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docs/lectures/acn/06_DTN_congestion_control.md

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+# 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.