TruckChain

TruckChain will enable an unprecedented level of traceability and reliability in logistics - worldwide.

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Inspiration

Bad logistics can cause life-threatening harm. In Africa, up to 80% of vaccacines go bad within the supply-chain. Many causes, including bad road conditions, extreme weather changes, and simply bad drivers can be anticipated. Global shipping companies like DHL and FedEx should be able to assess the quality of sub-contractors and accurately judge the risk of each trip.

What it does

TruckChain is a data-driven carrier quality and service risk rating agency. It is the digital Moody's of logistics. IoT data from the truck is being collected together with Fleet Board. Important events, like intrusions, off-road situations and route changes are being transfered to our own Blockchain. Thus, they become transparent and immutable. Using Blockchain Smart Contracts we determine risk ratings for carriers, trips and drivers and also enable payment authorization. This way DHL and FedEx can decide which service quality suffices for which kind of shipment. Making the decision based on data, rather than gut-feeling.

How we built it

We created the following subsystems and entities:

  • Sensor-data analysis software, based on python
  • Ethereum based blockchain implementation
  • Transaction event sent to blockchain, resulting from continuous data analysis
  • Frontend system using node.js and web3 blockchain client, displaying the trip-based carrier rating.ry.

Challenges we ran into

Explaining a blockchain project in 60 seconds Evaluate data and do work for us to create reputation system of drivers quality of delivery.

Accomplishments that we're proud of

  • Actually implementing the pipeline end-to-end
  • Using smart contracts to analyze the rating within the blockchain
  • Building our own route simulator that turns the FleetBoard data into a live-stream

What we learned

How to connect IoT, Blockchain, and UI to evaluate deliveries quality and give the possibility for a better choice of service in future. From physical sensors data to readily apparent rating system solution.

What's next for TruckChain

  • Catch up on our sleep
  • Implement more parameters from sensors to give a more precise ranking of delivery quality
  • Use machine-learning algorithms for anomaly detection and risk rating
  • Apply verification model to bring reliability to drivers

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Updates

posted an update —

Our Proof-of-Authority chain is up and running:

2017-12-02 15:45:55  Starting Parity/v1.8.3-beta-b49c44a19-20171114/x86_64-linux-gnu/rustc1.21.0
2017-12-02 15:45:55  Configured for TruckChain using AuthorityRound engine
2017-12-02 15:46:32     0/25 peers   8 KiB chain 7 KiB db 0 bytes queue 448 bytes sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:47:32     1/25 peers   8 KiB chain 7 KiB db 0 bytes queue 10 KiB sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:48:00  Imported #1 a102…d99c (0 txs, 0.00 Mgas, 1.00 ms, 0.56 KiB)
2017-12-02 15:48:02     1/25 peers   8 KiB chain 8 KiB db 0 bytes queue 10 KiB sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:48:32     2/25 peers   8 KiB chain 8 KiB db 0 bytes queue 10 KiB sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:48:45  Imported #2 bf02…75f8 (0 txs, 0.00 Mgas, 0.33 ms, 0.56 KiB)
2017-12-02 15:49:32     2/25 peers   8 KiB chain 8 KiB db 0 bytes queue 10 KiB sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:50:02     2/25 peers   8 KiB chain 8 KiB db 0 bytes queue 10 KiB sync  RPC:  0 conn,  0 req/s,   0 µs
2017-12-02 15:50:29  Imported #3 fb58…f509 (0 txs, 0.00 Mgas, 0.81 ms, 0.56 KiB)

Chain configuration and instructions are available on Github: https://github.com/TruckChain/refactored-train/tree/master/chain

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