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| − | =Lightning Network Payment Channel Simulation=
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| − | ==Introduction and Problem Definition==
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| − | This simulation models the Lightning Network payment channels system. The simulation focuses on analyzing payment channel dynamics, network routing efficiency, and transaction success rates in a distributed payment network. The goal is to understand how different network parameters and routing algorithms affect the overall performance and reliability of the payment channel network.
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| − | ==Model and Method==
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| − | The simulation uses an agent-based model approach where three types of agents (customers, merchants, and routers) interact through payment channels. The network is built using a graph structure where nodes represent agents and edges represent payment channels with specific capacities and balances.
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| − | ==Environment==
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| − | The simulation environment consists of a 2D space where agents are randomly distributed. Each payment channel (edge) maintains:
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| − | * Total capacity
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| − | * Current balance in both directions
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| − | * Base fee
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| − | * Fee rate
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| − | * Connection status
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| − | ==Agents==
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| − | ===Customers (70% of nodes)===
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| − | * Shape: "person"
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| − | * Color: Blue
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| − | * Behavior: Initiate transactions to merchants
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| − | * Properties:
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| − | ** Balance
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| − | ** Pending transactions list
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| − | ===Merchants (20% of nodes)===
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| − | * Shape: "house"
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| − | * Color: Red
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| − | * Behavior: Receive payments from customers
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| − | * Properties:
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| − | ** Balance
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| − | ** Pending transactions list
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| − | ===Routers (10% of nodes)===
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| − | * Shape: "circle"
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| − | * Color: Yellow
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| − | * Behavior: Facilitate payment routing
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| − | * Properties:
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| − | ** Balance
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| − | ** Pending transactions list
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| − | ==Variables==
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| − | ===Network Configuration===
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| − | * '''num-nodes''': Total number of nodes in the network (10-500)
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| − | * '''avg-connections''': Average number of connections per node (2-10)
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| − | * '''max-connections''': Maximum allowed connections per node (4-10)
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| − | * '''mean-channel-capacity''': Average capacity of payment channels (1000-100000)
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| − | * '''min-channel-capacity''': Minimum capacity of payment channels (100-10000)
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| − | ===Transaction Parameters===
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| − | * '''transaction-rate''': Rate of transaction generation (0-10)
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| − | * '''mean-tx-amount''': Average transaction amount (10-10000)
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| − | ===System Variables===
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| − | * '''auto-rebalance?''': Toggle automatic channel rebalancing
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| − | * '''pathfinding-algorithm''': Choice between "dijkstra" and "a-star"
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| − | ==Simulation Steps==
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| − | ===Setup Phase===
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| − | * Clear previous simulation state
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| − | * Initialize global statistics
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| − | * Create nodes (customers, merchants, routers)
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| − | * Establish payment channels
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| − | * Set initial channel capacities and balances
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| − | ===Go Phase (Repeated Each Tick)===
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| − | * Generate new transactions
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| − | * Customers randomly initiate payments to merchants
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| − | * Transaction amounts follow a lognormal distribution
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| − |
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| − | * Process pending transactions
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| − | * Select path using chosen routing algorithm
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| − | * Attempt payment along selected path
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| − | * Update channel balances if successful
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| − | * Channel rebalancing (if enabled)
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| − | * Identify unbalanced channels
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| − | * Adjust balances toward optimal distribution
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| − | * Update statistics and visualizations
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| − | ==Pathfinding Algorithms==
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| − | ===Dijkstra's Algorithm===
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| − | * Traditional shortest path algorithm
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| − | * Considers hop count as distance metric
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| − | * Implemented with standard distance tracking and path reconstruction
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| − | ===A* Algorithm===
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| − | * Enhanced pathfinding with heuristic function
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| − | * Uses geographical distance as heuristic
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| − | * Maintains f-score, g-score, and h-score for optimization
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| − | ==Results Analysis==
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| − | The simulation tracks and visualizes several key metrics:
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| − | ===Network Statistics===
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| − | * Total network capacity
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| − | * Average channel balance
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| − | * Number of transactions (total, successful, failed)
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| − | * Success rate percentage
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| − | ===Visualizations===
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| − | * Transaction Statistics Plot
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| − | * Tracks successful vs failed transactions over time
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| − | * Channel Balance Distribution Plot
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| − | * Shows percentage of channels with extreme balances
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| − | * Monitors channels below 20% and above 80% capacity
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| − | * Success Rate Plot
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| − | * Displays transaction success rate over time
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| − | ==User Interface==
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| − | [[File:lightning_network_interface.png|thumb|800px|Lightning Network Simulation Interface]]
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| − | ==Implementation Notes==
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| − | * Written in NetLogo 6.4.0
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| − | * Uses both built-in NetLogo features and custom extensions
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| − | * Implements realistic payment channel mechanics
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| − | * Includes automatic rebalancing mechanisms
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| − | * Features interactive UI controls for parameter adjustment
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| − | ==Future Improvements==
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| − | * Implementation of more sophisticated routing algorithms
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| − | * Addition of dynamic fee adjustment mechanisms
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| − | * Integration of more realistic network growth patterns
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| − | * Enhanced visualization of payment flows
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| − | * Implementation of channel opening/closing dynamics
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| − | ==Conclusion==
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| − | This simulation successfully models the complex dynamics of a payment channel network, providing insights into:
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| − | * Network topology effects on payment success
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| − | * Impact of different routing strategies
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| − | * Channel capacity and balance management
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| − | * System scalability and performance characteristics
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| − | The model serves as a valuable tool for understanding the behavior and limitations of payment channel networks under various conditions and configurations.
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| − | ==Sources==
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| − | * Poon, J., & Dryja, T. (2016). The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments.
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| − | * Sivaraman, V., et al. (2020). Routing cryptocurrency with the spider network. USENIX NSDI.
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| − | * Martinazzi, S., & Flori, A. (2020). The evolving topology of the Lightning Network. PLOS One.
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| − | * Béres, F., et al. (2019). A cryptoeconomic traffic analysis of Bitcoin's Lightning Network.
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| − | * Pickhardt, R., & Nowostawski, M. (2019). Imbalance measure and proactive channel rebalancing algorithm for the Lightning Network.
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| − | * Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling with NetLogo. MIT Press.
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