# Reputation Consensus Mechanism for Blockchain Based on Information-Centric Networking
## 4 Design  
### Reputation Model  
Existing research frequently introduces behavior-based reputation models to enhance the efficiency of blockchain consensus mechanisms [7; 22; 24]. However, these models primarily focus on nodes' performance in blockchain transactions and community contributions, often relying on on-chain and off-chain activities or subjective evaluations by other nodes while neglecting specific performance metrics at the network layer. This approach fails to fully consider the heterogeneity of nodes within the network, particularly those with lower network service quality and performance, which may become bottlenecks in the blockchain consensus process.  
Figure 4 illustrates our proposed multidimensional reputation model for a blockchain architecture based on ICN. This model comprises three main components: reputation evaluation dimensions and parameter selection, reputation processing, and reputation output.  
* **Reputation Evaluation Dimensions and Parameters:** We assess the reputation of ICN consensus nodes by integrating blockchain stakes, network service contributions, and historical performance from the perspectives of objective trust and historical trust.
* **Reputation Processing:** This phase involves correcting reputation value increments in each update cycle using a logistic growth model and applying a reputation decay mechanism based on the Ebbinghaus forgetting curve to historical reputation. These measures help prevent Sybil attacks and whitewashing attacks.
* **Reputation Output:** The reputation output includes the initial reputation and cumulative reputation. The initial reputation assigned when a node joins the network depends on its stake in the blockchain. The cumulative reputation is the long-term accumulation of the node's performance in the network, comprising corrected objective trust and decayed historical trust.  
Figure 4: Hierarchy of the reputation evaluation model.  
This hierarchical reputation model systematically evaluates and updates nodes' reputation values in a distributed network, providing a dynamically adaptive and robust framework for enhancing the security and efficiency of ICN.  
Based on the above structure, the formula for a node's reputation value is expressed as:  
\[R(t)=\begin{cases}R_{0},&\text{if }t=0\\ \sigma\cdot R(t-T_{R})+f(t)\cdot\Delta R\Big{(}\Big{\lfloor}\frac{t}{T_{R}} \Big{\rfloor}\Big{)},&\text{if }t>0\end{cases} \tag{1}\]  
To simulate actual update cycles, we define \(n=\Big{\lfloor}\frac{t}{T_{R}}\Big{\rfloor}\). The discrete form becomes:  
\[R(n)=\sigma\cdot R(n-1)+f(n)\cdot\Delta R(n) \tag{2}\]  
where:  
* \(R(t)\): Reputation value at time \(t\).
* \(R_{0}\): Initial reputation value when the node joins, defined as \(R_{0}=R_{c}(0)\), determined by the node's initial on-chain stake.
* \(\sigma\): Exponential decay factor for historical reputation.
* \(T_{R}\): Reputation update cycle (a fixed interval).
* \(n=\Big{\lfloor}\frac{t}{T_{R}}\Big{\rfloor}\): Number of completed reputation update cycles by time \(t\).
* \(\Delta R(n)\): Reputation increment during interval \(n\), comprising the blockchain stake increment \(\Delta R_{c}(n)\) and the ICN network contribution increment \(\Delta R_{i}(n)\).
* \(f(n)\): Logistic growth function evaluated at the \(n\)-th interval.  
We now provide a detailed introduction to the hierarchical structure of the reputation model.