# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 3 Effective Information Traffic
### Effective Information Traffic
Due to the fact that nodes in the road are moving, the changes of topology structure will lead to the EIT can not reflect the connectivity of nodes in real-time. Therefore, we define the self attenuation function to correct the topology changes caused by motion. In the following part, we will explain the mathematical formula and maintenance method of effective information traffic. All vehicles can calculate their effective information traffic, and add it to the hello message which is sent to neighbors periodically. Effective information traffic is defined on the basis of information flow (2). \[I(t)=\frac{P}{t}, \tag{2}\]  
where \(P\) is the amount of information contained in the packet for effective forwarding, and \(t\) is the time taken to send data \(P\). In our protocol, the packets forwarded in greedy mode become valid packets and enter into the calculation of effective information traffic, while packets forwarded in recovery mode are called invalid packets which are not included in EIT calculation. The information of forwarding mode is added to the hello message and periodically forwarded to neighbor nodes. The value of \(I(t)\) of the central node increases faster than that of the edge node, because of the preference of the central node in routing. Such a positive feedback mechanism will lead to the problem of excessive preference. The nodes which are originally located in the center have a higher increasing rate of the value of \(I(t)\). However, when the node moves from the center to the edge, the excessive preference makes the \(I(t)\) value too large to reflect the connectivity of the node correctly. Therefore, we define the attenuation function to reduce the influence of excessive preference. Each node maintains its own \(I(t)\), so the attenuation function is also performed by each node. After sending the hello packet, the sender executed the function periodically. When the vehicle is stationary, it can be considered that the information flow can correctly reflect the node connectivity. Because the vehicle is moving, the change in the location of the vehicle will cause the node connectivity to change. Therefore, we introduce the self decay function to adapt to changes in topology. Previous valid forwarding data does not correctly reflect the current node connectivity, so the EIT needs self-reduction to ensure timeliness. We define the half-life time \(t_{h}\). In other words, if the node does not make effective transmission in time \(t_{h}\), EIT will become half of the original. For example,the initial information traffic is \(\sigma_{0}\), and there is no effective data transmission from 0 to \(t_{h}\), then at \(t_{h}\) the EIT is \(1/2\sigma_{0}\). Attenuation function can be obtained as below:  
\[I(t)=\left\{ \begin{array}{c}\partial_{0}\left(\frac{1}{2}\right)^{\left(\frac {t}{t_{h}}\right)},t\in[0,T_{s})\\ 0,t\in[T_{s},+\infty)\end{array} \right. \tag{3}\]  
If a node does not effectively forward for a long time, we consider that the connectivity of the node is zero. Obviously, no matter how long the time has passed, the value of the function cannot be zero. Therefore, we introduce reset time \(T_{s}=2t_{h}\). If the node does not effectively forward within \(T_{s}\), the \(I(t)\) of the node will be reset. Morever, if node sends packets during the period of \(T_{s}\), the time to reset \(I(t)\) will be recalculated. For example, during \([t_{0}-t_{1}]\), node does not forward effectively, so \(I(t)\) decreases as (3). At the time of \(t_{1}\), the node begins to forward packet in greedy mode and the ending of transmission at \(t_{2}\). If node has no transmission requirement from \(t_{2}\), the reset time of \(I(t)\) is \(t_{2}+T_{s}\). The value of \(I(t_{2})\) is the sum of the original decay rate up to the value of \(t_{2}\) and the value of \(\sigma\) from \(t_{1}\) to \(t_{2}\), as (4). \[I(t)=\left\{ \begin{array}{c}\partial_{0}\left(\frac{1}{2}\right)^{\left( \frac{t}{t_{h}}\right)}+\frac{P}{t},t\in[0,T_{s})\\ \frac{P}{t},t\in[T_{s},+\infty)\end{array} \right.