Edge Server Deployment Scheme of Blockchain in Internet of Vehicles

Table of Contents

• 1. Introduction
• 2. Background and Related Work
  • 2.1 Blockchain in IoVs
  • 2.2 Edge Computing Integration
• 3. Methodology
  • 3.1 System Model
  • 3.2 Problem Formulation
  • 3.3 Randomized Algorithm
• 4. Experimental Results
• 5. Technical Analysis
• 6. Code Implementation
• 7. Future Applications
• 8. References

1. Introduction

With the development of intelligent vehicles, security and reliability communication between vehicles has become a key problem in Internet of Vehicles (IoVs). Blockchain is considered a feasible solution due to its decentralization, unforgeability, and collective maintenance. However, the limited computing power of vehicle nodes poses challenges for blockchain implementation. This paper proposes employing edge computing with roadside units (RSUs) as edge servers to address these limitations.

2. Background and Related Work

2.1 Blockchain in IoVs

Blockchain technology provides secure data transmission through its decentralized architecture. The mining process requires substantial computational resources, which is challenging for resource-constrained vehicle nodes. According to Nakamoto's original Bitcoin paper, proof-of-work consensus demands significant computational power that mobile devices cannot provide.

2.2 Edge Computing Integration

Edge computing extends cloud capabilities to the network edge, providing real-time services with lower latency. RSUs serve as ideal edge servers due to their stable network topology, reliable communication channels, and superior computing/storage capabilities compared to vehicle nodes.

3. Methodology

3.1 System Model

The system consists of vehicle nodes, RSUs acting as edge servers, and a blockchain network. Edge servers perform mining operations and manage blockchain data, while vehicles offload computational tasks to these servers.

3.2 Problem Formulation

The coverage problem is formulated as maximizing the number of vehicle nodes covered by deployed edge servers. The objective function can be expressed as: $\max \sum_{i=1}^{n} x_i$ subject to $\sum_{j \in N(i)} y_j \geq x_i$ for all $i$, where $x_i$ indicates whether vehicle $i$ is covered and $y_j$ indicates whether edge server $j$ is deployed.

3.3 Randomized Algorithm

The proposed randomized algorithm calculates approximation solutions for edge server deployment to maximize vehicle coverage. The algorithm provides a theoretical guarantee on solution quality with polynomial time complexity.

4. Experimental Results

Simulations compared the proposed scheme with other deployment strategies. The randomized algorithm achieved approximately 15-20% better coverage compared to greedy approaches and 25-30% improvement over random deployment. Performance was evaluated under varying vehicle densities and mobility patterns, demonstrating consistent superiority in coverage metrics.

5. Technical Analysis

Industry Analyst Perspective: This research tackles a critical bottleneck in vehicular networks with a pragmatic fusion of blockchain and edge computing. The approach is technically sound but faces scalability challenges in dense urban environments. The randomized algorithm provides good theoretical bounds but may struggle with real-time deployment decisions. Compared to similar work like CycleGAN for image translation, this solution addresses a more constrained optimization problem with stricter latency requirements. The integration of RSUs as miners is innovative but depends heavily on infrastructure investment.

6. Code Implementation

Pseudocode for the randomized deployment algorithm:

function RandomizedDeployment(vehicles, potentialSites):
    deployedServers = []
    uncovered = vehicles.copy()
    
    while uncovered not empty:
        candidate = randomSelect(potentialSites)
        coverage = calculateCoverage(candidate, uncovered)
        if coverage > threshold:
            deployedServers.append(candidate)
            uncovered = removeCovered(uncovered, candidate)
        potentialSites.remove(candidate)
    
    return deployedServers

7. Future Applications

Future directions include integrating machine learning for predictive deployment, developing hybrid consensus mechanisms combining proof-of-work and proof-of-stake, and expanding to 5G/6G vehicular networks. Applications extend to smart city infrastructure, autonomous vehicle coordination, and distributed traffic management systems.

8. References

1. Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
2. Shi, W., et al. (2016). Edge Computing: Vision and Challenges.
3. Zhu, L., et al. (2020). Blockchain-based Secure Data Sharing in IoV.
4. IEEE Transactions on Vehicular Technology, Special Issue on Blockchain in IoV.
5. Isola, P., et al. (2017). Image-to-Image Translation with Conditional Adversarial Networks (CycleGAN).