Core Algorithms

Core Algorithms

The Lumora network is powered by advanced algorithms that ensure efficient resource utilization, task execution, reward distribution, and overall network optimization. These algorithms form the backbone of the decentralized system, balancing scalability, fairness, and security.

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1. Bandwidth Allocation Algorithm

Purpose:

Efficiently allocate bandwidth contributions from providers without disrupting their primary internet usage.

Steps:

1. Input Variables:

   • B_i: Available bandwidth of provider i.

   • U_i: User-defined maximum bandwidth contribution limit for i.

   • N: Total number of nodes in the network.

2. Allocation Formula:

   A_i = min(B_i, U_i) / Σ(min(B_k, U_k)) for k ∈ N

   • A_i: Allocated bandwidth fraction for provider i.

3. Output:

   • Proportional allocation of tasks to nodes based on their available bandwidth and contribution limits.

Benefits:

• Ensures optimal utilization of available bandwidth.

• Maintains user-defined constraints for fair participation.

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2. Proximity-Based Task Assignment Algorithm

Purpose:

Minimize latency and optimize task execution by assigning tasks to the closest available nodes.

Steps:

1. Input Variables:

   • P_i: Proximity of node i to the task source.

   • L_i: Latency of node i.

   • C_i: Current load capacity of node i.

2. Weighted Score Calculation:

   Score_i = α * (1 / P_i) + β * (1 / L_i) + γ * (1 - C_i / C_max)

   • α, β, γ: Weighting factors for proximity, latency, and load capacity.

   • C_max: Maximum load capacity of the network.

3. Task Assignment:

   • The node with the highest Score_i is selected for task execution.

Benefits:

• Reduces task execution time by prioritizing nodes near the data source.

• Balances network load to avoid overloading specific nodes.

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3. Data Scraping Coordination Algorithm

Purpose:

Ensure efficient, ethical, and accurate data scraping from publicly available sources.

Steps:

1. Input Variables:

   • T: List of tasks.

   • N: Total nodes in the network.

   • R: Rate limit imposed by the data source.

2. Task Distribution:

   Tasks_per_Node = T / min(N, R)

3. Adaptive Scraping:

   • Adjust scraping speed dynamically based on changes in the data source structure or rate limits.

4. Error Handling:

   • Implement retries for failed tasks up to a predefined limit.

Benefits:

• Complies with rate limits and ethical data usage policies.

• Ensures reliable task completion across distributed nodes.

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4. Reward Distribution Algorithm

Purpose:

Fairly distribute rewards to nodes based on their contributions to the network.

Steps:

1. Input Variables:

   • C_i: Contribution of node i.

   • R_total: Total reward tokens for the cycle.

   • N: Total nodes in the network.

2. Reward Calculation:

   R_i = (C_i / ΣC_k) * R_total for k ∈ N

3. Reward Disbursement:

   • Smart contracts automate token transfers to participating nodes.

Benefits:

• Ensures fair compensation proportional to contributions.

• Fully automated via blockchain smart contracts for transparency.

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5. Dynamic Load Balancing Algorithm

Purpose:

Distribute tasks evenly across nodes to prevent overloading and optimize network performance.

Steps:

1. Input Variables:

   • L_i: Current load on node i.

   • C_i: Capacity of node i.

   • N: Total nodes in the network.

2. Load Balancing Condition:

   If L_i >= 0.8 * C_i, redistribute tasks to the next available node.

3. Redistribution Strategy:

   • Tasks are reallocated to nodes with the lowest load to balance the network.

Benefits:

• Enhances network reliability by preventing node overloading.

• Improves overall task execution efficiency.

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6. Fraud Prevention Algorithm

Purpose:

Detect and mitigate fraudulent activity in bandwidth contributions and task execution.

Steps:

1. Input Variables:

   • R_i: Reported bandwidth contribution of node i.

   • V_i: Validated bandwidth contribution of node i.

   • T: Threshold for acceptable variance.

2. Fraud Detection:

   If abs(R_i - V_i) > T, flag node `i` for review.

3. Penalties:

   • Reduce rewards or temporarily ban nodes with repeated violations.

Benefits:

• Maintains network integrity by deterring fraudulent activities.

• Ensures accurate and reliable contributions.

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7. AES-256 Encryption Algorithm for Data Security

Purpose:

Secure all data transfers within the network using industry-standard encryption.

Steps:

1. Input Variables:

   • k: Encryption key.

   • D: Data to be encrypted.

2. Encryption Formula:

   E_k(D) = AES-256(k, D)

3. Decryption Formula:

   D = AES-256_Decryption(k, E_k(D))

Benefits:

• Protects sensitive data from unauthorized access.

• Ensures compliance with global data privacy regulations.

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8. Reputation Scoring Algorithm

Purpose:

Assign and manage reputation scores for nodes based on task performance.

Steps:

1. Input Variables:

   • T_success: Successful tasks completed by the node.

   • T_total: Total tasks assigned to the node.

2. Reputation Score Calculation:

   Reputation_i = (T_success / T_total) * 100

3. Adjustment:

   • Penalize nodes with low scores or frequent failures.

Benefits:

• Encourages reliable participation.

• Ensures high-quality task execution across the network.

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These core algorithms collectively ensure that Lumora’s decentralized network operates efficiently, securely, and fairly, enabling seamless bandwidth sharing, task execution, and reward distribution.