# Framework for Congestion Control in Delay Tolerant Opportunistic Networks

DTNs mainly focus on increasing the probability of delivery 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 changes 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](img/g.png)

As you move up each layer, 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.

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

Node $X$ has a buffer of size $B_c(X)$. When a message of size $M^i_{size}$ is sent to node $X$, its available 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. This could mean the node has limited connections.

###### Node Congestion Rate

- Aims to avoid or decrease sending rates to **nodes** that become congested at a 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 becoming 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 retentiveness of node $X$ and its 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 if a node isn't congested but all connected nodes are. It stops the node 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.