Everything that can be automated will be automated, and it is up to us, as people, to learn how to adjust to this development With the advent of the networking of processes and the Industrial Internet of Things, IT has further cemented its place in the production facilities of modern enterprises and is now set to revolutionise the way in which maintenance is approached. The Chamber of Industry and Commerce has its hands full when it comes to making sure that vocational and training concepts both accompany and keep up with these developments. Many employees are anxious, believing that the ongoing digitalisation of the world of work will result in greater job insecurity; a general misconception which regrettably continues to abound. The fact that digitalisation is set to provide both new opportunities and challenges, and that not every workplace is in danger, is often conveniently overlooked in the surrounding hype. It’s as if history is due to repeat itself every time any major industrial revolution occurs. Production workers begin to fear for their jobs and fear of change in the workplace remains high. Nevertheless, production is subject to constant change and we must all learn to adapt. Today, it is IT, in the wake of the IIoT, which stands to replace traditional rosters and blackboards. What’s more, the advent of employees directly communicating with machines via speech in order to reset them is also fast approaching. Voice-control has already gained general acceptance, but an even greater degree of trust in technology is required. If no changes are made to the way you work, the sudden advent of digitalisation may make it appear as though things are out of place or even missing. That isn’t to suggest that there was a time in which IT didn’t exist in the realm of production; such a statement wouldn’t be true, as evidenced by the fact that, in times past, maintenance staff spent an inordinate amount of time making their rounds accompanied by a programmer’s notebook, which had different editors to program components and helped to facilitate communication between human and machine. Nevertheless, the fact remains that the networking of processes continues to generate considerable uncertainty. Customised production The introduction of online marketing has resulted in a large percentage of industrial production being tailored to fit the customer. Affiliate marketing allows you to find out much more about your customers, their behaviour as consumers, and the underlying motives that drive their decision making. Thus, in certain sectors, it no longer makes sense to produce products, place them in storage units, and then wait until they are sold off. Instead, it is becoming the norm to make predictions according to customer decisions or trends. By using information gathered from CRM systems, customer feedback and digital sales statistics, it is possible to determine the colours, form, and features that a customer would desire a future product to have. It is also possible to produce products in such a way that the targeted customer immediately purchases them, thus resolving the need to store the products away until such a time as they are sold. Customised production places high demands on maintenance. Common topics that are frequently brought up in addition to classical and continual improvement processes include: - Preventive Maintenance - Corrective Maintenance - Condition-related maintenance The umbrella term ‘predictive maintenance’ is often used to encompass the topics listed above. Predictive maintenance is a strategy that is based on real-time data taken from production. It permits you to quickly recognise and respond to problems or results which were not visible in the past, but which are now, thanks to new advances in technology (e.g. condition monitoring), immediately detectable. What does the process of networking involve? When surveying a newly digitalised production hall for the first time, the first difference that one notices is that a specific IP address has been assigned to all automated devices connected to the network, which allows for data to be received and sent. These automated devices can be completely different from each other. It does not matter. What does matter (where plant or machine controllers are involved) is the PLC (programmable logic controller). A digital network topology looks as such: sensors, drives, and actuators move things around; robots weld, solder, press and pack; and HMI/SCADA systems supervise the processes. Then there are presses, drills, machine tools, milling processes, and much more. Generally, there is a different editor used to program each type of automated device type. There are very few uniform standards when it comes to software editors and thus automation engineers cannot use the same software to program a wide range of devices. Visual programming languages in DIN EN 61131-3 are regulated, however, each editor has its own special features and they are seldom compatible. Editors continue to be further developed if only for the purpose of continuously updating them to support current operating systems. Software developers are eager to offer their customers ongoing updates, the reason for which lies in the fact that customers do not have any reason to pay for software editors that have reached the end of their development. They will only pay for new developments. For maintenance staff, this trend necessitates them to undergo constant further training in order to understand and implement the latest functions and features brought out by the software developer. In that regard, it is interesting to note that, even as the number of people present in the production hall continues to decline, the number of maintenance staff continues to grow. This stands in stark contrast to the hype about the human factor becoming an obsolete element when it comes to production; on the contrary, the human factor will continue to grow in importance, especially when it comes to fixing unplanned malfunctions and errors that may occur to the complex machines and systems during production. All visions involving the future state of digital production thus have one thing in common and that is the fact that people will continue to play a vital role: the ability to understand the complex connections between numerous machines, controllers and programs, will continue to be a sure-fire guarantee of success.
The key business challenges are: - Effective prevention of derailments - Reduction of oil spills on railroad transportation of crude oils - Alerts to first responders depending on material carried - Public safety answering points (PSAPs) and State Emergency Response Commissions (SERCs) need to know the schedule, load, and location - Flawless communication between PSAPs and first responders
Road congestion and strained transportation networks are persistent concerns associated with the rapid urbanization of developing and developed economies. A 2015 study1 reported that travel delays due to traffic congestion led to the waste of 3.1 billion gallons of fuel and a loss of nearly 7 billion extra hours to travelers during rush hour traffic, with a nationwide cost of around $160 billion, or $960 per commuter. Alleviating traffic congestion, in addition to improving safety, is leading public and private organizations to explore new mobility paradigms such as ride-share autonomous vehicles. GOAL The goal of the Connected Vehicle Urban Traffic Management (CVUTM) testbed is to create a smart road traffic ecosystem featuring connected vehicles using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, sensor fusion, industrial IoT platforms, cloud infrastructure, and edge analytics. This testbed will serve to preempt road congestion, automatically detect unusual eventson the road, and enable cooperative movement of traffic. In due course, both autonomous and non-autonomous vehicles will participate in this ecosystem with a goal of minimizing road congestion and improve overall improving motorist and pedestrian safety.
The assumption of trust which accompanied the early days of the internet is gone and replaced by privacy and security concerns accompanied with attitudes to risk which rise and fall across different sector and application scenarios. IoT covers a diverse range of services and products deployed in both managed and unmanaged use-cases with varying network topologies which bring different security challenges and new potential for attack.
The ability to learn is a precondition for autonomy. With this in mind, Siemens researchers are developing knowledge networks based on deep learning-related simulated neurons and connections. Such networks can be used to generalize information by identifying associations between extraordinarily complex realms, such as the publicly accessible Internet and a company’s internal information systems. Far-reaching and generic, this technology appears to hold the potential of mimicking what humans call intuition.
Amyx+ worked with a local government authority to develop an Internet of Things-enabled public safety strategy. In the current state, vigilance meant manually scanning through potentially hundreds of analog surveillance videos feeds. Manual, costly and ineffective, the local agency desired to transition from analog to digital CCTV, apply computer vision and other technologies to automatically detect potential crime in progress, expedite and streamline emergency calls and integrate with personal wearables to ensure the safety of their citizens.
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Unmanned weather stations play an essential role in the effort to analyze and predict the world's ever-changing weather patterns. The unmanned stations collect and store large amounts of weather data and then download the data at regular intervals to a back-end host for analysis and long-term storage. The computing device housed in the weather station must be robust enough to work continuously for long periods of time while exposed to a wide range of temperatures. It should also be able to collect readings from various sensors that use different data transmission protocols, and have the capability to store large amounts of data.
It is clear: even the most modern technology remains dependent on people Networks and programs for automated devices in production will continue to be further developed. So long as there is someone on board who is able to document who changed what, when, where and why, everything will function as it should. It pays to be able to be able to identify and extract the right data from a multitude of different data streams, so that one can then draw the right conclusions about the production. Thus it pays to have a well-trained maintenance staff on board, who are capable of performing tasks such as the ones included in the list below: - Extract data from networked sensors and actuators (which provide information about the current status of machines) - Extract data from devices programmed to monitor differences in temperature (which provides vital information about any suspicious conditions occurring during production) - Manage data taken from devices measuring pressure and volume streams by using setpoints and parameters - Identify, analyse and carry out preventive maintenance of data generated by changes or oscillations from brackets and motors (which helps to increase a machine’s service life) - Remotely configure and set data (such as parameters or setpoints) The reality that fewer and fewer employees will continue to work directly on the machines (a by-product of digitalisation) is due to the fact that it is now possible to control such machines from anywhere. All that is required is an IP address, and then the operator on the other side of the network is able to the control the machine while sitting in front of a monitor in any office. As stated before, it is important to be able to extract the right data from the controller (which in-turn allows you to draw the right conclusions about the production) without the need to be physically present. However, it is also important that one possesses the requisite expertise, creativity and abstract thought-processes. Without these human qualities, it is impossible to eliminate malfunctions and errors, or to optimise machinery. In the end, modern technology and all of its varying components will always be dependent upon maintenance staff and their ability to detect and identify changes. The numbers reflect this: 65% of maintenance actions are purely reactive, while only 30% focus on predictive maintenance. Using your data strategically In order to interpret and maintain automated production in the IIoT age, a data management strategy is needed. Extracting data from the production for the purpose of analysing it remains the undisputed task of the maintenance staff member. The only difference will be that the next generation of maintenance staff will be employed to engage with and perform new and additional tasks from a remote distance. The future topics that are set to impact on new business models in maintenance include: - Safety & security - Flexible and customised production - Efficiency (resources and energy) - Supply chain management (spare parts) and logistics - Production line efficiency - Economic efficiency - Maintenance costs Maintenance departments that are suddenly confronted with having to deal with these areas will quickly become overwhelmed by the sheer volume of work involved in performing them. To further complicate matters, one only has to factor in the multitude of approaches that exist across the globe. Due to a lack strategy and standardisation of data management, these approaches are often notably different. For example: - In countries where the labour costs are low, the inverse has occurred. Rather than having a networked production that performs data backups and analyses automatically, remote control has been replaced by employees, who are physically present at the controllers and who carry out data backups and analyses on-site - In some work environments, even if data backups of automated devices have been carried out, it still remains to be determined what exactly was changed since the last backup was performed. Software versions can have different setpoints and parameters, which bears the question: how do you manually verify these software codes and according to which standards? - Across the board, changes frequently continue to be documented by hand. This creates the perfect opportunity for errors to occur. Automated documentation goes a long way towards eliminating the likelihood of such errors occurring - Obsolete methods concerning cyber-related safety & security measures continue to persist. However, cybersecurity cannot be achieved using a sword and a shield. It can be achieved, however, with know-how and a digital cybersecurity defence strategy
Synapt’s team developed the end-to-end Connected Railroad solution that sends alerts along with preventive measures to first responders, departments in charge. It makes use of several sensors, including accelerometers, gyroscope, seal lid openers, and motion sensors.
*This is an IIC testbed currently in progress.* LEAD MEMBERS Infosys SUPPORTING COMPANIES Bosch Software Innovations, Real-Time Innovations (RTI) CLOUD SERVICE PROVIDER Microsoft MARKET SEGMENT Transportation (Connected Vehicles, Cooperative Traffic Movement, Shared Autonomous Mobility) FEATURES • The ability to integrate insights from connected vehicles with V2V/V2I technologies and cloud analytics to provide both a microscopic and macroscopic view of road congestion that will augment existing capabilities. • The ability to employ sensors and machine learning algorithms to automatically detect unusual events on the road and use this data to preempt road congestion, and provide such insights to motorists. • The ability to integrate IoT and connected vehicle technologies to enable cooperative movement of traffic and prevent road congestion for both non-autonomous and autonomous vehicles. TESTBED INTRODUCTION This testbed is focused on realizing an IIoT-enabled end-to-end smart mobility ecosystem that is augmented with cloud analytics, edge analytics, machine learning techniques, and V2V/V2I technologies. One of the many prerequisites for efficient movement of private and public autonomous traffic is the ability to evaluate, preempt and prevent road congestion, automatically identify unusual events on the road, and allow for cooperative point-to-point travel. We will adopt a phased approach towards realizing these goals. Directions and instructions regarding routes to take and recommendations on speed for each stretch of the road are provided by the cloud-enabled industrial internet system to the motorist. In due course, when fully autonomous vehicles are introduced into the system they will use this information and perform necessary speed changes and course corrections automatically without the need for driver intervention. MARKET CHALLENGES Widespread adoption of connected vehicle technology is essential for the CVUTM ecosystem to realize its ultimate vision. However, the adoption of CV technologies will follow market needs, and may start in smaller towns and cities and gradually spread to larger metros and states. Although a few auto OEMs are rolling out vehicles with V2V capabilities, federal mandates around CV technologies will benefit the larger adoption of the CVUTM ecosystem. TECHNICAL CHALLENGES A critical technology that enables the usage scenarios described in the testbed is the road-side unit. Adequate coverage of neighborhoods, streets, and highways with road-side units is essential for successful adoption of the CVUTM ecosystem. Meanwhile, other technologies such as LTE Direct and 5G that will allow for direct communications are also being considered to support inter-vehicular connectivity. Nevertheless, this testbed will serve as a stepping stone toward larger deployments of the CVUTM ecosystem at the level of a town, city or state, and serve as a proving ground for integrated industrial internet and existing or newer connected vehicle technologies.
Only Arkessa staff are authorised to provision your M2M/IoT connections and it’s important that during the provisioning process only the services required for the solution are applied to the subscriptions / SIMs, therefore reducing the risk of unintended or malicious use.
Biological systems that learn include everything from roundworms with approximately 300 nerve cells to adult elephants, whose brains contain 200 billion neurons. But regardless of whether you’re dealing with a fruit fly, a cockroach, a chimpanzee or a dolphin, the neurons of all of these creatures process and transmit information. Moreover, they do so for the same reasons: All organisms need to be able to discern and interpret their surroundings and then react appropriately in order to avoid danger and ensure their survival, as well as their ability to reproduce. They also need to be able to recall stimuli that signal risk or reward. In other words, learning is the key to survival in the natural environment. Through creating a computational neural network that mimics the neurons in a human brain, Siemens' engineers are getting closer to creating virtual intuition.
Amyx+ developed a holistic IoT strategy. Then worked with the government agency to evaluate best-in-class IP CCTV cameras, computer vision algorithms, wearables, and sound detection solutions for automated crime detection and prevention. Then identified ways to streamline the emergency call routing to minimize bureaucratic re-routing from central station to local dispatch to accelerate response time. The authority implemented a public relations initiative to help citizens become more aware of their surroundings, especially in the evening hours. Moreover, the authority educated the public about wearables devices for personal safety, including but not limited to bracelets, smart watches and other devices that could send emergency notification and messages to friends, family and the police.
Built on Bright Wolf’s Strandz platform and leveraging Amazon Web Services (AWS) cloud storage, Haemonetics won IoT Evolution App of the Year in 2015. Through their partnership with Bright Wolf, Haemonetics released HaemoCloud, connecting the blood supply chain from donation to transfusion. The new system works directly with their existing HaemoCommunicator suite – connecting all Haemonetics sensors and devices in order to format and transfer device information, as well as collect and manage operational data, and send it to the AWS cloud. HaemoCloud also integrates with hospital and other institutional information systems to store and share information with relevant hospital and IT staff, creating a seamless data experience for customers. Data collected in HaemoCloud will allow Haemonetics to build preventative maintenance algorithms and provide customers industry-leading device performance. The system’s flexible architecture also enables Haemonetics to take advantage of new market opportunities and grow with new offerings from Amazon Web Services (AWS) in the future.
Protocol Conversion and Front-end Data Processing The data collected by a typical unmanned weather station includes temperature, rainfall and snow accumulation, air pressure, humidity, and ultraviolet intensity. One of the tasks that the UC-7420 unit must handle is protocol conversion, since there is no guarantee that the devices collecting the weather data all use the same protocol. In addition, the UC-7420 can be used to do preliminary data processing before downloading data to the central computer. MOXA's UC-7420 is ideally suited for these tasks, since the user can easily embed a C program that is custom written for the devices used at the station. If future changes involve adding or removing devices, the programmer simply needs to modify the C code, recompile it, and then download the executable program over the network to the UC-7420 unit. Connecting to the Network and the Internet In addition to being programmable, the UC-7420 also offers users an array of connection options. To begin with, data entering the serial ports from attached sensors can be processed and then forwarded through one or both of the dual Ethernet ports to the LAN. One of the serial ports can also be connected to a V.90 or GPRS modem for PPP connections, and a PCMCIA port is available for installing a wireless LAN card for 802.11b/g networks. By including multiple connection options in the UC-7420's design, user's gain the flexibility needed to connect from virtually anywhere. Combinations of connection types can also be used to provide redundancy. For example, if unavoidable network problems cause an interruption in service, the user can connect by modem. CompactFlash Storage Space One of the dilemmas faced when creating a "small" computer is how to provide users with adequate storage space. For unmanned applications, it is best if the storage device does not contain moving parts. Although hard drives may seem to last forever for day-to-day use, we cannot make this assumption for continuous use at remote locations. The UC-7420 overcomes this problem by providing a CompactFlash slot. If needed, flash memory cards with storage capacity of up to several gigabytes can be used to store data until it is convenient to transfer the data to a central computer.
The challenges that digitalisation presents for maintenance In terms of cost-benefit, digitalisation is worth investing in. There are, however, challenges. For today’s smart maintenance, the challenges lie in remote maintenance and in data analysis, both of which necessitate that hardware and network infrastructure be subject to ongoing updates in order to efficiently manage the ever-increasing volume of data. Furthermore: - Managing data is an increasingly complex process - It requires a strategy for each step in production and for each automated device, depending on the way in which the maintenance staff view their tasks - Modern maintenance needs to be coordinated and supervised. It requires the ability to anticipate planned and unplanned results (predictive maintenance) while taking into account time constraints and pressure on the company to generate a profit Settling on a strategy for data management, as part of a new business model in maintenance, is a solid basis for ensuring and safeguarding investments and sustainability in production. The aim here is to sustainably strengthen and increase the significance of maintenance through proactive, value-adding measures. The fact that IT and automation continue to grow together, has a positive effect on production efficiency and is the basis for increased plant dependability. At the same time, the means to make the most of this are often lacking because maintenance departments do not always have the appropriate IT tools. But these tools alone are not enough. Maintenance also needs to be accompanied by new vocational and training opportunities. For example: - Data manager Tasks: quality control, change documentation, cybersecurity - Data analyser Tasks: Compare CRM to PPS, task management, internal and external communication/reports - Network specialist Tasks: networking processes, cybersecurity, data communication, production control The examples listed above represent just some of the potential vocational opportunities from the point of view of data management. However, any review of maintenance as a profession should be carried out separately and adapted to suit the industry in question. A company involved in the pharmaceutical industry is subject to different digital production strategies than a manufacturer from the automobile industry, or a power supply company. By adapting vocational and training concepts with regard to maintenance, classical maintenance, as it now stands, needs to be re-evaluated. It is simply a question of making this new business model as acceptable and attractive as possible, and also of involving the employees who are set to be affected by this development. Advancing the process of standardisation in relation to digitalisation Numerous studies have prognosticated that the introduction of IIoT and other related technologies are set to create added value in the billions by 2025. Those who remain pessimistic about this change and the tasks involved will have a hard time realising this business model. As it stands, there already exists several success stories which serve as paragons in this new age. The story of Lufthansa CARGO serves as an example of a company that began, in the mid-90s, to align their approach to maintenance with their successes elsewhere. Moreover, the tools have already been developed. The data management specialists at AUVESY have worked tirelessly for the last 10 years in order to make data management in production socially acceptable and to give maintenance staff the adequate tools needed in order to analyse data. The daily challenge will continue to be how to handle the terabytes of production data produced until such a time as a process of standardisation for IIoT is complete. In conclusion, all analysists are in agreement: the one who masters data, is the one who, in the near future, will be at the forefront when the latest developments from the digitalisation have reached market maturity. And it is down to maintenance to make this implementation sustainable. After all, who else would be capable of contextualising the complex and intricate connections that link production quality, efficiency and profitability?
The customer is a major US emergency service provider controlling the 9-1-1 service to US as well as global citizens for more than 30 years. The end customers include US wire-line, wireless, VoIP carriers, municipalities, and over 3000 public agencies.
Generally appilicable for security authentication for IoT platforms.
This specific case study analyzes the ramifications of neural networks on the renewable energy industry, specifically companies involved with wind turbines.
Haemonetics Corporation is the leading global provider of blood and plasma supplies and services. Our comprehensive portfolio of devices, information management, and consulting services offers blood management solutions for each facet of the blood supply chain — from plasma and blood collectors to hospitals.
|Solution Maturity||Mature (technology has been on the market for > 5 years)||Emerging (technology has been on the market for > 2 years)||Emerging (technology has been on the market for > 2 years)||Emerging (technology has been on the market for > 2 years)||Cutting Edge (technology has been on the market for < 2 years)||Cutting Edge (technology has been on the market for < 2 years)||Emerging (technology has been on the market for > 2 years)||Mature (technology has been on the market for > 5 years)|
Data from automated production, machines, robots, frequency changers, plcs, hmis, SCADA-systems,...
Machine maintenance data, anonymized procedure data, detailed maintenance logs.
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|Use Cases||Process Control & Optimization (PCO)||Smart City Operations||Cybersecurity ||Autonomous RobotsBuilding Energy Management System (BEMS)||Perimeter Security & Access Control ||Process Control & Optimization (PCO)||Process Control & Optimization (PCO)|
New Business Models in Maintenance
Regular notifications with shipment information to rail carriers, departments in charge, and county officials
Address Smart City Urban mobility needs, which include improved overall driving experience, reduced lost time in traffic, and improved well-being and health of citizens owing to decreased emissions.
Real Time Monitoring of machines
Increased Efficiency in the consistent flow of the process
Traffic from the mobile device is tunnelled through the land based part of the mobile network to the GPRS Gateway Support Node (GGSN).
The capabilities of the latest machine learning systems are illustrated by AlphaGo, with which Google achieved a milestone in the development of self-learning machines and artificial intelligence in March 2016, when AlphaGo succeeded in defeating one of the world’s best Go players: Lee Sedol. The amazing thing is that up until Google’s accomplishment, this Asian strategy game had been considered to be too complex for a computer.
Automated crime-in-progress detection through implementation of solutions
Institutional System Integration - Integrated across multiple information systems to share information with hospital and IT staff.
Multiple connection options for greater networking versatility. Maintenance personnel can monitor from a remote location
Advancing the process of standardisation in relation to digitalisation
Effective prevention of derailment
Provide insights into the functioning of a connected vehicle-IIoT integrated architecture at the scale of a neighborhood, municipality, and city.
Decreased Downtime due to early detection of possible machine failure
Predictive Maintenance with alert systems
When a device is configured to use an Arkessa APN the GGSN asks Arkessa for authentication and IP address assignment.
Researchers at Corporate Technology (CT) are studying how machine learning techniques could be used to enable wind turbines to automatically adjust to changing wind and weather conditions, thus boosting their electricity output. The basis for self-optimizing wind turbines is the ability to derive wind characteristics from the turbines’ own operating data. Up until now, this type of data has been used exclusively for remote monitoring and diagnosis; however, this same data can also be used to help improve the electricity output of wind turbines.”
Timely intelligence aided in the capture of criminals and de-escalated potential situations through proactive police response
Edge-based Intelligence - A smart system of devices providing critical operational data for service and support.
Programmability gives system integrators infinite possibilities. No fan, no hard drive design for longer MTBF. CompactFlash slot for adding gigabytes of storage space
Full-fledged dashboard with infrastructure, weather, and shipment information with GIS data
Serve as proving ground for a Smart Connected Ecosystem for autonomous or self-driving cars that allows for cooperative movement, improved safety, and testing and deploying ride-sharing mobility paradigms.
Data Aggregation for studying patterns and calculating the OEE.
Arkessa’s RADIUS server checks the SIM in the device is allowed to connect and before assigning a private IP address specific to that SIM and enabling data services to begin.
Deep learning techniques are a new trend in machine learning. These techniques utilize up to 100,000 or more simulated neurons and ten million simulated connections —numbers that break all previous records in the field of artificial intelligence. Thanks to their many levels of artificial neurons, whereby each addresses a different level of abstraction of the material to be learned, deep learning techniques are expected, for instance, to enable new applications for automated image recognition.
Increased public trust and confidence in the government authority to protect their citizens
Complete Business Transformation - Delivers an improved level of care, reduced costs, and traceability across the entire blood value chain.
75% reduction in overtime pay
Enable increased number of procedures per facility per day.
Flexible and customized production
40% decrease in crime through deterrence, automated monitoring and public awareness
Reduced downtime and unexpected maintenance calls.
50% improved emergency dispatch time
Increased customer satisfaction.
|Software||versiondog||Siemens S7-1200Siemens S7-300||Amazon Virtual Private Cloud (VPC)Amazon Elastic Compute Cloud (Amazon EC2)Amazon Route 53Amazon Simple Storage Service (Amazon S3)Bright Wolf Strandz Enterprise IoT Application PlatformAWS Cloud|
|Tech Partners||Industrial Internet Consortium (IIC)MicrosoftRTIBosch||Arkessa||Amazon Web Services|
IoT Snapshot: Hardware
|Processors & Boards|
|Sensors & Actuators|
|Devices & Equipment|
IoT Snapshot: Software
|Software as a Service|
|Platform as a Service|
|Infrastructure as a Service|
IoT Snapshot: Service
|Construction & Buildings|
|Equipment & Machinery|
|Logistics & Warehousing|