Edge Computing Improves Data Accuracy

Data are Validated On-Device Before Syncing to the Cloud

Posting bad data to cloud-based or local area servers can create costly accuracy problems. Consider logistics applications that rely on accurate barcode reading and data entry.

The Cost of Bad Data: A Logistics Example

For asset tracking, it is often critical for logistics providers to scan and accurately read the right asset barcode along with a location barcode and a dispositioned quantity. Otherwise, the wrong asset could be delivered to the wrong location in the wrong quantity with potentially serious consequences.

Edge Computing codeREADr app to scan barcodesLet’s look at a specific, real-world example. A codeREADr customer, Lily Transportation, received a contract from one of the world’s largest grain companies to design and implement a tracking system for grain deliveries.

Why? Because another provider’s driver delivered the wrong grain to a silo. That one mistake caused the unfortunate death of many cows, inflicting a loss of $250,000. To stop that from happening again, Lily implemented a barcode tracking system to not only collect data but also to validate that the right grain is loaded into the right silo.

How Can Edge Computing Help?

Edge computing (sometimes referred “On-Device Validation”) works with or without Internet connectivity. An invalid scan or entry can be ignored by the receiving server, or it can even be blocked from being captured and posted to the cloud.

A typical toolset would include:

  1. A database stored on-device to validate every scan. If a database isn’t available, pattern validation can be used with regular expressions. Pattern validation will invalidate captured data if the result doesn’t meet the specified pattern.

  2. Smart scanning technology with rules for what to capture and what NOT to capture.

  3. Duplicate checking to stop scanning the same barcode twice.

  4. On-device database validation item-by-item for kitting, picking, packing, assembly, etc.

  5. Matching a barcode scan to a subsequent barcode scan. If matched, the result is valid. In nor, the result is invalid.

  6. Alter Scan technology to match scans to the data formats used.

  7. Artificial Reality (AR) to visually target the right barcode and select it once visually identified as the correct barcode to capture.

  8. When using the cameras of smartphones or tablets, accuracy is dramatically improved when using the SD PRO scan engine.

If bad data is posted to a server despite precautions being taken, you can also know the who, what, when, where, and how an asset was scanned. In that way, corrective actions can be taken.

The Future

Smartphones and tablets are powerful computing devices ideally suited for edge computing. They offer fast processing, data storage, and an entire ecosystem of native applications. This will help drive edge computing technology forward over the coming years.

Article by Rich Eicher Sr

About CodeREADr Inc.- The codeREADr SaaS platform is a cloud-based auto-ID and data capture (AIDC) solution which enables mobile apps to read, track & authenticate data-embedded barcodes & NFC objects anywhere, at any time. It is especially useful, fast and easy for small & medium sized venues and businesses to deploy since no CAPEX or training is required. Data embedded objects may include tickets, coupons, inventory and virtually any asset as well membership, corporate, student, patient and other IDs, whether presented in printed form or on a mobile phone.  Find out more at : https://www.codeREADr.com