Lightning Network Channel Dynamics Simulation

The Lightning Network represents a promising solution to Bitcoin's scalability challenges, enabling fast and low-cost transactions. In the Czech Republic, it's gaining practical adoption with merchants like Alza.cz, Rohlik.cz and payment processors like Qerko and GoPay.

The network consists of payment channels, direct connections between two participants. Each channel has a fixed capacity, representing the total funds locked in for transactions. This capacity is shared between the two participants, with each holding a portion of the total on his side of the channel. Payments update the balance distribution within the channel. If two participants don't have a direct channel, the network routes payments through connected channels, relying on intermediary nodes with enough capacity to complete the transfer.

What will be simulated:

A NetLogo agent-based simulation of Lightning Network payment channels that models:

Nodes (merchants, customers, routing nodes) as agents with defined balances and behaviors
Payment channels as links between nodes with specific capacities
Transaction routing through the network
Channel balance changes and payment success/failure dynamics

Goal of the simulation:

To analyze how different network configurations affect payment success rates and channel efficiency, specifically:

Identify optimal channel capacity distribution for maximizing successful payments
Determine relationship between node connectivity and network reliability
Find bottlenecks in payment routing that cause transaction failures

Who would use the simulation and how it helps them:

Primary users:

New merchants considering Lightning Network adoption:

Understand required number of payment channels
Plan initial channel capacity requirements
Estimate liquidity needs

Lightning Network node operators:

Optimize channel configurations
Understand routing effectiveness
Plan capital allocation

Method and simulation environment:

Method: Agent-based modeling
Environment: NetLogo

Variables incorporated:

Deterministic Variables:

Number of nodes (N):

Initial setup: 100-200 nodes
Based on small subset of real network for feasibility
Can be adjusted via interface slider

Initial channel capacity (C):

Range: 0.001 - 0.1 BTC per channel
Based on average values from 1ML.com statistics
Set at channel creation time

Node types:

Merchant: 20% of nodes
Customer: 70% of nodes
Routing-node: 10% of nodes
Proportions based on network analysis papers

Base fee per transaction:

Default: 1 satoshi base + 0.001% of amount
Based on common Lightning node configurations

Network topology structure:

Average connections per node: 3-8
Based on observed network patterns
Follows power-law distribution

Random Variables:

Transaction amounts:

Distribution: Log-normal
Mean: 50,000 satoshis
Standard deviation: 25,000 satoshis
Based on public Lightning Network statistics

Transaction timing:

Poisson distribution
Mean arrival rate (λ): 0.1 transactions per node per minute
Can be adjusted via interface slider

Channel balance fluctuations:

Random walk within channel capacity
Step size: Normal distribution (μ=0, σ=0.01 × capacity)
Updates every simulation tick

Node connection preferences:

Probability of connection decreases with distance
Weighted by node type and existing connections
Uses preferential attachment model

Payment success probability:

Base probability: 0.95
Modified by:
- Channel capacity utilization
- Path length
- Node type

Data sources for variable values:

Network structure and capacities:

Public Lightning Network explorer (1ML.com):

Current number of nodes (~17,000)
Average channel capacity (~0.03 BTC)
Node distribution and connectivity patterns

Mempool.space statistics:

Total network capacity
Channel count
Node count

Transaction patterns:

Public Lightning Network statistics from Bitcoin Visuals (bitcoinvisuals.com)
Academic research papers on Lightning Network analysis:
- "Lightning Network: a second path towards centralisation of the Bitcoin economy" (Lin et al., 2020)
- "A Quantitative Analysis of Security, Anonymity and Scalability for the Lightning Network" (Tikhomirov et al., 2020)

User:Filip Simulátor

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