# Self-Learning Power Control in Wireless Sensor Networks
## 7 Discussion  
Our nodes are able to automatically adapt their transmission power to the varying conditions of the environment. The conditions have been changed per scenario by means of the distance between the transmitter and receiver of each node's pair, the number of simultaneous transmitters and the traffic load. We have seen that increasing the value of the variables, the nodes raise their power. For each scenario, the QoS requirement of the system is the same: PRR higher than 95% using the minimum transmission power. The research question of this paper is about the transmission power that should be used to fulfill the QoS constraints, while minimizing the interference to the neighbours. Measurements and analysis per link at the network's installation phase may provide useful hints, but then it is legitimate to ask ourselves whether users, or someone on their behalf, want to make measurements before using their products within the IoT market. Initial measurements would only be relevant for the static conditions tested at deployment time. However, the environment is influenced by changes (e.g., moving objects, new objects, variable number of people, variable number of sensor nodes), thus the power should be adaptive and autonomous (no user intervention). For instance, if the transmitter is moved further away from its recipient, it causes an increment on the transmitted signals' attenuation and a drop on the received signal power. Hence, the transmission power needs to increase. Yet, if the number of transmitters in the network increases, a receiver will detect more interference. In both the cases, the receivers' _SINR_ worsens, affecting the QoS. The solution is either lowering the interferers power or boosting the transmitting signal. However, the limitation is that decreasing other nodes' power influences their own network performance, whereas increasing one transmitter's power produces more interference to the neighbours. There might be a trade-off that allows each node to satisfy their own performance constraints. A typical industrial solution is to set the transmission power to its maximum. This would be the easiest and apparently the most conservative approach, which would however consume energy excessively and waste spectrum, issues that become unsurmountable in high-density scenarios.  
The benefit of our approach is that the nodes learn the minimum power they can use, without having to rely on any previous knowledge about the environment. Additionally, our self-learning process can take QoS requirements into account. Even though, in this paper, we have addressed only PRR, our method could consider any other features during the self-learning process, by modifying the nodes reward function. We could also set different goals for different nodes or subnetworks; there is no need to set up the learning process uniformly. In our work, we adopt the Manhattan grid topology, which is symmetric in the node disposition. Each transmitter in the multi-agent system has the same target, namely the QoS requirements. Therefore, as expected, the nodes learn similar transmission power levels, which are within a range smaller than 4 dBm. As future work, it would also  
Figure 16: PRR (**a**); and Q-value (**b**) obtained through experiments in the lab.  
be straightforward to study our protocol in asymmetric topologies, with dissimilar QoS constraints per node, whereby the transmission power varies across nodes.