This whitepaper presents the nRide Network Rewards Model, a geospatial staking system built on the H3 hierarchical hexagonal grid. The model creates a balanced ecosystem where network participants can stake tokens to receive a share of network rewards generated within specific geographic areas. The system is designed to incentivize both granular local coverage by drivers and broad regional coverage by investors, creating a sustainable and scalable ride-sharing network.

## Table of Contents

1. [Introduction](#introduction)

2. [The H3 Hierarchical Hexagonal Grid](#the-h3-hierarchical-hexagonal-grid)

3. [Staking Mechanics](#staking-mechanics)

4. [Reward Distribution Mathematics](#reward-distribution-mathematics)

5. [Stakeholder Strategies](#stakeholder-strategies)

6. [Real-World Scenarios](#real-world-scenarios)

7. [Economic Implications](#economic-implications)

8. [Technical Implementation](#technical-implementation)

9. [Conclusion](#conclusion)

## 1. Introduction

nRide is a decentralized ride-sharing platform that distributes network rewards to stakeholders based on their token stake and geographic coverage. Unlike traditional ride-sharing platforms where a central company captures most of the value, nRide distributes network rewards to token holders who have staked in the geographic areas where rides occur.

The core innovation of the nRide Network Rewards Model is the use of the H3 hierarchical hexagonal grid to create a geospatial staking system that:

– Allows precise geographic targeting of stake

– Creates natural incentives for optimal coverage

– Balances the interests of different stakeholder types

– Provides passive income opportunities for network participants

– Scales efficiently across cities, regions, and countries

## 2. The H3 Hierarchical Hexagonal Grid

### 2.1 What is H3?

H3 is a geospatial indexing system developed by Uber that divides the world into hexagonal cells at different resolutions. Each resolution level divides parent cells into approximately 7 smaller child cells, creating a hierarchical structure.

### 2.2 H3 Resolution Levels

The nRide model primarily uses two resolution levels:

– **Regional Level (Resolution 5)**: Covers approximately 252.9 km² per cell

– **District Level (Resolution 6)**: Covers approximately 36.13 km² per cell

Each Regional cell contains exactly 7 District cells. This hierarchical relationship is fundamental to the staking power calculations in our model.

### 2.3 Why Hexagons?

Hexagonal grids offer several advantages over traditional square grids:

– Uniform adjacency (each cell has exactly 6 neighbors)

– More circular shape (minimizes distortion from center to edge)

– Better representation of natural movement patterns

– Smoother transitions between resolution levels

## 3. Staking Mechanics

### 3.1 Basic Staking Principles

In the nRide system, token holders can stake their tokens in specific H3 cells at different resolution levels:

– **District Staking**: Staking at Resolution 6 (smaller, more precise areas)

– **Regional Staking**: Staking at Resolution 5 (larger areas covering multiple districts)

When network activity (rides) occurs within a cell, the generated network rewards are distributed proportionally among all stakeholders with effective stake in that cell.

### 3.2 Effective Staking Power

A key principle of the model is that staking at higher resolution (smaller cells) grants full staking power, while staking at lower resolution (larger cells) results in reduced effective staking power:

– **District Stake**: 100% effective (1:1 ratio)

– **Regional Stake**: 1/7 effective (1:7 ratio)

This creates a natural incentive to stake in smaller, more precise areas while still allowing for broader coverage through regional staking.

## 4. Reward Distribution Mathematics

### 4.1 Effective Stake Calculation

For any given H3 cell at the District level (Resolution 6), the effective stake is calculated as:

“`

Effective_Stake_District = Actual_Stake_District

Effective_Stake_Region = Actual_Stake_Region / 7

Total_Effective_Stake = Sum(Effective_Stake_District) + Sum(Effective_Stake_Region)

“`

### 4.2 Reward Share Formula

When a network reward is generated in a District cell, each stakeholder receives a proportion based on their effective stake:

“`

Stakeholder_Reward_Percentage = (Stakeholder_Effective_Stake / Total_Effective_Stake) * 100%

“`

### 4.3 Mathematical Example

Consider a District cell with the following stakes:

– District Stakeholder 1: 75 tokens

– District Stakeholder 2: 25 tokens

– Regional Stakeholder: 250 tokens (covering the parent Regional cell)

The effective stakes would be:

– District Stakeholder 1: 75 tokens (full power)

– District Stakeholder 2: 25 tokens (full power)

– Regional Stakeholder: 250/7 = 35.7 tokens (1/7 power)

Total Effective Stake: 75 + 25 + 35.7 = 135.7 tokens

Reward distribution for a 100 USDC network reward:

– District Stakeholder 1: (75/135.7) * 100 = 55.27 USDC (55.27%)

– District Stakeholder 2: (25/135.7) * 100 = 18.42 USDC (18.42%)

– Regional Stakeholder: (35.7/135.7) * 100 = 26.31 USDC (26.31%)

## 5. Stakeholder Strategies

### 5.1 Driver-Focused Staking

Drivers are incentivized to stake in the specific District cells where they operate:

– **Advantages**: Full staking power, higher reward percentage per token

– **Strategy**: Accumulate incentivized tokens from the DAO during network growth phase

– **Benefit**: Passive income in USDC stablecoin even when not actively driving

### 5.2 Investor-Focused Staking

Investors can efficiently stake across broader geographic areas:

– **Advantages**: Wider coverage, diversified exposure, less management

– **Strategy**: Stake at Regional level to cover multiple District cells

– **Benefit**: Participation in network growth across larger areas despite reduced power

### 5.3 Hybrid Approaches

Sophisticated stakeholders may employ mixed strategies:

– Concentrate stakes in high-activity Districts

– Supplement with Regional stakes for broader coverage

– Adjust stake distribution based on network activity patterns

## 6. Real-World Scenarios

### 6.1 Scenario 1: Single District Stakeholder

**Setup:**

– One District Stakeholder with 100 tokens in a single District cell

– No Regional Stakeholders

**Outcome:**

– The District Stakeholder receives 100% of network rewards generated in that cell

– Effective stake: 100 tokens

**Real-world example:**

A driver in central London stakes 100 tokens in the Soho district. Every ride that occurs in Soho generates network rewards that go entirely to this driver. If rides generate 50 USDC in network rewards daily, the driver earns 50 USDC per day from this stake.

### 6.2 Scenario 2: Multiple District Stakeholders

**Setup:**

– District Stakeholder 1: 75 tokens

– District Stakeholder 2: 25 tokens

– Both staked in the same District cell

**Outcome:**

– District Stakeholder 1: 75% of network rewards

– District Stakeholder 2: 25% of network rewards

**Real-world example:**

Two drivers stake in the Westminster district of London. The first driver has staked 75 tokens, while the second has staked 25 tokens. If rides in Westminster generate 100 USDC in network rewards daily, the first driver earns 75 USDC and the second earns 25 USDC daily.

### 6.3 Scenario 3: Mixed Staking Levels

**Setup:**

– District Stakeholder 1: 75 tokens

– District Stakeholder 2: 25 tokens

– Regional Stakeholder: 250 tokens (covering the parent Regional cell)

**Outcome:**

– District Stakeholder 1: 55.27% of network rewards

– District Stakeholder 2: 18.42% of network rewards

– Regional Stakeholder: 26.31% of network rewards

**Real-world example:**

Two drivers stake in the Camden district of London (75 and 25 tokens respectively), while an investor stakes 250 tokens at the Regional level covering all of North London. For every 100 USDC in network rewards generated in Camden, the first driver receives 55.27 USDC, the second driver receives 18.42 USDC, and the investor receives 26.31 USDC.

## 7. Economic Implications

### 7.1 Network Growth Incentives

During the early growth phase, the nRide DAO can incentivize network participation by:

– Distributing tokens to active drivers based on their contribution

– Encouraging drivers to stake these tokens in their operating areas

– Creating a virtuous cycle of network growth and reward distribution

### 7.2 Passive Income Generation

The staking model creates opportunities for passive income:

– Drivers can earn network rewards even when not actively driving

– All rewards are distributed in USDC stablecoin, providing reliable income

– Staking positions can be adjusted to optimize reward potential

### 7.3 Market Dynamics

The model naturally creates market-driven coverage:

– High-demand areas will attract more stake

– Underserved areas present opportunities for higher reward percentages

– Price discovery occurs naturally through staking decisions

## 8. Technical Implementation

### 8.1 Smart Contract Architecture

The staking system is implemented through a series of smart contracts that:

– Track token stakes by H3 cell and resolution level

– Calculate effective staking power in real-time

– Distribute network rewards according to the mathematical model

– Allow stakeholders to adjust their positions as needed

### 8.2 H3 Integration

The system leverages the H3 library to:

– Convert geographic coordinates to H3 cell IDs

– Determine parent-child relationships between cells

– Calculate cell areas and boundaries

– Visualize staking coverage and network activity

### 8.3 User Interface Considerations

The staking interface allows users to:

– View available cells on an interactive map

– See current stake distributions and reward potentials

– Place and adjust stakes with intuitive controls

– Monitor reward accrual in real-time

## 9. Conclusion

The nRide Network Rewards Model represents a significant innovation in decentralized ride-sharing economics. By leveraging the H3 hierarchical hexagonal grid and implementing a sophisticated staking power model, nRide creates a balanced ecosystem that:

– Incentivizes optimal geographic coverage

– Rewards both drivers and investors proportionally

– Creates passive income opportunities

– Scales efficiently across geographic regions

This model ensures that network value is distributed fairly to those who contribute to the network’s success, whether through active participation or capital investment, while maintaining the economic incentives necessary for sustainable growth.

—

## Appendix A: Mathematical Proofs

### A.1 Proof of Balanced Incentives

The 1/7 power reduction for Regional staking creates a balanced incentive structure where:

– District staking is exactly 7 times more powerful per token

– Regional staking covers exactly 7 times more area

– The effective stake per unit area is therefore equal

This can be proven as follows:

Let:

– S_d = Stake amount in a District cell

– S_r = Stake amount in a Regional cell

– A_d = Area of a District cell

– A_r = Area of a Regional cell

Given:

– A_r = 7 * A_d (Regional cell contains 7 District cells)

– Effective S_r = S_r / 7 (Regional stake has 1/7 power)

For equal effectiveness per unit area:

– S_d / A_d = (S_r / 7) / A_r

– S_d / A_d = (S_r / 7) / (7 * A_d)

– S_d / A_d = S_r / (49 * A_d)

– 49 * S_d = S_r

This means that to achieve the same effective stake per unit area, a Regional stakeholder would need to stake 49 times more tokens than a District stakeholder. However, since the Regional cell covers 7 times more area, the Regional stakeholder only needs 7 times more tokens to achieve the same total effective stake across the entire Regional cell.

## Appendix B: Glossary of Terms

– **H3 Cell**: A hexagonal geographic area defined by the H3 indexing system

– **Resolution**: The level of detail in the H3 grid, with higher numbers indicating smaller cells

– **District Cell**: An H3 cell at Resolution 6 (approximately 36 km²)

– **Regional Cell**: An H3 cell at Resolution 5 (approximately 253 km²)

– **Stake**: The amount of tokens committed to a specific H3 cell

– **Effective Stake**: The stake amount after applying the resolution-based power adjustment

– **Network Reward**: The portion of ride fees distributed to stakeholders

– **Staking Power**: The relative influence of a stake in determining reward distribution