## 2 Related Works
Another scenario of metric interdependence is observed: when the LQI in the network is increased, energy consumption and waste are decreased, as well as the number of collisions, interferences and transmission time. On the other hand, a better quality in the link would increase the PDR (%), since the packets would arrive intact at their destination, reducing the processing of data in memory and guaranteeing that all the routes in the routing table are valid. The result of the behavior of the different metrics can be analyzed to grant a classification regarding the Quality of Service (QoS) level. As an example, the QoS parameter metrics to evaluate IEEE 802.15.4 protocol performance in a star topology are presented by [PERSON], [PERSON] [43], taking into consideration metrics such as PDR, ECA, network lifetime and percentage of time in sleep mode; moreover, a general definition of QoS is put forth. QoS could be measured for the physical (hardware) layer in terms of PDR, RSSI, ECA and Bad Packet Ratio (BPR). The change in the levels of performance metrics depends on various factors. In an IWSN, the interference experienced can be caused by _unintentional_ radiant sources or by _intentional_ noise, causing in both cases the deterioration of communication on the network and a state of vulnerability in certain coverage areas [44]. Some examples of _broadband_ interferences include unintentional transmissions coming from motors, inverters, power circuits, electric switches and contacts, electrostatic devices, ignition systems, voltage regulators, lighting electromagnetic pulses, pulse generators, thermostats, welding machines, frequency converters, etc. On the other hand, _narrowband_ interferences consist in _intentional_ transmissions coming from cell phones, TVs, radios, line hums, signal generators, local oscillators, test equipment, microwaves, ultrasonic equipment, electronic ballasts, medical equipment, microprocessors, high-frequency generators, etc. Other works have focused on relating the _density_ of the nodes of a network with the damage that a jamming attack produces to the performance metrics. [PERSON] [45] uses node density and initial node power as evaluation criteria to find the _survivability_ (the ability to provide basic services after an interference attack) of a network after an interference attack. If the node density and the initial power increase, the survivability increases, manifesting itself in an increase in the PDR measurement. Secondly, [PERSON] [46] mentions that if a cluster-based WSN size is high and the density of the cluster is high, it leads to wasting more energy of cluster heads that are far from the base station. Finally, [PERSON] et al. [47] propose an energy-proficient clustering approach that performs better in terms of ECA, PDR and network lifetime. We define node _density_ as the number of network nodes present in a certain geographic area. WSNs have a large number of practical applications [48; 49], ranging from places with a very low density of nodes per area, such as environmental monitoring stations, agricultural care, animal tracking and some military applications, to WSNs with a high density such as applications within hospitals, buildings and smart factories. An extra effort was made to know for different contexts the types of practical applications that exist. Some WSN applications for various network densities are suggested in Figure 1.  
The upper left area represents networks with a low density, because there are few nodes in a very large area, and the lower right area represents networks with a high density, that is, with many nodes in a small area. The blue line represents the boundary between the zone of high and low node density. According to [PERSON] [50], a high density of nodes within a geographic approach can improve network connectivity, causing an increase in the reliability of deliveries, but entails an increase in latency due to the use of non-optimal forwarding routes.