> For the complete documentation index, see [llms.txt](https://aetherservice.gitbook.io/about/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aetherservice.gitbook.io/about/product-vision-and-distributed-infrastructure/resource-contribution-framework.md).

# Resource Contribution Framework

The bandwidth provisioning mechanism in Aether is based on the operation of a local network client that functions as an asynchronous resource scheduler and secure traffic proxy. After installation, the device launches a resident module that continuously monitors system and network-level metrics, including available throughput (Mbps), RTT, jitter, packet loss, bufferbloat, local bandwidth consumption, NIC load, and network environment parameters such as connection type, NAT state, and route quality.

Based on this continuously updated dataset, the module calculates the **Residual Bandwidth Window (RBW)**, which defines the actual portion of network capacity that remains unused by local processes. The RBW is recalculated at high frequency using sliding time windows and adaptive filtering techniques such as Kalman-based estimation, allowing the system to exclude short-term usage spikes and ensure that only genuinely idle bandwidth is allocated to the network layer. Transmission is strictly bounded by the RBW, and any increase in local network activity immediately reduces or suspends external throughput allocation.

Once the RBW is established, the client initializes an encrypted multiplexed transport tunnel using a combination of **TLS 1.3 and QUIC**, with forward secrecy and per-session key isolation. All outbound traffic is encapsulated into encrypted frames and routed through distributed ingress nodes operated by institutional participants. The client operates in a blind relay mode, meaning it has no visibility into the content of the transmitted data and functions purely as a transport layer forwarding mechanism without interpretation of payloads.

Each transmitted packet is assigned a structured set of metadata, including timestamp, packet size class, session identifier, region tag, and RBW index. These parameters are used internally by the system to validate transmission consistency and reconstruct contribution proofs. The aggregated data is then passed to an off-chain processing layer where the **Proof-of-Bandwidth Contribution (PoBC)** is computed as a cryptographically verifiable record of delivered bandwidth, channel stability, RTT variance, uptime consistency, and accuracy of RBW estimation relative to observed network conditions.

The resulting PoBC output is periodically committed to the **Solana** blockchain as an aggregated, anonymized record linked to a temporary node identity hash. No personally identifiable information, device fingerprints, or reconstructable network traces are included in the on-chain representation, ensuring that contribution verification remains decoupled from user identity.

The reward calculation mechanism is defined by the formula:

```
Reward = f(Delivered Bytes, RBW Accuracy, Stability Index, Demand Multiplier)
```

Where Delivered Bytes represents the confirmed volume of transmitted traffic, RBW Accuracy measures the deviation between predicted and actual available bandwidth, Stability Index reflects channel reliability (including RTT variance and retransmission rate), and Demand Multiplier adjusts rewards based on real-time regional demand conditions that are updated continuously.

The system incorporates multiple anti-manipulation mechanisms designed to detect and mitigate attempts to artificially influence contribution metrics, including synthetic load suppression, RBW prediction gaming, traffic emulation, and replay-based distortion. When anomalous behavior is detected, the affected node may experience reduced weighting in reward calculations or temporary exclusion from contribution scoring.

Overall, the mechanism ensures that bandwidth contribution is measured in a deterministic, verifiable, and privacy-preserving manner, where value is derived strictly from real network participation rather than simulated or falsified activity, while maintaining complete isolation from user-level data and application behavior.


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