Illustration: © IoT For AllScaling Industrial IoT (IIoT) solutions requires a DevOps organization that can manage increased software and hardware complexity in terms of capability, capacity and footprint. DevOps is derived from Development and Operations and is one of the buzz words for ICT companies.Often it is the amalgamation …
By AI Trends Staff
Driverless trains powered by AI are coming. Driverless train software produced by New York Air Brake was used in a demonstration last summer of a 30-car freight train traveling 48 miles at a research and testing facility owned by the Association of American Railroads, according to a …
Illustration: © IoT For All The book Design, Launch, and Scale IoT Services classifies the components of IoT services into technical modules. One of the most important of these is Artificial Intelligence (AI). This article is intended to supplement the book by providing insight into AI and its applications for …
By AI Trends Staff
As the US steel industry looks for ways to lower costs in a global market facing slowing demand, a modern steel plant in Arkansas is using AI to help it become more competitive.
The Big River Steel Mill, which began operating in January 2017, melts scrap metal and produces steel for more than 200 customers, including four automakers, according to a recent account in WSJPro.
The plant’s AI system has been designed by Noodle Analytics of San Francisco, which uses deep learning and neural networks to continually train algorithms on data captured by thousands of sensors.
“We’re using the best available technology and pressing that technology farther, we think, than anyone in the steel industry,” stated Big River Chief Executive David Stickler, a veteran of the steel, mining and recycling industries. “Any future steel facilities that are built will try to capitalize on what we’ve done and replicate it.”
An environment of falling steel prices and a decline in demand from manufacturers is creating an opportunity for newer plants with lower operating costs. The hope for the AI at Big River is that it will lower operating costs and help to sell unused power when demand for electricity is high.
One expert credited Big River for being at the cutting edge of steel mill technology. It is the world’s first steel plant designed to manage its operations with the aid of “artificial intelligence from the drawing board,” stated Ron Ashburn, executive director of the Association for Iron & Steel Technology.
Big Steel started the AI project in 2017, collecting and analyzing data and training algorithms used to predict maintenance requirements for new machinery. The system collects data on equipment conditions, assessing wear and tear in the hopes of reducing shutdown time and gaining operating hours.
Noodle.AI is also working with SSAB Americas, a global steel manufacturer, to pair the company’s Enterprise AI Platform with its sensor data with external data to help plan business operations, according to an account in Robotics Business Review. The plan is to improve machinery uptime, engage in predictive maintenance and seek ways to optimize the plant.
“We are excited to implement new digitalization technologies and to explore how the application of Enterprise AI can impact our performance and create a competitive advantage,” stated Tom Toner, Vice President of Operations for SSAB Americas. “Our goal is to learn how we can increase efficiency and decrease any bottlenecks in our operations with this advanced technology.”
Noodle.ai’s founder and CEO Steve Pratt stated, “SSAB Americas is a pioneering manufacturing company that is looking to embrace new technologies to improve the quality of their products, service to customers and competitiveness.”
The steel industry has seen disruption in the past two decades by steel plant capacity added in China, which now produces 50% of the world’s steel. As the Chinese began to export excess inventory at lower prices, it put pressure on western producers. As a result, steel manufacturers in the west are concentrating on improving efficiency by modernizing, according to an account written by Hiranmay Sarkar, a managing partner with Tata Consultancy Services, in SupplyChainBrain.
The steel making labor force has been reduced in favor of automation in the last 25 years, a period when world steel production grew by two and a half times, and the industry has reduced the workforce by more than 1.5 million members, Sarkar reported.
A digital twin is a digital replica of a physical asset, including its systems and devices. The twin can serve as the backbone for cyber-physical integration, enabling seamless transition of data between digital and physical worlds. To enable enterprise AI, Sarkar suggests, the digital twin needs to have these attributes:
- An ecosystem commerce platform, off-the-shelf software, for information exchange with internal and external business partners;
- Physical equipment connectivity and event capture, through IoT devices. This ensures real time data collection at various nodes of the supply chain, such as ore storage by miners, suppliers and vessel operators, production by coke oven, blast furnace and mill, product store and distribution by yards and freight transporters.
In this episode of the IoT For All Podcast, Chief Product Officer of BehrTech, Wolfgang Thieme, joins us to talk about BehrTech’s MYTHINGS connectivity platform and LPWAN’s greater role in the industrial IoT space.
To kick off the episode, Wolfgang introduces himself and provides an update on what BehrTech has been up to since we last spoke to their CEO, Albert Behr. Wolfgang shares the latest progress on the launch of the MYTHINGS platform and talks about the process of refining and executing a go-to-market strategy. He also discusses the process BehrTech uses to gather feedback and lessons learned as the team adjusted to best serve customers in the industrial space.
We talk about the role LPWAN plays in the IoT space and how it enables companies to roll out massive deployments with hundreds or thousands of devices at a relatively low cost. Wolfgang shares with us some of the key considerations when building a network meant to scale and how companies launching new IoT deployments must consider scalability upfront.
To close out the conversation, Wolfgang shares his advice for companies looking to get into the IoT space, saying that speed to market and flexibility are key. He also shares some of his excitement for the rest of 2020, speaking to some of the advances in connectivity, in particular, that he believes will propel IoT capabilities forward.
Interested in connecting with Wolfgang? Reach out to him on Linkedin!
About BehrTech: BehrTech offers a disruptive wireless connectivity software platform that is purpose-built for massive-scale Industrial Internet of Things (IIoT) networks. At the core of the platform is MIOTY, a new communication technology standardized by ETSI that provides reliable, robust, and scalable connectivity unlike any other technology on the market. With its approach to interoperability, BehrTech makes it easy for end-users to retrofit its MYTHINGS platform in any environment and enables partners, system integrators, and VARs to deliver fully-integrated IIoT solutions that enable data-driven decisions to be made.
Key Questions and Topics from this Episode:
(01:40) Introduction to Wolfgang Thieme
(02:35) Introduction to Behrtech
(03:36) Are there any new initiatives happening at Behrtech?
(05:52) What’s the difference between LoRA and Behrtech’s platform MYTHINGS?
(07:42) What are some ideal use cases for LPWAN technologies?
(09:07) In developing the MYTHINGS platform, how did you approach your go-to-market strategy?
(11:35) What has the market response been like since launching MYTHINGS?
(13:56) How important are partnerships in IoT development and deployment?
(15:45) What role does LPWAN play in launching massive and scalable networks of devices?
(20:33) What are some of the challenges in implementing existing LPWAN technologies in industrial IoT deployments?
(22:53) How important is quality of service in industrial IoT deployments?
(24:04) What advice do you have for companies interested in getting into IoT?
(28:03) What are you looking forward to the rest of the new year?
Source: IoT For All
IIoT deployments are moving fast up the maturity chain, from pilot projects to large scale implementations that are delivering real value. Below, we take a look at some examples and figures to map out where the IIoT market is currently, and what will shape the future of the industry.
‘Think big, start small, scale fast’ has been a tagline for digital startups for many years, but the story of the maturing Industrial Internet of Things (IIoT) market brings new meaning and perspective to the phrase. Initially tipped to be a runaway success, with a market worth $933.62 billion by 2025, the IIoT market has been off to a slower start than initially forecast.
Overcoming the Challenges
Complex integration procedures, with multiple new sensors and data streams generating false positives and requiring recalibration, technology standards competition, a fragmented market and lengthy cost/implementation periods have been key barriers in preventing IIoT adoption.
The journey from concept to full maturity has been plotted in many different ways over the years, including Gartner’s much-quoted ‘hype cycle’. Gartner’s Hype Cycle for Emerging Technologies in 2018 placed the IoT industry at the “peak of inflated expectations”, ready to crash down into the “trough of disillusionment” before beginning the plateau into maturity. The big question unanswered by Gartner was when the plateau would be reached.
The Beginning of Proof Of Value
Deloitte recently went on record to state that they believe IIoT is finally reaching maturity, and that their clients are now looking beyond proof-of-concept towards proof of value. Robert Schmid, chief IoT technologist at Deloitte provided an example of a plastic manufacturer client that planned to build a new production line to satisfy the demand for a specific product. By connecting a variety of processes with IIoT devices and overlaying analytics, Deloitte was able to help increase the manufacturer’s throughput by almost 10% and saved £20 million by not building a new manufacturing line.
Diverse Use Cases Emerge
Manufacturing turns out to be the tip of the iceberg in terms of active IIoT applications. Several other industries are gaining significant traction including oil and gas, mining, utilities and agriculture.
McKinsey reported that an anonymous top ten global energy company used IoT applications and devices as part of a broader program of process and technology upgrades. The program resulted in a 33% reduction in unit production costs over five years. According to the analyst firm, the enterprise saved more than $9 billion in capital costs. In addition, they deployed IoT analytic tools to assess drilling data, which resulted in increased yields from existing mature oil wells.
Utilities See the Value
Another early adopter, GE, has been developing renewable energy generation IIoT solutions. GE attaches sensors to wind turbine blades to finesse blade angles in order to maximize efficiency in changing winds. These sensors collect and feed the overall wind farm data into efficiency analysis tools. These tools help us to understand the economic loss from downtime for each turbine and how it could be used to drive maintenance schedules, enabling engineer time to be used more effectively.
An IIoT pilot to track water leaks launched recently in Kent, UK. South East Water partnered with Vodafone’s low power NB-IoT network to deploy digital water meters, sensors and acoustic loggers on underground mains water pipes in Kent. This will enable the system to ‘listen’ for escaping water within the network, determine when leaks have occurred and pinpoint a precise location. It’s worth noting that this pilot may not be entirely unprompted, as the UK utility watchdog Ofwat has demanded all water companies reduce water leakage by 15% by 2025.
Healthcare Data Analysis Delivers
Philips has also been actively piloting IoT in its healthcare devices for many years. The company has migrated from proof-of-concept towards proof of value as a result of analyzing IIoT data garnered from the firm’s ultrasound and CT scan machines. The data harvested by Philips showed that healthcare providers waste significant amounts of time recalibrating CT machines between the head and abdominal scans. This information was used to create scheduling software that ensures the number of recalibrations is minimized.
IIoT Maturity Beckons
In short, IIoT is maturing quickly, and while enterprise scale is clearly a factor in forging successful applications and value chains, these early successes should serve to anchor standards and blaze a trail for smaller enterprises and second-generation adopters alike. Another key factor will prove to be the network operators themselves, as they move from beta testing next-generation networks and into the active promotion of commercial packages based on them.
Think big, start small, scale fast. As technology continues to mature, IIoT is likely to prove very fast indeed.
Source: IoT For All
As the industrial IoT market continues to expand at rapid rates, companies across the world are reaping the benefits. Utilizing this growing network of tools and systems, businesses have been able to prevent costly downtime, decrease product development costs, enhance customer engagement and satisfaction and acquire and implement intelligent data for strategic planning purposes.
The potential benefits are seemingly endless, and the list of organizations that are embracing this industrial revolution is continuing to grow, so let’s highlight some of the main IIoT companies you need to know for a number of the most common IIoT use cases.
IIoT Use Cases
One of the leading use cases for IIoT is predictive maintenance. Imagine being able to predict and prevent machine failures before they occur. Think of all of the costs and downtime that could be avoided with strategic maintenance that’s implemented at key intervals to maintain uninterrupted production. Relying on advanced analytics to identify and eliminate potential issues, IIoT has been a game-changer in the equipment monitoring sector.
- Augury – For Augury, machine health is the mission driving their IIoT technology. Monitoring over 70,000 machines, Augury is helping companies identify and uncover blind spots in their maintenance practices and empower businesses with vital information so they can be proactive in diagnosing and repairing equipment before reaching a point of failure. Like other IIoT companies, Augury employs artificial intelligence to run complex algorithms that compare machine signals across a vast network to predict malfunctions and provide actionable alerts when issues are detected.
- Uptake – Uptake IIoT technology has been implemented in an expansive range of industries including mining, energy, agriculture, construction and beyond. Utilizing data extracted from machine sensors and maintenance reports, Uptake’s machine learning algorithms are able to analyze and interpret complex information to identify anomalies in standard equipment operation and foresee upcoming failures. This predictive data allows companies and operators to prevent costly malfunctions before they occur.
- C3 IoT – C3 Predictive Maintenance is helping foresee asset failures in equipment across a wide variety of industry sectors including aircraft systems, oil extraction sites, substation machinery and more. With their broad suite of IIoT tools, C3 is able to utilize failure prediction algorithms to assess potential threats in real-time. These advanced diagnostics and projections can then be visualized, allowing for the ability to track machine performance over time and improve strategic planning in the future.
Asset Tracking and Monitoring
Another area where IIoT is making waves revolves around asset tracking and monitoring. In today’s modern age, information is key and there’s an increasing demand for digital data that allow companies to track and monitor assets in real-time. Here are some of the industry leaders who are leveraging IIoT to provide powerful information to businesses of all sizes.
- Roambee – Using a robust system of automated smart sensors and cloud data analytics, Roambee offers companies the ability to track shipments and inventory on the fly, with access to real-time location and condition reports across a global network. This enhanced visibility provides businesses the chance to track and monitor their assets with ease and helps create a truly digital supply chain.
- Konux – Artificial intelligence and IIoT sensors integrate seamlessly in the KONUX system to make insights more readily available for companies. Primarily employed in the rail industry, KONUX IIoT systems pull critical information from a wide range of source points to help track and monitor assets for optimal utilization. When used to monitor railway switches, the KONUX system is able to track and analyze asset usage 24/7, and this information can then be used to streamline maintenance protocols, implement quality checks and predict product conditions into the future.
- Shooftech – By essentially creating “smart” assets, Shooftech is capitalizing on IIoT to revolutionize the logistics sector. With their innovative wireless technology, Shooftech is able to transmit data across a vast cloud network to provide low-cost and scalable asset tracking and monitoring to businesses large and small.
Relying on a network of smart meters, substations, transmission lines and more, the Smart Grid is essentially the modern evolution of a standard electric grid. It can be used to remotely track and monitor everything from energy usage and traffic congestion to power surges and extreme weather events. It’s being utilized to restore electricity during outages, optimize energy usage, eliminate waste and better implement power generation systems for businesses and homeowners. Many companies have developed innovative tools to leverage the powerful offerings of the Smart Grid. Below are some key players in this space.
- Landis + Gyr – Landis+Gyr provides energy management solutions to utility companies across the globe. With an advanced metering infrastructure and industry-leading smart grid technology, Landis+Gyr has helped more than 3,500 businesses reduce energy costs, monitor and streamline their usage, integrate renewable technology and more.
- Aclara – Aclara is helping companies implement smarts meters, sensors and controls and harness the power of the Smart Grid. Partnering with more than 1,000 gas, water and electrical utilities world-wide, Aclara provides software and hardware solutions to create a comprehensive communications network that provides companies with the information needed to streamline their systems and better utilize vital resources.
- Itron – “Cut public lighting energy use by 30 percent over 10 years”. This is just one of Itron’s visionary goals for the city of Paris, and it’s using the power of the Smart Grid to turn this vision into a reality. Itron is helping cities and companies optimize their water and energy use, while also positioning them to better recover in the event of a natural disaster. Employed in more than 100 countries, Itron is working to create smart cities that can track, forecast and optimize their utilities in hopes of limiting waste and providing more efficient and effective power solutions.
Fleet management in the age of IoT revolves around automated systems and processes to streamline trip planning, minimize downed vehicles, plan and execute maintenance operations and much more. As the integration of smart devices in vehicles become more of an industry standard, and as automated driving becomes more prevalent in cars and trucks, it’s no wonder that we’ve seen a rapid expansion of the IoT fleet management market. Below, we’ve highlighted a few of the big names who’ve played a part in shaping the modern fleet management landscape.
- Fleetmatics – Owned by Verizon, Fleetmatic offers a host of smart tools to monitor and manage fleet vehicles. They provide advanced GPS tracking software that works with smart hardware to visualize vehicle locations and communicate delays in real-time. This information can then be used to optimize routes, limit fuel costs and analyze fleet performance over time.
- Omnitracs – Omnitracs provides a comprehensive platform of fleet tracking tools that help companies manage trips, maximize cargo capacity, customize forms and travel plans and more. These tools can be leveraged to enhance fleet safety, improve efficiency, monitor drivers and assets and ensure continued compliance with regulatory standards.
- Samsara – Samsara has a number of fleet management solutions ranging from cellular gateways and dashcams to wireless sensors and a robust mobile app. With an emphasis on safety, efficiency and quality, Samsara tools not only help track and monitor drivers and vehicles in real-time, but they can be used to reduce operating costs and expand fleet size by forecasting optimal vehicle usage.
Digitalization and Industry 4.0 still remain a frequent topic in 2020, but the technologies, methodologies and processes that it enables are still only being embraced by early adopters. Sewio’s newest infographic uses up-to-date data from early adopters to answer the following questions on a single sheet:
- WHO drives change in digital transformation?
- WHICH use cases are the most requested compared to those that make it to reality?
- WHAT use cases have shown proven ROI in the real world?
- WHY do organizations cover these use cases and what were their success metrics?
- HOW long does the process last and which steps lead to customer success?
The infographic begins with a breakdown of the job titles of the people who are driving digital transformation in their companies. In the second graph, the percentage of use cases required are easy to compare with the percentage of use cases that really make it to fruition. The third part of the infographic lists eight different projects and their main objectives and metrics that have been achieved in the top five use cases. Finally, the infographic shows a timeline of the overall process of digitalization with the average times of each phase.
“We are glad to be able to share these fresh insights with our ecosystem and the global community to contribute to the evangelization of digital transformation. This process is enabling companies to achieve greater efficiency, profitability, and, most importantly, safety,” notes Petr Passinger, CMO at Sewio Networks.
To view the infographic and download its different language versions, visit: http://www.sewio.net/infographic-industry-4-0-expectations-vs-reality/
Source: IoT For All
By AI Trends Staff
Many AI initiatives are loosely defined, lack proper technology and data infrastructure, and are often failing to meet expectations, according to a new report from Plutoshift on implementation of AI by manufacturing companies.
A supplier of an AI solution for performance monitoring, Plutoshift surveyed 250 manufacturing professionals with visibility into their company’s AI programs. Overall, the survey found that manufacturing companies are gaining experience while taking a measured approach to implementing AI.
Among the specific findings:
- 61% said their company has good intentions but needs to reevaluate the way it implements AI projects;
- 17% said their company was in full implementation stage of their AI project;
- 84% are not yet able to automatically and continuously act on their data intelligence, while some are gathering data;
- 72% said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of AI
- Only 57% said their company implemented AI projects with a clear goal, while almost 20% implemented AI initiatives due to industry or peer pressure to utilize the technology.
- 17% said their company implemented AI projects because their company felt pressure to utilize this technology from the industry
- 60% said their company struggled to come to a consensus on a focused, practical strategy for implementing AI
Among its conclusions, the report stated, “To truly utilize data, manufacturing companies need a data infrastructure and platform that is designed around performance monitoring for the physical world. That means gaining the ability to take data from any point in the workflow, analyze that data, and provide reliable predictions at any point. Right now, few companies report these full capabilities and would rethink their direction.”
Plutoshift CEO and Founder Prateek Joshi stated in a press release about the survey, “Companies are forging ahead with the adoption of AI at an enterprise level. Despite the progress, the reality that’s often underreported is that AI initiatives are loosely defined. Companies in the middle of this transformation usually lack the proper technology and data infrastructure. In the end, these implementations can fail to meet expectations. The insights in this report show us that companies would strongly benefit by taking a more measured and grounded approach toward implementing AI.”
Biggest Players Investing and Gaining Valuable Experience with AI
Another way to gauge how AI is or will penetrate manufacturing is to examine what the biggest players are doing. Siemens, GE, FANUC, and KUKA are all making significant investments in machine learning-powered approaches to improve manufacturing, described in a recent account in emerj. They are using AI to bring down labor costs, reduce product defects, shorten unplanned downtimes and increase production speed.
These giants are using the tools they are developing in their own manufacturing processes, making them the developer, test case, and first customers for many advances.
The German conglomerate, Siemens, has been using neural networks to monitor its steel plants and improve efficiencies for decades. The company claims to have invested around $10 billion in US software companies (via acquisitions) over the past decade. In March of 2016, Siemens launched Mindsphere, described as an “IoT operating system,” and a competitor to GE’s Predix product. Siemens describes Mindsphere as a smart cloud for industry, being able to monitor machine fleets throughout the world. In 2016, it integrated IBM Watson Analytics into its tools service.
Siemens describes an AI success story with its effort to improve gas turbine emissions. “After experts had done their best to optimize the turbine’s nitrous oxide emissions,” stated Dr. Norbert Gaus, Head of Research in Digitalization and Automation at Siemens Corporate Technology, “our AI system was able to reduce emissions by an additional ten to fifteen percent.”
Siemens envisions incorporating its AI expertise within Click2Make, its production-as-a-service technology. It was described in an account in Fast Company in 2017 as a “self-configuring factory.” Siemens envisions a market where companies submit designs and factories with the facilities and time and handle the order would start an automatic bidding process. The manufacturer would be able to respond with the factory configuring itself. That’s the idea.
GE’s Manufacturing Software Strategy a Work in Progress
GE, which has had fits and starts with its software strategy, has over 500 factories worldwide that it is transforming into smart facilities. GE launched its Brilliant Manufacturing Suite for customers in 2015. The first “Brilliant Factory” was built that year in Pune, India, with a $200 million investment. GE claims it improved equipment effectiveness by 18%.
Last year, GE sold off most of the assets of its Predix unit. An account in Medium described reasons for the retrenchment, including a decision to build a Predix cloud data center, and not recognize the competition from Amazon, Microsoft, and Google. Another criticism was that Predix was not known to be developer-friendly. Successful platforms need developer content, and developers need support from a community.
GE’s software strategy in manufacturing is a work in progress.
FANUC Has Invested in AI
FANUC, the Japanese company producing industrial robotics, has made substantial investments in AI. In 2015, Fanuc acquired a stake in the AI startup Preferred Networks, to integrate deep learning into its robots.
In early 2016, FANUC announced a collaboration with Cisco and Rockwell Automation to develop and deploy FIELD (FANUC Intelligent Edge Link and Drive). This was described as an industrial IoT platform for manufacturing. Just a few months later, with NVIDIA to use their AI chips for their “the factories of the future.”partnered with NVIDIA to use their AI chips for their “the factories of the future.”
FANUC is using deep reinforcement learning to help some of its industrial robots . They perform the same task over and over again, learning each time until they achieve sufficient accuracy. By partnering with NVIDIA, the goal is for multiple robots can learn together. The idea is that what could take one robot eight hours to learn, eight robots can learn in one hour. Fast learning means less downtime and the ability to handle more varied products at the same factory.train themselves. They perform the same task over and over again, learning each time until they achieve sufficient accuracy. By partnering with NVIDIA, the goal is for multiple robots can learn together. The idea is that what could take one robot eight hours to learn, eight robots can learn in one hour. Fast learning means less downtime and the ability to handle more varied products at the same factory.
KUKA Working on Human-Robot Collaboration
KUKA, the Chinese-owned, Germany-based manufacturer of industrial robots, is investing in human-robot collaboration. The company has developed a robot that can work beside a human safely, owing to its intelligent controls and high-performance sensors. KUKA uses them; BMW is also a customer.
Robots that can work safely with humans will be able to be deployed in factories for new tasks, improving efficiency and flexibility.
Source: AI Trends
The Internet of Things (IoT) is transforming the way the construction industry does business, allowing companies to become faster, smarter, safer, and more efficient.
New Construction Technology
The industry has entered a new phase of digitalization through IoT and other enabling technologies. IoT devices are no longer just primary sensors, but they are evolving into advanced computers, which are capable of new and demanding applications in construction projects such as remote operation, supply replenishment, construction tools, equipment tracking, equipment servicing, repair, remote usage
monitoring, augmented reality (AR), building information modelling (BIM), predictive maintenance, progress monitoring, construction safety, and quality monitoring. Clearly, there’s a growing need for bandwidth. Given that the adoption of IoT in the industry is projected to keep on rising, the future success of deployments will rely on innovations in connectivity, like 5G.
Early adopters in the industry are already using IoT solutions, albeit in a fragmented approach. There’s no developed ecosystem for all-round integrated business decision support, mainly due to a lack of fast, reliable and robust connectivity options that can handle communication from distributed locations. Usually, construction sites are in remote areas, and their headquarters are located in cities. Constant communication is required between the various stakeholders within the site, and from the site office to home-office and as well as offices of other partners involved in the projects. Currently, it’s challenging to find a network that’s cost-efficient, feature-rich, and capable of multiservice.
One of the biggest innovations within 5G is support for IoT use in construction in all its forms: providing high-speed data access, addressing mission criticality, and making it possible to connect constrained devices. In construction, project delivery depends on efficient data collection, capture and analysis/evaluation – all of which require reliable connectivity. 5G offers the possibility of real-time data processing, so decisions can be made almost instantly and issues rectified quickly.
The most popular key differentiator of 5G is bandwidth, i.e., data transfer rates of up to 10Gbps. However, according to Ericsson, there are about three different ways to think about IoT in a 5G-enabled world.
- Broadband IoT: Enables high volume and high-speed data transfer.
- Critical IoT: For mission-critical applications that rely on large bandwidths.
- Massive IoT: To connect a large number of devices.
Massive connectivity targets low complexity narrow-bandwidth devices that infrequently send or receive small volumes of data, like concrete maturity monitoring sensors, GPS and RFID tags. The devices can be in challenging radio conditions, like enclosures, and therefore require coverage extension capabilities and usually battery power. Additionally, the number of “things” involved in an IoT network is large, so it’s much different from a computer network, as the number of nodes increases the complexity of the network.
Broadband connectivity enables large volumes of data transfer, extreme data rates and low latencies for devices with significantly larger bandwidths than massive IoT devices. Broadband IoT connectivity is also capable of enhancing signal coverage per base station and extending device battery life if requirements on data rate and latency are not stringent. Broadband IoT is vital for the majority of the mobile equipment use cases that require high data rates and low latency, such as construction equipment telematics, fleet management, sensor sharing, and basic safety.
Mission-critical connectivity enables super-low latency communication. It aims to deliver messages with strictly bounded low latencies, even in heavily loaded cellular networks. In IoT ecosystems, the sensors, actuators and other gadgets are dependent on the responsiveness of the system or network to work effectively. This validates that high latency means delayed responsiveness, and with that comes the inability of things to function to their full capacity. Some IoT systems are designed to respond in case of emergencies, and delayed responsiveness can result in loss of life or property, for example, in the case of autonomous vehicles.
Possible 5G Use Cases in Construction
Real-time automation: Real-time automation is one of the most popular segments of construction applications. It consists of autonomous applications like robotic masons, welders, and cranes that leverage data from sensors in real time to trigger specific actions. It’s often used in mission-critical applications, where latency, availability, reliability, and security are of key importance.
Given that construction sites are complex and constantly evolving environments, teams can rely on 5G to understand activities on worksites in real time and to perform remote or autonomous construction operations. Combined with high communication speeds, this will give those working in construction almost instantaneous access to data-intensive edge and cloud applications, enabling multiple users to interact with each other in real time, and remotely.
While reliability and trust are key considerations in all IoT applications, they’re of utmost importance in mission-critical applications such as the predictability of data delivery to robots.
Monitoring, tracking, and surveillance: Self-driving vehicles are gaining prominence at construction sites, combined with data collected and fused from a vast array of sensors, including concrete maturity, structural health, waste management, location, weather, GPS and IP cameras. With the advent of 5G, this information will become indispensable as companies and cities overlay other technologies, such as artificial intelligence and machine learning, onto real-time data outputs and revolutionize how to work safely and efficiently.
5G will be crucial in monitoring the health, location, status, and specifications of assets of all kinds, including the following:
- Site machinery to ensure operational ability, availability, remote or autonomous construction operations.
- Site components to ensure coordination with the project, enabling real-time reaction to changes and updates.
- Improved safety. With 5G, sensors can more effectively be deployed to improve safety by tracking individuals’ safety compliance through smart vests, helmets, and shoes.
Supply chain optimization: The construction job site has a lot of repetitive activities, and hunting for materials is a constant challenge. Autonomous vehicles, RFIDs, computer vision, BLE (Bluetooth low energy), or other digital tools can be used to help address such issues. If materials can arrive on demand, it will greatly improve productivity.
Real-time information on the order status of materials or various components manufactured offsite is important to ensure a project is running on time. This will benefit project managers, principal construction contractors, and tradespeople.
Multi-trade prefabrication, including utilization of cyber-physical assistance systems, advanced building information modeling (BIM) and design-to-fabrication technologies has a direct impact on improving quality and reducing time spent at the worksite. This requires real-time collaboration, and 5G’s broadband IoT is a possible solution.
Enhanced video services: In terms of video capture, 5G will also help organizations inexpensively deploy technology to quickly capture, organize and analyze massive volumes of video information. This reduces the need for some teams to even visit the construction site. Further, this kind of real-time, rich, visual information can provide reassurance to the owner as well as an on-demand transparent view of the project at any particular moment in time.
Drones are already being employed to take 4K video footage, and 5G will enable real-time video sharing and analytics. Construction status and reporting can now rely on the use of computer visualization to understand the work and automatically update progress on the project. Computers can take care of 80 percent of field engineers’ repetitive work. This would free the knowledge workers to resolve problems as opposed to physically verifying work status.
Another example is the ability to deploy subject matter experts directly to the workplace through augmented and mixed reality, regardless of physical location. With a BIM model and 5G capability, it’s possible to have it instantly available and enable rich video content to provide an even greater level of visualization on the job site or about the site.
Hazard and maintenance sensing: Visual data can help us identify hazards instantly and proactively intervene to reduce accidents and injuries. Videos rather than static images can help streamline inspections, punch lists, audits, safety audits, as-builts, and even compliance. 5G enables visual data. Images make us reactive. In a proactive scenario, data capture is automated, continues through various sources, and is analyzed in real time. AI and machine learning become your predictive analytical engine that reports potential areas of risk before issues arise.
Fostering collaboration: We’re seeing an increasing number of joint ventures as construction projects become complex. Sharing knowledge is now important, not just internally but with peers. Often, we are trying to solve the same problems with the same set of resources (design, vendors, and trade partners). 5G makes this process much easier.
Caveats Regarding 5G in Construction
Just like any other new construction technology, 5G has to be adopted strategically. There are several caveats that companies need to consider, such as the following:
Standardization: While 5G will help to increase collection, capture, and analysis of data, there are many organizations and projects today that don’t have a strategy around the standardization of project delivery. This can reduce the potential benefits of 5G but also impact the safety, quality, completion time and budget of a project.
Security: Given the massive number of connected devices enabled by 5G, there’s an increased need for rigor around updating and following security standards.
To realize the full potential of 5G, construction businesses need a tailored implementation strategy. As a general approach, the following steps may prove useful:
- Clearly define the problems you want to solve and identify value creation drivers.
- Make sure you are solving a problem that matters, i.e., one that is supported by a compelling ROI.
- Choose a credible partner to help you decide on the ecosystem, channel model, and business model to pursue.
- Build internal capabilities to deploy the solution and to secure technical enablers.
- Implement the solution and allow its capabilities to evolve. Continuously improve as you experiment and learn until the solution can be deployed to scale.
Although 5G is still in its early days of deployment, fast progress is being made in the development and testing of the technologies, and the standardization process is expected to be completed in 2020 with 3GPP release 16.
When it comes to IoT, 5G’s capabilities open up a seemingly infinite number of new use cases. Data collected at the edge can be understood and acted on in near real time. High bandwidth and low-latency times ensure more data than ever can be quickly and easily collected and analyzed, overlaying increased intelligence into every device at the edge. The integration of 5G and IoT can help AEC organizations to improve productivity, safety, and compliance.
Written by Farai Mazhandu
Source: IoT For All