I Don’t Care—Not One IoTa, But Many BACK from the EDGE
I’m amazed at what I am learning about business and transportation by observing my 14-month-old grandson. I’ve watched him learn to identify faces and all sorts of objects using his vision. He is learning to respond to sounds from his name, to Alexa playing music, to the dog barking, to having his parents read him a book before bed. He knows how to push buttons on his toys and get sounds to come out. I’ve seen him learn to move breast milk to his mouth—first from his mother, then from a bottle, and now from a sippy cup. He’s learned to take small pieces of hard food and begin to dissolve it initially, and now to chew and bite with a few teeth in his mouth. He’s learned to move the food from a tray to his mouth. He knows how to have food delivered to him on a spoon and how to begin using his fingers to move things to his mouth himself (in fact, move everything to his mouth). We were ecstatic when he rolled over, when he raised himself up, when he crawled slowly and then more quickly. We rejoiced when he learned to crawl up the steps (Mama and Daddy are teaching him how to crawl down backwards). He started walking with the help of a toy he could wheel around like a shopping cart (I hope he does not learn too quickly what that is) and with grownups holding his hands above his head. He took his first tentative steps just a few weeks ago, then quickly started walking on his own and is already close to running. What have I learned that has anything to do with transportation and business from that? I’ve learned the importance of positive motivation and correcting bad behavior. I’ve learned that we are wired from birth—it’s all about identifying everything, keeping information on everything, moving everything faster, better, cheaper from
Read that as Point A To Point B where the question mark/underline represents all the options we have for how we get something from one place to another. This works as well for an individual’s career, a business, or moving a product from manufacturing source to end user.
The trucking and transportation industries have a long history of tracking products. From the late 1980s to the present, fleets have worked to track the whereabouts of the tractor/truck. Omnitracs was one of the first companies to do this, utilizing geostationary satellites and their proprietary ranging software. From then, until now, we’ve gone through LEO (Low Earth Orbit) satellites, GPS being enabled for commercial use by President Clinton in the late 1990s, the emergence of analog, 2G, 3G, and now, 4G LTE communications and location services. 5G is just around the corner, as are DSRC and C-V2X communications. WiFi and Bluetooth helps us with tracking. RFID tags are used in several industries as well as a number of different UPC, bar code, and QR codes. Near Field Communications allows us to be identified and control things at short distances. We are obsessed with tracking things, making sure things happen as they should, reporting on things. We are so obsessed that we have multiple names for the concepts, frameworks, hardware, and software such as:
- M2M-Machine to Machine
- Predictive Diagnostics
- Big Data Analytics
- Edge Computing
- The Cloud
- World Wide Web
- Internet of Things
- Industrial Internet of Things
- Internet of Everything
- Smart Cities
- Smart Buildings
- Mesh Networks
- Distributed Control
- Industry 4.0
- Bluetooth mesh
- Facial Recognition
- Object Recognition
- V2X Communications
- RF Communications for Tire Pressure
- Autonomous vehicles
- Augmented and Virtual Reality
In all of this, we are attempting to address several key or core concepts:
- Make decisions
- End-to-end accountability
- Standardization versus personalization/customization
- Make money
- Improve experience and customer satisfaction
Let me posit a vision for the future of transportation of products/services based on the many items I’ve studied. If you have an inordinate amount of time on your hands, you can read some of the many stories in the links at the end.
Some bulk, raw materials are received at the dock of the manufacturing plant. The container holding it is a returnable one with an embedded tracker in it. Using cyber-secure blockchain wireless communications, the container automatically talks with the door of the dock, recording that it has entered the building. An invoice is automatically sent to the manufacturer and the funds automatically paid to the supplier, again using blockchain to record the monetary transaction.
This is a fully automated, industry 4.0 compliant factory. As early as possible, the raw material is moved to a machine where it is converted from its raw format to a more finished format. For instance, it could be a sheet of metal that is laser cut or, better yet, its titanium powder put into a 3D sintering machine that uses a laser to create the part from the bulk raw material. As part of the process, the part is permanently marked with a machine-readable marking. As the part begins to cool, a message is sent to the assembly plant that this part has been made, but not quality inspected yet. The part is automatically loaded onto an automated guided vehicle (AGV) in the plant and taken to the QA department. Above the door is a camera that reads the marking on the part and registers with a blockchain transaction that the part is now in the QA room. A variety of pieces of inspection equipment check the dimensions of the part, the shine of the surface, the density of the part and its weight. All of the information identifying the part, just like our individual DNA, is recorded in a blockchain packet. The part is loaded back onto the AGV and transported to the shipping dock. As it enters the dock area, a camera on the dock door registers arrival in the blockchain packet now in the cloud and available to the assembly plant.
As the truckload common carrier vehicle arrives at the dock, it automatically and securely connects to the WiFi network and accesses the blockchain packet for the part. The dock automatically opens the door as the trailer identification/tracking device in the backup camera embedded in the rear identification lamps comes within 2 feet of the dock door. The AGV is signaled to move the part into the trailer. As the part passes under the backup camera, the part number is read and registered in the blockchain packet as on board the trailer. The packet is updated and a message is sent via the cloud to initiate another financial transaction to pay for the part.
As the trailer passes various street lights on the side of the road, progress is registered for the movement of the part. The traffic light just before entering the highway registers the movement and sends an encrypted message to the assembly plant. As the trailer passes the weigh station, the weight measured at the QA lab is transmitted to the station with the unladen weight of the vehicle separately transmitted from the trailers own identification and tracking device, combined via wireless mesh with the weight data of the tractor. At the toll station to cross the river at the border, the financial transaction is done automatically. The part is identified as one covered by tariffs recently imposed by the president. The blockchain, financial transaction is automatically initiated to pay the government for the item.
While traveling down the interstate, the common carrier has identified another load traveling in the same direction for about 1,000 miles to the auto assembly plant. The two loads link up using V-2X or some other short range, high-speed, low-latency communications. The drivers take turns hauling the load from the front truck, basically doubling the time available to transport the loads. Instead of it taking 2 days of driving with rest breaks, they are able to deliver the load to the plant in less than 24 hours. The trucks are equipped more like RVs, complete with bathrooms to avoid stopping along the way.
I’m sure you begin to get the picture. But, there is more. This could be a load of expensive, controlled pharmaceuticals that have active GPS trackers. This could be the load of Bush’s baked beans that I had to recently check to see if they were covered by the food recall of the FDA. Imagine if the can could have automatically registered entry into my house through the camera enabled door bell and the WiFi and Bluetooth mesh networks in my home. When that recall went out, the manufacturer would automatically know exactly where each and every can of potentially contaminated beans is located and could have informed me via notifications on my cell phone.
As exciting as the last 40 years have been for me in medical, aerospace, military, manufacturing, and transportation industries, the next 10-20 are going to outpace them. I looked for a way to redefine what the Internet of Things (IoT) is to describe this. I’m leaning toward IoTa meaning:
Information on Transformed assets
Information includes all types of information including identity and value. Transformed represents what happens inside a factory, retail store, or home. Assets representing anything and everything of value, from that expensive titanium automotive part, to the controlled pharmaceuticals needed to keep a person healthy, to the food we eat, and to the big boy toys we play with, like my golf clubs that have GPS trackers in the grips to help me learn my distances better.
This involves electronics everywhere. The concept of EDGE computing becomes Every Device Goes Electronic. Rather than being “up in the cloud,” the big data needed to make instantaneous good business decisions keep us from going over the edge with this concept. It brings us BACK, which means Business Analytics Creating Knowledge.
So, rather than not caring one bit, not one IoTa, I care an awful lot about what it takes to efficiently, safely, cheaply move anything from
Not One IoTa—But Many BACK from the EDGE
Industrial Internet Consortium
Introduction to Edge Computing in IIoT
Blockchain in trasport Alliance (BiTA) https://bita.studio
Online checkout virtual cart becomes real with Amazon Go shopping stores.
The only sign of the technology that makes this possible floats above the store shelves — arrays of small cameras, hundreds of them throughout the store. Amazon won’t say much about how the system works, other than to say it involves sophisticated computer vision and machine learning software. Translation: Amazon’s technology can see and identify every item in the store, without attaching a special chip to every can of soup and bag of trail mix. . . .
Because there are no cashiers, an employee sits in the wine and beer section of the store, checking I.D.s before customers can take alcohol off the shelves.