The problem DAP Protocol solves

Digital advertising currently relies heavily on manual price setting and fixed rates, creating inefficiencies in ad slot valuation. Display owners struggle to maximize revenue as they cannot dynamically adjust prices based on real-time demand, while advertisers often overpay during low-demand periods or miss opportunities during peak times.
Our protocol solves this through an automated, market-responsive pricing system. The smart contract automatically calculates optimal pricing based on three key factors: current market demand, time of day, and slot positioning. This eliminates manual price adjustments and ensures display owners receive fair market value for their advertising space.
For display owners, the system maximizes revenue by automatically increasing prices during high-demand periods and adjusting them during slower times. For advertisers, it provides transparency and fairness through algorithmic pricing, allowing them to optimize their advertising spend by choosing slots based on clear market signals.
The implementation of blockchain technology ensures all transactions and price calculations are transparent and immutable, eliminating pricing disputes and reducing administrative overhead. This creates a more efficient marketplace where both display owners and advertisers can participate with confidence, knowing prices reflect true market conditions.

Challenges we ran into

During the development of our dynamic pricing protocol, we faced three significant technical challenges that required innovative solutions.
The first major hurdle involved precision issues in price calculations. Solidity's limitation with floating-point numbers initially caused rounding errors in our dynamic pricing algorithm. When calculating prices using multiple factors like demand, time, and position, sequential multiplications and divisions led to significant precision loss. We resolved this by implementing a scaled decimal arithmetic system with a SCALING_FACTOR of 1e18, which ensured accurate price calculations while maintaining computational efficiency.
Another critical challenge emerged in gas optimization. Our initial implementation of the demand-based pricing mechanism consumed excessive gas due to complex mathematical operations. We addressed this by redesigning the pricing formula to use a more efficient constant product approach, similar to automated market makers. This optimization significantly reduced transaction costs without compromising the accuracy of our pricing mechanism.
The third challenge involved contract integration testing. The interaction between our advertisement system and the operator registry proved complex due to intricate state dependencies. We overcame this by developing a comprehensive testing framework using Hardhat, which included mock contracts and automated test scenarios. This solution enabled us to validate complex state transitions and ensure reliable cross-contract interactions, ultimately strengthening the robustness of our platform.