IoT data analytics via blockchain

Nicholas Ord
3 min readJul 20, 2019

A press release yesterday announced a patent application from E.ON. Available here for download.

Simplified Block Diagram of the Data Analytics / Blockchain flow

The blockchain part serves three main functions:

1. immutable time-chain of transactions between users and customers for analytics results

2. aggregate of authenticated data sources

3. digital market place for data analytics between trusted parties

(details on the right of diagram)

GCP services like DataFlow and Pub/Sub encrypt both in transit and at rest. Customer managed encryption with the ATECC608B on the DPU allow pinning of certificates related to the IoT data fingerprint and location. Proof of concepts can be seen here for BigQuery and here for DataFlow at GCP blog.

The Data Processing Unit (DPU) fits into a standard circuit breaker or can be integrated into OEM products for fittings in walls or ceilings:

DPU units are double sided 6 layer. The blockchain “chip” is a crypto interface between real time synchronous data (framed in gold) and IP cloud processing area

Q. What was the motivation for making the prototype?

A. Data scientists summarised 7 main pain points with industrial data sources

1. IoT data is generally not time stamped at source

“Even basic time series models are not really possible”

2. IIoT clusters are not synced

“Time sync deltas between sensor readings at different locations, even within just 70m², means significant correlation errors”

3. IIoT is not contextual

“Data context from the field cannot be instantly understood or classified”

4. IIoT data can’t be sequenced for ML / AI

(consequence of the above 3 issues)

5. IoT data formats are not standardised

“Little compatibility between industrial hardware vendors, data types or clouds”

6. IoT systems have high latency

“Takes too long to determine anomalous patterns (hours instead of milliseconds)”

7. IoT data is not clean

“Data needs 3 days data prep for every 2 days analysis per week”

“Industrial hardware data sources and real time data processing at scale costs more than the net value it provides if full CAPEX and OPEX are accounted for”

The first customer to consider in any data driven product (hardware or software) is the data scientist.

“How are we supposed to turn raw data into gold for “customers” if the raw data is dirty?” a pain data scientists often express for IoT.

The DPU is a first attempt to produce high octane real time data for the scientists, machine learning systems or AI.

DPU details

  • Synchronise data points in real time at the same time from 14 vectors
  • Each DPU generates 2.5 Mio “data science ready” points/day with millisecond granularity (NTP / PTP globally between DPUs and GCP)
DPUs can be produced in batch sizes as low as 20 economically

Small batches of DPU reference designs are produced at a secure automated facility

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