View source for Lightning Network Channel Dynamics Simulation (NetLogo)

=Lightning Network Payment Channel Simulation=

==Introduction and Problem Definition==
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.

==Model and Method==
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.

==Environment==
The simulation environment consists of a 2D space where agents are randomly distributed. Each payment channel (edge) maintains:
* Total capacity
* Current balance in both directions
* Base fee
* Fee rate
* Connection status

==Agents==

===Customers (70% of nodes)===
* Shape: "person"
* Color: Blue
* Behavior: Initiate transactions to merchants
* Properties:
** Balance
** Pending transactions list

===Merchants (20% of nodes)===
* Shape: "house"
* Color: Red
* Behavior: Receive payments from customers
* Properties:
** Balance
** Pending transactions list

===Routers (10% of nodes)===
* Shape: "circle"
* Color: Yellow
* Behavior: Facilitate payment routing
* Properties:
** Balance
** Pending transactions list

==Variables==

===Network Configuration===
* '''num-nodes''': Total number of nodes in the network (10-500)
* '''avg-connections''': Average number of connections per node (2-10)
* '''max-connections''': Maximum allowed connections per node (4-10)
* '''mean-channel-capacity''': Average capacity of payment channels (1000-100000)
* '''min-channel-capacity''': Minimum capacity of payment channels (100-10000)

===Transaction Parameters===
* '''transaction-rate''': Rate of transaction generation (0-10)
* '''mean-tx-amount''': Average transaction amount (10-10000)

===System Variables===
* '''auto-rebalance?''': Toggle automatic channel rebalancing
* '''pathfinding-algorithm''': Choice between "dijkstra" and "a-star"

==Simulation Steps==

===Setup Phase===
* Clear previous simulation state
* Initialize global statistics
* Create nodes (customers, merchants, routers)
* Establish payment channels
* Set initial channel capacities and balances

===Go Phase (Repeated Each Tick)===
* Generate new transactions
* Customers randomly initiate payments to merchants
* Transaction amounts follow a lognormal distribution

* Process pending transactions
* Select path using chosen routing algorithm
* Attempt payment along selected path
* Update channel balances if successful

* Channel rebalancing (if enabled)
* Identify unbalanced channels
* Adjust balances toward optimal distribution

* Update statistics and visualizations

==Pathfinding Algorithms==

===Dijkstra's Algorithm===
* Traditional shortest path algorithm
* Considers hop count as distance metric
* Implemented with standard distance tracking and path reconstruction

===A* Algorithm===
* Enhanced pathfinding with heuristic function
* Uses geographical distance as heuristic
* Maintains f-score, g-score, and h-score for optimization

==User Interface==

The simulation interface provides interactive controls and real-time visualization of the network:

<center>[[File:lightning_network_interface.png|800px|Lightning Network Simulation Interface]]</center>

==Results Analysis==

The simulation tracks and visualizes several key metrics:

===Network Statistics===
* Total network capacity
* Average channel balance
* Number of transactions (total, successful, failed)
* Success rate percentage

===Visualizations===
* Transaction Statistics Plot
* Tracks successful vs failed transactions over time

* Channel Balance Distribution Plot
* Shows percentage of channels with extreme balances
* Monitors channels below 20% and above 80% capacity

* Success Rate Plot
* Displays transaction success rate over time

=Results=

==Simulation Scenarios==

===1. A* with Auto-rebalance ON===
* Success rate stable at 66.88%
* Stable channel balance distribution
* Maintains consistent network functionality

[[File:Astaron.png|800px|A* with Auto-rebalance Results]]

===2. Dijkstra with Auto-rebalance ON===
* Success rate stable at 88.6%
* Well-managed channel balances
* Most stable and efficient configuration

[[File:pr2.jpg|800px|Dijkstra with Auto-rebalance Results]]

===3. A* without Auto-rebalance===
* Success rate at 13.95% after 75 000 transactions and declining
* Channels become heavily unbalanced
* Network performance deteriorates rapidly

[[File:pr3.jpg|800px|A* without Auto-rebalance Results]]

===4. Dijkstra without Auto-rebalance===
* Success rate at 15.27% after 75 000 transactions and declining
* Increasing channel imbalance over time
* Declining network performance

[[File:pr4.jpg|800px|Dijkstra without Auto-rebalance Results]]

==Key Findings==

===Auto-rebalancing Impact===
* Success rates stabilize at 66.88-88.6% with rebalancing
* Without rebalancing, rates drop to 13.95-15.27% after 75 000 transactions
* Rebalancing prevents continuous performance decline
* Channel distribution stability requires rebalancing

===Pathfinding Algorithm Effects===
* Dijkstra consistently outperforms A*
* Dijkstra + rebalancing achieves best results (88.6%)
* Both algorithms suffer significantly without rebalancing
* Algorithm efficiency matters more with rebalancing enabled

===Channel Balance Patterns===
* Rebalancing maintains healthy distribution
* Without rebalancing, channels tend toward extreme states
* Balanced channels correlate with successful transactions

==Parameter Impacts==

* Higher mean capacity increases success probability

* Lower mean tx amounts relative to capacity increase success rates

* Higher average connections provides:
** More routing options
** Better network resilience
** Increased path finding success
* Trade-offs:
** Pathfinding complexity
** Rebalancing overhead
** Capital requirements
** Network maintenance costs

==Simulation File==
The complete NetLogo simulation can be downloaded here: [[File:lightning_network_simulation.nlogo|Lightning Network Simulation]]

==Future Improvements==
* Implementation of more sophisticated routing algorithms
* Addition of dynamic fee adjustment mechanisms
* Integration of more realistic network growth patterns
* Enhanced visualization of payment flows
* Implementation of channel opening/closing dynamics

==Conclusion==
This simulation successfully models the complex dynamics of a payment channel network, providing insights into:
* Network topology effects on payment success
* Impact of different routing strategies
* Channel capacity and balance management
* System scalability and performance characteristics

The model serves as a valuable tool for understanding the behavior and limitations of payment channel networks under various conditions and configurations.

==Sources==
* Poon, J., & Dryja, T. (2016). The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments.
* Sivaraman, V., et al. (2020). Routing cryptocurrency with the spider network. USENIX NSDI.
* Martinazzi, S., & Flori, A. (2020). The evolving topology of the Lightning Network. PLOS One.
* Béres, F., et al. (2019). A cryptoeconomic traffic analysis of Bitcoin's Lightning Network.
* Pickhardt, R., & Nowostawski, M. (2019). Imbalance measure and proactive channel rebalancing algorithm for the Lightning Network.
* Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling with NetLogo. MIT Press.

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