# Framework for Congestion Control in Delay Tolerant Opportunistic Networks

DTNs mainly focus on increasing the probability to deliver to the destination and on minimising delays

* Using complex graph theory techniques
* Where load is unfairly distributed towards the better connected nodes
* May lead to network congestion

## CAFREP

CAFREP or Congestion Aware Forwarding and Replication

* Detects the congested nodes and parts of the network
* Moves the traffic away from hot-spots and spreads it around while preserving the directionality of the traffic and not overwhelming non-interested nodes with unwanted content
* Adaptively change message replication rates

When deciding on the best carrier and the optimal number of messages, CAFREP dynamically combines three heuristics

1. **Contact** analytics
2. Predictive **node congestion** (node storage and in-network delays)
3. Predictive **ego network congestion** 

![img](/lectures/acn/img/g.png)

Each layer you go up, the more information is exchanged between the nodes.

### Metrics

###### Node Retentiveness

- Aims to avoid or replicate proportionally less at the **nodes** that have lower buffer availability.

$$
Ret(X) = B_c(X) - \sum^N_{i=1} \space M^i_{size}(X)
$$

For a node $$X$$, it has buffer of size $$B_c(X)$$. When a message of size $$M^i_{size}$$ is sent to node $$X$$, it's buffer size is the total buffer minus the memory taken by the sum of all messages in the buffer.

###### Node Receptiveness

- Aims to avoid or decrease sending rates to the **nodes** that have higher in network delays

$$
Rec(X) = \sum^N_{i=1}(T_{now} - M^i_{received}(X))
$$

How long a node keeps a message before forwarding it on. If a high level of receptiveness is found on a node, it means the node isn't useful as messages aren't forwarded. Could mean the node has limited connections.

###### Node Congestion Rate

- Aims to avoid or decrease sending rates to **nodes** that congest at the higher rate

$$
CR(X) = \frac{100\cdot T_{FullBuffer}(X)/T_{TotalTime}(X)}{\frac{1}{N}\cdot \sum^N_{i=1}(T_iend(X) - T_istart(X))}
$$

Estimates the time between a node being full and full again. Measures the time the node is unusable.

#### Ego Network Congestion Metrics

###### Ego Network Retentiveness

* Aims to replicate less at the **parts of the network** with lower buffer availability.

$$
EN_{Ret}(X) = \frac{1}{N}\sum^N_{i=1}Ret(C_i(X))
$$

Gets the average of the retentiveness of node $$X$$ and it's neighbours $$c_i(X)$$

###### Ego Network Receptiveness

* Aims to replicate less at **parts of the network** with higher delays.

$$
EN_{Rec}(X) = \frac{1}{N}\sum^N_{i=1}Rec(c_i(X))
$$

###### Ego Network Congestion Rate

- Aims to send less to the **parts of the network** that have higher congestion rates.
- This is useful as if a node isn't congested, but all connected nodes are. It stops it from being used.

$$
EN_{CR}(X) = \frac{1}{N}\sum^N_{i=1}CR_i(X)
$$

#### Contents of CAFREP Node

![img](img/h.png)
$$
Replication\space rate = M \times \frac{TotalUtil(Y)}{TotalUtil(X) + TotalUtil(Y)}
$$
Total utility, changes constantly. The replication limit grows to take advantage of all available resources, and backs off when congestion increases.

Social utility prevents replication at a high rate on free nodes that are not on the path to the destination.