## 4 Results
### Network Model Analysis  
In this section we present the main results obtained from the evaluation of the algorithm that was presented in Section 3.2. We compare the proposed algorithm with a traditional one, based on the minimum cost, where costs are assigned to links according to the minimum transmission power required to reach the next node. Both algorithms are sequentially applied to the graph, in two different scenarios. This evaluation has been performed using the NS-3 framework, but without the actual implementation of EA-AODV. We start by generating the same traffic flows for each scenario and the algorithms are periodically executed, establishing the routes in all nodes. It is worth noting that the route definition in this case does not use protocol signaling, but it is set using the facilities provided by the simulation environment. Before re-executing an algorithm, the information regarding the accumulated exposure is updated to appropriately capture the temporal evolution. We will study both the overall transmitted power, as well as how the exposure is accumulated. This way, the entire procedure will reflect the behavior of the protocol and reach the global optimum. This methodology allows the use of an ad-hoc implementation, which considers the temporal evolution due to different traffic flows, without using heavier tools, which would greatly impact the computation time. We account for the accumulated exposure with the variable \(\mathcal{K}\), which is used as a proxy of the physical exposure. To this end, the value of \(\mathcal{K}\) for a given node is defined as the power that such a node transmits within a given time interval. We assume that all flows are alike, so that if a node is part of a route (i.e., source, destination or relaying entity) the exposure induced by each node to its neighborhood grows linearly with the number of flows traversing it. Thus, the value of \(\mathcal{K}\) in each node increases by a constant value \(\epsilon\) for every flow traversing the node. The performance of the proposed algorithm is assessed by comparing it with that yielded by a minimum power algorithm, where the selected route is the one that requires less power to send a packet from the source to the destination. This minimum power algorithm is a shortest-path search, in which the cost of each edge represents the transmission power required to get to the next hop, without taking into account the overall flow traversing that particular edge. We will refer to these two approaches as _EMFaware_ and _MinPower_, respectively. This analysis aims to study how the proposed algorithm is able to yield a reduction of both the transmission power and the accumulated exposure. We have studied scenarios with 50 nodes, having a wireless technology with a coverage range of 15 m, nodes being randomly deployed over a square area. Then we varied the density of nodes in the scenario by varying the side of the area, while keeping the coverage range of the nodes constant. The analyzed topologies are defined in Table 1. In all scenarios, four nodes are chosen as traffic sinks, a varying number of nodes play the role of traffic sources, and the rest are simple traffic forwarders. As can be seen, the scenarios differ in the number of traffic sources and density of nodes, while the overall number of nodes is kept at 50. The last row shows the Probability Density Function (PDF) of the number of gateways, or possible destinations, which can be reached in the corresponding scenarios. In each topology we deploy four traffic sinks so as to maximize the covered area. The number of traffic sources, and thus the traffic flows, is varied, and routes are established without any preference that could favor any of them. From a network perspective, the destinations are considered as generic traffic gateways, which provide access to an external network. Note that this is reflected by the virtual destination, which was included in the network graph. It is worth explaining how the accumulated exposure, \(\mathcal{K}\), is measured.