are a few ways IIoT can improve modern agriculture: Smart ag sensors collect data surrounding soil quality, weather, crop growth, and herd health so you can track the state of your business, equipment efficiency, and workforce performance. IIoT gives you more control over internal processes and lowers production risks. You can improve distribution forecasts with better production output visibility. IIoT gives you more production control to reduce waste and improve cost management. When you can monitor crop growth or herd health anomalies in real-time, you can lessen the possibility of yield loss. IIoT process automation increases business efficiency. Smart devices allow you to automate critical production cycle processes like irrigation, pest control, fertilization, and more. Automation can enhance product quality and output. Agriculture automation gives you more control over production processes, helps you maintain higher crop quality standards, and enhances growth capacity. Sample Agricultural Automation and IIoT Use-Cases Grain-Bin Level Monitoring and Control Agriculture automation gives farmers real-time visibility into storage conditions and ensures blowers only operate during off-peak electrical hours, saving as much as 50% in energy costs. Automated Irrigation and Compliance Precision agriculture technologies let you schedule off-peak hour irrigation, allowing you to save as much as $30,000 per year in energy costs. You can automate water consumption reporting processes to ensure regulatory compliance. Herd Health Tracking Smart ag technology helps farmers monitor feed intake to improve livestock health and mitigate feed shrink. Self-Driving and Autonomous Tractors Real-time kinetics from precision agriculture technologies improve guidance and steering accuracy up to 100 times compared to traditional GPS. Intelligent Weed Control The Industrial Internet of Things (IIoT) powers high-accuracy robotic weeders to reduce herbicide consumption by 20%. We want to infuse intelligence into your agriculture operations. Making the most of automation in agriculture means ensuring field
FreeWave’s Industrial Internet of Things (IIoT) Glossary
Our exploration of terminology related to the Industrial Internet of Things. The tech world is full of buzzwords and terms unfamiliar to most people, many of which you can ignore. But there comes a time when it’s essential to walk the walk and talk the talk. Our IIoT glossary comes in handy for those moments. Here is a list of relevant IIoT terms we think you should know. Access Control Access control ensures that asset access is limited to authorized personnel and is restricted based on security and business requirements. Ambient Computing Ambient computing is the evolution and combination of gesture and voice interfaces, speech recognition, cloud computing, wearable computing, IoT, augmented reality, AI and machine learning, and the quantified self. Analytics Analytics is a systematic analysis of information (data) or statistics for the discovery, communication, and interpretation of meaningful data patterns for better decision-making. Application Domain An application domain is a functional domain for application logic implementation. Artificial Intelligence Artificial Intelligence (AI) is the development and theory of systems that can perform tasks typically requiring human intelligence, like speech, visual perception, decision-making, language translation, and speech recognition. Asset Assets are mission-critical systems, physical hardware, applications, support systems, high-impact programs, personnel, equipment, locations, and more. Attack Surface Attack surface refers to the system elements and interactions that are vulnerable to cyberattacks. Attack Vector An attack vector is a pathway by which a cybercriminal can gain access to an entity. Autonomy Autonomy is an intelligent system’s ability to independently create and select different courses of action to achieve goals based on the system’s understanding and knowledge of the world and other factors. Brownfield Brownfield refers to an existing industrial system targeted for new functionalities with zero operational disruptions. Business Intelligence Business intelligence refers to the applications, technologies, and practices for collecting, analyzing, and integrating business data to support improved business decision-making. Business Viewpoint A business viewpoint is an architecture viewpoint used to describe the purpose of establishing an IoT system by encompassing a business’s vision, mission, values, and objectives. Cloud Computing Cloud computing is a term used to describe the delivery of computing services over the Internet. Computer Network A computer network is a collection of interconnected endpoints in a many-to-many arrangement. Connectivity Connectivity refers to a system or application’s ability to communicate with other systems, networks, or applications. Connectivity Endpoint Connectivity endpoints are interfaces that provide connectivity. Control Domain Control domains are functional domains for industrial control system implementations. Cross-Cutting Concern A cross-cutting concern impacts an entire system and may also affect multiple architectural viewpoints. Cross-Cutting Function A cross-cutting function is one that can be applied across multiple functional architectural domains to address cross-cutting concerns. Cryptography Cryptography exemplifies the means, principles, and mechanisms for data transformation to hide information to prevent its undetected modification or unauthorized use. Data at Rest Data at rest is stored data that is not processed or transferred. Data Center A data center is a facility that contains connected equipment for computing resources. Data in Motion Data in motion is information that’s transferred from one location to another. Data in Use Data in use is information that’s being processed. Data Integrity Data integrity proves that data hasn’t been tampered with, altered, or destroyed in an unauthorized way. Databus Databus is a data-centric sharing system where applications exchange information in a virtual, global data space. Denial of Service (DoS) Denial of service prevents unauthorized access to resources. It also prevents time-critical operation delays. Digital Twin A digital twin, also called a virtual doppelganger, is a three-dimensional representation of IoT-enabled physical assets that can show how the asset is functioning. ECM (Electronically Commutated Motor Technology) ECM is a smart pump technology that is becoming more common in HVAC and commercial building markets. Edge The edge is the boundary between pertinent physical and digital entities, delineated by IIoT devices. Edge Computing Edge Computing encompasses the space between the network’s core and its endpoints, like local servers, including devices and infrastructure. It also includes essential network gateways that collect data and preliminary analytics before sending the information back to the core for more processing. Emergent Behavior Emergent Behavior is the behavior of a system created by the interactions of its component. Encryption Encryption is a scrambling method using a cryptographic algorithm so that only authorized people can understand the data. Endpoint Endpoints are components with computational capabilities and network connectivity. Environment Environment refers to the circumstances and setting of all IT system interactions, including infrastructure, hardware, systems, and software. Functional Component Functional components are necessary JavaScript actions. They are typically arrow functions but can also be made using the regular function keyword. Functional components are also often called “stateless” or “dumb” because they accept display data in a form mostly responsible for rendering UI (User Interface). Functional Domain Functional domain refers to the collection of functions that make up a system. Functional Framework Functional frameworks are sets of abstract and reusable functional components that can be customized, extended, and applied to many applications in a specific domain. Functional Viewpoint Functional viewpoints are architecture viewpoints that frame concerns specific to the structure and functional capabilities of IoT (Internet of Things) systems and components. Greenfield Greenfield refers to a new industrial system with no operational disruption concerns. Identity Authentication Identity authentication is a formal process of identity verification. Identity Domain An identity domain is an environment that allows an entity to use a set of identification attributes and related purposes. Identity Access Management Identify access management keeps data secure by defining and managing the roles and access privileges of individual network users. It also controls the circumstances under which users are granted – or denied – access permissions. Implementation Viewpoint The implementation viewpoint is an architecture viewpoint to address
Manufacturing in the Age of IIoT
Few industries can claim such a foundational impact on the United States as the manufacturing industry. Modern manufacturing began with the birth of the assembly line and the transformational effect it had on the automobile industry. Companies then adopted that approach to product manufacturing and logistics. The early phases of the next generation of manufacturing appeared as machine-to-machine (M2M) communication, a forbearer of the concept behind the Internet of Things (IoT). Eventually, IoT became so broad that specific designations were needed to differentiate between the consumer and industrial side of things, thus paving the way for the Industrial IoT (IIoT). Today, manufacturing companies, while often on the leading edge of automation technology, are still scrambling to adapt to the explosion of sensors, communication platforms, big data and high-speed analytics to maximize efficiency and future-proof their products or designs. Some companies are touting the idea of retrofitting – a concept that has existed for some time – but some plant engineers may be wary of the need for continual updating to a system that is bound to become irrelevant at some point. Still, the process can be relatively painless, and is quickly becoming necessary, as Plant Magazine notes: … Most food manufacturing and processing plants have motors powering essential equipment such as mixers, conveyors and packaging machines. But they’re just motors. They don’t play in the same league as other intelligent devices. With years of service to go, it’s difficult for plant managers to justify replacing motors that work just to make an upgrade with smart features. But motors can connect to the IIoT without a complete overhaul. Instead of investing in new, more intelligent/smart equipment, consider investing in sensors that provide similar functionality to connected devices. Smart sensors attach to almost any standard low-voltage induction motor. Sensor technology is sophisticated enough to be small, functional and energy efficient. For certain kinds of manufacturing plants, a complete overhaul may not be necessary, and a ‘simple’ retrofitting process might easily solve the first part of the problem. The second part of the problem, or challenge, is that along with smart hardware, plants also need the software and data processing capabilities to keep pace. Some plant engineers are solving these challenges by deploying programmable radios capable of hosting third-party applications so that the data can be transmitted in smaller, highly specific packets, making the transport both fast and easier to push into predictive analytics platforms. From there, software companies are building in the ability to process data in the cloud, essentially running all critical data and software operations through either a fog or cloud computing process. Cloud software services have the potential to be highly customizable based on the needs of the manufacturing plant. These technologies are good examples of the ongoing convergence between traditional information technology (IT) and operations technology (OT) needs in industrial markets. Currently, the manufacturing industry is sitting in an interesting spot: leaders in the M2M world, but still adapting to the IoT world. Where the industry ends up in the next 10 years could be a strong indicator of the economic and financial temperature of the domestic and international marketplaces.
A Very Thankful Time of Year in IIoT
Thanksgiving is the time of year where we reflect on the accomplishments of the prior year and to be thankful for all that we have
Sensor-2-Server: Benefits & Security for IIoT Communications
*This is part of a series of blogs examining Sensor-2-Server (S2S) communications, development, security and implementation. For the past two weeks, we’ve taken an in-depth look at what Sensor-2-Server communications are, how to implement these systems, and some of the specific aspects of communication that these systems facilitate. This week, for our final installment, we’ll examine some of the benefits, as well as security considerations, for S2S communications. Benefits of Sensor-2-Server Communications From a technology partnership perspective, Big Data vendors face the challenge of comparing data in motion versus data at rest. If the data has already moved through a SCADA system and has been aggregated, changed, stalled, or is not quite granular enough, it can be difficult to deliver high-value predictive analytics. The concept of predictive analytics is that an operator can make an accurate estimate that certain things can happen during operations. However, the operator needs to determine what the drivers are for the predicted actions to happen and must look at active data to determine if this is, in fact, happening. Without insight into the active data in motion, they are lacking an essential piece of the predictive analytics. This ability to compare data in motion at the access layer could benefit Big Data vendors when it comes to predictive analytics because it allows them to give higher value to their customers, which drives additional revenue. With S2S technology, they can deploy a tiered application infrastructure that allows data to intelligently move from one point to another. S2S also enables operators to go beyond a legacy SCADA data network. To operate a SCADA network, it requires a lot of institutional knowledge to truly understand, manage and work within the environment. S2S expands beyond moving the data into SCADA systems and allows operators to leverage more advanced technology, like predictive analytics. Essentially, S2S communications provide the opportunity to take advantage of new advanced tools, but the operator doesn’t necessarily have to sacrifice the institutional knowledge built into the SCADA data systems. As new generations enter the workforce, it’s likely that there will be a shift and some of that institutional knowledge will be replaced with technology that will allow operators to do more than they ever could before. The addition of new technology and IoT networks is where operators are starting to see the functional lines blur between the IT and production groups. As more technology is leveraged, these two disparate groups will have to work together more often. There is now a drive for a more holistic picture of what is going on in IT, what is going on in the field, and whether the technology used will be compatible with future needs. SCADA will likely always have value for industrial communications but, going forward, there will be an increase in the use other technologies as well. Additionally, with more technology physically in the field, there is always going to be a focus on data security. Security Sensors at the access layer present interesting security challenges. For example, consider a data concentrator sitting on an oil pad that is collecting data. This device is collecting data from a number of sensors and has data logging capabilities, which also means the other devices sitting at the remote site contain historical data. Technology providers need to insure that the technology used is taking advantage of all the security features that are available to make sure their data is protected through a variety of means including encryption, authentication, virus and intrusion protection, and by being physically tamperproof. With the growing interest in IIoT, the system is providing a communication path with highly valuable information. These sensors may be running an application on the edge of the network, and many of these devices are using IP. When there are Ethernet and IP devices going out to edge devices in the field, each one of those devices has the potential to become a threat to the entire corporate network if they’re not secure. Operators in IIoT environments need to be concerned with everything that could be introduced to the network at every single connection point. Data protection data is a fundamental and extremely important element in determining the effectiveness of S2S communication. Technology vendors must be mindful of security in every step of the design and installation process, and operators must require security features that will protect their data and networks. In addition to data security, the threat to physical infrastructures in very remote locations is driving the need for new security solutions such as intelligent video surveillance designed to maximize security and minimize cost. S2S solutions need to be physically capable of delivering the bandwidth to enable these new solutions. Where Do We Go From Here? Industrial communication is changing in the sense that IIoT enables the possibility for every device in a network to be connected – including those in the outer access layer. This has created a convergence of OT and IT operations in many instances or – at the very least – has brought the two departments to a closer working capacity. IoT and technology at the access layer enable the option for Sensor-2-Server, a form of intelligent communications that can move the sensor data to a specific server for detailed analysis. New data and technology are allowing operators to do things they’ve never done before, such as predictive analytics. As this shift continues, SCADA is not becoming an obsolete technology; rather it will become a piece in the bigger technology picture. Any operator choosing S2S technology, or any technology for that matter, must carefully consider the options and keep security as a top priority.
IIoT Bold Prediction Series Part 5: Discrete RF Manufacturers Obsolete in Three Years
What a week it has been for the connected world! As we grow closer to the end of 2015, there are plenty of movers and shakers in the IoT space, and for good reason – the excitement around the industry is palpable. In fact, it’s hard to keep a pulse on all the activity as there seems to be innovations occurring daily. Additionally, the IoT provider ecosystem itself continues to grow rapidly as the influx of companies – from device manufacturers and software vendors to IT and Cloud services as well as industry groups and regulators – continue to push the bounds of possibility not just for consumers, but businesses as well. To further add to the end of year developments in IoT, our 2016 IIoT Bold Prediction Series ends the week with a bang – after all, it’s not every day that the CEO of a company predicts the demise of its own industry! However, Kim Niederman, CEO of FreeWave Technologies, is making the bold prediction that discrete Radio Frequency (RF) technology manufacturers will be obsolete within the next three years. Prediction #5: The obsolescence of discrete RF manufacturers will occur by 2019 The catalyst that will drive this change will be the open standards in place that will eventually commoditize the market by bringing backwards compatibility and interoperability between different radio manufacturers. Large chip manufacturers are going to drive physical layer standards, meaning the chipsets themselves are going to be more ubiquitous and will make it increasingly difficult for discrete radio manufacturers to find and capitalize on business opportunities in the marketplace. Companies will continue to drive the adoption of open standards and the concept of the software-defined radio will soon become meaningless. Stay tuned for more on this bold IIoT prediction!
