## 6 The Extensions of EAR2M  
As described in the previous chapters, EAR2M based on multiple flooding points improves the performance of real-time data dissemination by reducing the flooding time. However, multiple data transmissions from a source to multiple flooding points wastes a significant amount of energy. So, efficient data transmission from the source to the multiple flooding points could reduce the energy consumption. In order to achieve efficient data transmission, the source node transmits its data to one of the flooding points (the closest) and the node on the flooding point relays the data to the other flooding points for data flooding. In this paper, we have two approaches for a flooding point of an \(EA\) to relay toward the other flooding points as the extension of EAR2M. The first approach, EAR2M_R, as shown in Figure 4a. This approach minimizes the transmission delay from the selected flooding point to the other flooding points for increasing the real-time deadline success ratio. Figure 4b indicates the second approach, EAR2M_E. The second approach aims to minimize the energy consumption of data relay between flooding points. In both EAR2M_R and EAR2M_E, to provide real-time data routing using multiple flooding points, we recalculate the data forwarding time \(T_{forwarding}\) and the data delivery speed \(S_{speed}\) to each flooding point in Equations (3) and (4). In the next paragraph, the method for recalculation of the data forwarding time \(T_{forwarding}\) and the data delivery speed \(S_{speed}\) in EAR2M_R and EAR2M_E is described in detail. To provide real-time data routing using multiple flooding points, EAR2M_R also recalculates the data forwarding time \(T_{forwarding}\) and the data delivery speed \(S_{speed}\) for each flooding point in Equations (3) and (4) through the flooding time \(T_{flooding}\) when using multiple flooding points. Equally, EAR2M_R also uses the observed flooding time in Figure 2 as the flooding time \(T_{flooding\_n}\) when exploiting n flooding points. Thus, Equation (3) is equally calculated as Equation (5) of EAR2M as follows:  
\[T_{setdeadline}\geq T_{forwarding}+T_{flooding\_n}. \tag{7}\]  
However, the data delivery speed \(S_{speed\_i}\) to each flooding point \(i\) should be calculated differently than in Equation (6) of EAR2M. Because EAR2M delivers the data from a source to each of the multiple flooding points as shown in Figure 3, it individually calculates the data delivery speed \(S_{speed\_i}\) to each flooding point \(i\). However, in the pursuit of energy efficiency, EAR2M_R forces the data of a source to be delivered from the nearest flooding point (p1) to the other flooding points (p2, p3, and p4), as shown in Figure 4a. Thus, different from EAR2M, EAR2M_R synthetically calculates the data delivery speed to multiple flooding points. In order to calculate the desired delivery speed, EAR2M_R considers the distance from a source to the farthest flooding point via the closest flooding point. The reason to consider the distance is that the distance to the other flooding points is shorter than the previous one. Namely, the modified delivery speed for the farthest flooding point could be applied to the other flooding points using Equation (7). Thus, EAR2M_R calculates the data delivery speed \(S_{speed\_fp}\) to the farthest flooding point \(fp\) via the nearest flooding point \(p1\) as follows:  
\[S_{speed\_fp}=\frac{d(src,p1)+d(p1,fp)}{T_{setdeadline}-T_{floading\_n}}. \tag{8}\]  
In EAR2M_R, if the source node selects the number and position of flooding points, it calculates the data delivery speed \(S_{speed\_fp}\) for them using Equations (7) and (8). Then, it sends its data with the information of the \(S_{speed\_fp}\) and the flooding point locations to the closest flooding point (\(p1\) in Figure 4a) to it. Next, the sensor node nearest to the closest flooding point and belongs to the \(EA\) receives the data from the source. Then, the sensor  
Figure 4: The extended schemes: (**a**) EAR2M_R and (**b**) EAR2M_E.