1. Hjalmar Delaude, Jamente Cooper, Sivakumar Pillai, Istvan Barabasi
How Blockchain and AI accelerate the application of IoT technology to keep drivers’ safe on the road
Hjalmar Delaude, Jamente Cooper, Sivakumar Pillai, Istvan Barabasi

2. AI, ML, IoT and Blockchain offer new insights to car and driver interactions
Advancements in artificial intelligence (AI) and machine learning (ML) help implement smart technology into cars and provide new ways of interactions with the drivers.
Internet of Things (IoT) technology implemented within cars enables collecting rich data about the road location, traffic, driver’s behavior and overall car and driver interactions.
Trusted communication with the driver and IoT device(s) within the car can be established using Blockchain technology.
Blockchain enables trusted delivery of detected anomalies and/or alerts to critical service providers.
This technology is readily available to students and faculty, making easy implementing prototypes and performing research in and for problems areas of their choice

3. Background
According to Centers for Disease Control and Prevention (CDC):
in 2015 over 10,000 people died in alcohol impaired driving accidents, which accounts to about 30% of all fatal traffic accidents within the US.
In the same year, about 1.1 million drivers were arrested for driving under the influence.
CDC’s Behavioral Risk Factor Surveillance System (BRFSS) provides data and reports regarding health-related risk behavior for 50 states and District of Columbia.
Today this data is collected mainly based on surveys.
Using IoT technology, BRFSS could collect real-time data about drivers and analyze risk factors, trends.
IoT data would enable BRFSS to real-time assess risk levels using AI and subsequently help implement solutions that can mitigate and lower them.
Important factor for such AI solutions is establishing trust between source of data and back-end cloud services and protecting drivers’ privacy.
Key requirement for AI solutions is how to establish personalized service with the drivers, but also anonymize the same data when providing large scale reports and statistics about the drivers.
Detecting critical conditions affecting drivers ability to safely operate a vehicle can help save lives.

4. Raspberry PIs as private Ethereum nodes
We have chosen Ethereum as our choice to implement private blockchain, smart contract and trusted transactions
Blockchain technology was introduced by Satoshi Nakamoto (2008) in his paper “Bitcoin: A peer-to-Peer Electronic Cash System” (
The problem he solved in his paper was the constitution of trust in a distributed network.
Ethereum is an open-source decentralized platform based on blockchain, implementing smart contract (scripting) functionality.
According to Wikipedia, Ethereum implements a modified version of Nakamoto consensus via transaction based state transitions.
We have downloaded Ethereum (geth) from
We followed best practices and technical guides from chainskills.com

5. Solution outline for driver condition detection
Cloud Analytics Service
Alert as sent as trusted transaction via blockchain
Heart Rate monitoring using Fitbit ChargeHR
Private Blockchain
Sleep condition sensing via Fitbit ChargeHR
Central Computer
Critical condition detection:
Driver falling asleep
Driver heart condition
Reckless driving
GPS location and accelerometer sensor data capture via BerryGPS-IMU
We have collected sensor data using Fitbit and BerryGPS-IMU devices
Sensor data was sent to a Raspberry Pi (RPI), where conditions are detected using programs and algorithms. The RPI can communicate with cloud services for analytics, such as IBM Driver Behavior
The RPI initiates trusted blockchain transaction to a central computer
Alert is received and processed (forwarded to emergency services)

6. Implementing AI and ML in context of IoT devices
When using AI and ML with small IoT devices, implementing solutions requiring intensive processing becomes an engineering problem
Analytics can be performed in the cloud and results sent to IoT device
Other approach is to implement lightweight engines on a device and perform analytic at small scale
A recent third option is to pre-train models, reduce them and apply download to small IoT devices
Recent IoT at edge technology available to us is the Intel Movidius Compute Stick
This stick enables implementing machine learning at the RPI
The model (such as for KNN classifier) must be trained on a larger computer compacted and downloaded to the stick

7. Any questions?