V-Guard achieves high performance operating under dynamically changing memberships, targeting the problem of vehicles' arbitrary connectivity on the roads. When membership changes occur, traditional BFT algorithms (e.g., PBFT and HotStuff) must stop to update system configurations using additional membership management approaches, thereby suffering from severe performance degradation.
In contrast, V-Guard integrates the consensus of membership management into the consensus of data transactions. In a consensus process, The integration makes V-Guard achieve consensus seamlessly under changing members (e.g., with joining or leaving vehicles) and produces an immutable ledger recording traceable data transactions with their corresponding membership profiles.
Consensus target of tradition BFT algorithms: <data transactions>
Consensus target of V-Guard: <data transactions, membership profiles>
Vehicle-to-Everything (V2X) is communication between a vehicle and any entity that may affect, or may be affected by, the vehicle. There are two types of V2X communication technology depending on the underlying technology being used: (1) Dedicated Short-Range Communication (DSRC), which is WLAN-based, and (2) Cellular Vehicle-to-Everything (C-V2X), which is cellular-based.
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DSRC utilizes WLAN technology, enabling direct communication between vehicles (V2V) and between vehicles and traffic infrastructure (V2I). This interaction forms a vehicular ad-hoc network, establishing a connection when two V2X senders come within range of each other. WLAN technology is particularly suitable for V2X communication because of its high throughput and low latency, allowing for efficient and timely transmission of data between vehicles and infrastructure.
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C-V2X uses 3GPP standardised 4G LTE or 5G mobile cellular connectivity to exchange messages between vehicles, pedestrians, and wayside traffic control devices such as traffic signals.
V-Guard develops a Membership Management Unit (MMU) that keeps track of available vehicle connections and manages membership profiles. The MMU describes a membership profile that contains a set of vehicles as a booth. Below illustrates the management of booths (of size 4) when vehicles are communicating via DSRC.
In the below example, the yellow car is the proposer, and the MMU manages 10 members. The consensus target are the data (in blue font) and membership (in red font), where O is the orderingID (sequence #), B is the data batch, V is the booth, Q is the quorum, and Sigma is the threshold signature.
Ordering instances can take place in different booths. E.g., when the proposer conducts consensus for data entry 1, the ordering takes place in Booth 1. When Booth 1 is still available, the next ordering instance reuses it (e.g., data entry 2). However, when Booth 1 becomes unavailable (e.g., some vehicles go offline), the MMU will provide a new booth (i.e., Booth 2) for the next ordering instance.
Consensus instances are executed periodically as "shuttle buses" with a sole purpose of committing the entries appended on the total order log. A consensus instance (C) can also operate in a different booth where the new members will scrutinize the signatures of included entries that they have not validated previously.
GoLang should have been properly installed with GOPATH and GOROOT. The GoLang version should be at least go1.17.6. In addition, three external packages were used (check out go.mod).
// threshold signatures
go get go.dedis.ch/kyber
// logging
go get github.com/sirupsen/logrus
// some math packages
go get gonum.org/v1/gonum/
Below shows an example of running a V-Guard instance with a booth of size 4 and 6 initial available connections. The quorum size of a booths of size 4 is 3, so the threshold is set to 2, as the proposer is always included.
// Assume the downloaded folder is called "vguardbft"
// First, move the keyGen folder outside of vguardbft.
mv keyGen ../
// Then, go to "keyGen" and generate keys
cd ../keyGen
go build generator.go
// Keys are private and public keys for producing and
// validatoring threshold signatures where t is the threshold
// and n is the number of participants
./generator -t=2 -n=6
// A "keys" folder should be generated with 6 private keys and 1 public key
// Privates keys: pri_#id.dupe
// Public key: vguard_pub.dupe
// Now copy the "keys" folder into the "vguardbft" folder
cp -r keys ../vguardbft/
// Compile the code in "vguardbft" using the build script
cd ../vguardbft
./scripts/build.sh
// Next, create a log folder
mkdir logs
// Finally, we can start running a V-Guard instance by starting
// a proposer, which always has an ID of 0. The script takes
// two parameters: $1=ID; $2=role (proposer: 0; validator: 1)
./scripts/run.sh 0 0 // this starts a proposer
// run 5 validators
./scripts/run.sh 1 1 // this starts a validator whose ID=1
./scripts/run.sh 2 1 // this starts a validator whose ID=2
./scripts/run.sh 3 1
./scripts/run.sh 4 1
./scripts/run.sh 5 1
Check out parameters.go for further parameters tuning.
V-Guard is a flexible blockchain platform that allows users to define their own message types. This platform enables vehicles to reach a consensus on the decisions made by their autonomous driving software. The messages can include various data, such as GPS location, speed, direction, acceleration, bearing, and more. Selected projects using V-Guard as the foundation for their applications are listed below:
| Project Name | Authors | Features |
|---|---|---|
| VGuardDB | Michalis Bachras, Tim Cheng | VGuardDB creates a storage layer upon VGuard, making data available to all users in a structured way with read functionalities. Messaging are formatted to on-vehicle GPS sensors data. |
| VGuardSCM | Dixi Yao, Kai Shen, and Xiaochong Wei | Utilize VG within the realm of Supply Chain Management, specifically with a focus on monitoring individual trucks in granular detail. |
| VGuard Visualization | Nu Zhang, Chen Zhang, and Xianxin Zeng | A visualization engine implemented using a web-based approach with a JavaScript and React-based frontend, a Flask-based web server, and a set of V-Guard instances. |
V-Guard offers a solution to the trust and accountability issues associated with accident data by providing a guarantee of data accuracy and integrity. Our blockchain technology effectively addresses three key challenges that are often encountered with traditional centralized solutions:
- Transparency in data management. Data is more easily accessible to drivers, thereby preventing manufacturers from monopolizing it.
- Integrity in data management. V-Guard prevents data tampering. There is no room for manipulation or concealment of evidence by either drivers or manufacturers. V-Guard ensures that data cannot be fabricated or deleted, even if it goes against the interests of the parties involved in legal proceedings
- Accountability in data management. V-Guard promotes data accountability and ownership protections, creating a trusted environment within the industry, and ensuring compliance with data management regulations, such as GDPR and CPRA. |