# Exploiting Chemistry for Better Packet Flow Management 2: Formal Model

*Note: for those more interested in the application and implementation review and discussion, this section can be skipped.*

## Formal Model of Artificial Packet Chemistry

### Artificial Chemistry

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**Stochastic Molecular Collisions.**Every single molecule is worked with, where a sample of molecules from the reaction vessel \mathcal{P} is drawn and the algorithm checks to see if a particular rule r \in \mathcal{R} applies.**Differential Rate Equations:**This approach seeks to describe the dynamics of a chemical system using concentrations of molecular species. The rules under this algorithm take a species approach: r: a_{1}s_{1} + a_{2}s_{2} + \ldots a_{N}s_{N} \longrightarrow b_{1}s_{1} + b_{2}s_{2} + \ldots + b_{N}s_{N}Here, the s_{i}‘s are species, not individual molecules. The coefficients are stoichiometric factors of the reaction. They are simply indicator functions to denote whether species s_{i} is a reactant or product. That is a_{i} = 1 if and only if s_{i} is a reactant in the rule r, and b_{i} = 1 if and only if s_{i} is a product in the rule r. It is this form of \mathcal{A} that Meyer and Tschudin [11] utilize in their packet chemistry.The change of overall concentration (concentration denoted c_{s_{i}}) is given by a system of differential equations\frac{\text{d}c_{s_{i}}}{\text{d}t} = (b_{i}-a_{i})\prod_{j=1}^{N}c_{s_{j}}^{a_{j}}, \quad i=1,\ldots,Naccording to the Law of Mass Action discussed earlier. There may be multiple rules/reactions r \in \mathcal{R} that affect the concentration of species s_{i}, so\frac{\text{d}c_{s_{i}}}{\text{d}t} = \sum_{r\in \mathcal{R}}\left[(b_{i}^{r}-a_{i}^{r})\prod_{j=1}^{N}c_{s_{j}}^{a_{j}^{r}}\right], \quad i=1,\ldots,N-
**Others**: There are other options, such as metadynamics (where the number of species and thus differential equations may change over time), mixed approaches, or symbolic analysis of the differential equations. As this article would be far too cumbersome to discuss these, they are omitted, but may be found in [1].

### Artificial Packet Chemistry

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Figure 3 from Meyer and Tschudin [11] gives an explicit example to help solidify these abstract ideas. The network consists of 4 nodes, so V = \{n_{1}, n_{2}, n_{3}, n_{4}\}. Each node has a bidirectional link with its neighbors, so E = \{n_{1}n_{2}, n_{2}n_{1}, n_{2}n_{3}, n_{3}n_{2}, n_{2}n_{4}, n_{4}n_{2}, n_{3}n_{4}, n_{4}n_{3}\}. In this case, we only have one species of molecule (one queue) per node, so \mathcal{S} = \{X_{1}, X_{2}, X_{3}, X_{4}\}. The set of reactions is simply a first-order reaction per arc: \mathcal{R} = \{r_{a,b}: X_{a} \to X_{b}: ab \in E\}

From a review standpoint, I would have liked to see a less trivial example, such as one with multiple queues in a node, and rules that may keep packets in a node instead of just transmitting. These types of scenarios would be interesting to model this way, and demonstrate better the power of this approach.

## Continuation

The next post in the series will discuss the mathematical analyses of the artificial packet chemistry described here.

## References

- Dittrich, P., Ziegler, J., and Banzhaf, W. Artificial chemistries – a review. Artificial Life 7(2001), 225–275.

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