## 3 Materials and Methods
### Experiment  
In addition to the simulation, and in order to complement the results of the simulation, measurements will be made from a WSN located in the engineering building of the Universidad Panamericana, Campus Guadalajara. Figure 5 presents a Google Maps search of the area where the WSN is physically installed and a general distribution of the sensors inside the network. Blue nodes are sensor nodes, while the green node is the coordinator and yellow nodes represent the possible jammer nodes. The network resembles the characteristics of an IWSN since in the area there is traffic of people using various mobile devices, cars in constant motion, school laboratories with electrical and computer machines as well as kitchens and offices with ventilation systems and computer equipment. In the environment, there is a considerable traffic of people from Monday to Friday, since machines are being used in laboratories and personnel are working in their offices; traffic decreases considerably starting Saturday afternoon and all through Sunday as the campus closes its doors. Figure 6 shows the distribution of the WSN in a grid that covers 500 m\({}^{2}\) of the campus in which, as already mentioned, signals of various types coexist, since it consists of different elements such as an avenue in the northern part where many types of vehicles circulate, in addition to some laboratories with industrial equipment, people circulating, gardens, parking lots, classrooms, etc. The experiment ran for a full seven days, starting on a Monday and ending on a Sunday with the sensors running under the same parameters from Table 4 that were used for simulations. Figure 5: WSN location on campus within an area of 500 m\({}^{2}\). The network consists of 12 static and homogeneous nodes with sensors of various types sending information to the sink node, which is located in the center of the grid; four jammer nodes have also been placed, which will help to demonstrate the theme of this publication by seeing their impact on the cooperation and collaboration of the nodes to send the messages to the sink node in the best way. Six nodes are based on Zigbee wireless technologies and the other six are based on Lora. Texas Instruments CC2650 SimpleLink model, for sensor nodes, and CC2530 model, for gateway, have been used for the experiment. CC2650 SimpleLink supports Multi-Standard Wireless MCU: Bluetooth, Zigbee, 6 LowPAN and IPv6, and offers low-power consumption supporting low-power sensors such as ambient light, infrared temperature, ambient temperature, accelerometer, gyroscope, magnetometer, pressure, humidity, microphone, magnetic sensor, etc. On the other hand, CC2530 is good in system-on-chip solutions for Zigbee and 2.4 GHz IEEE 802.15.4, optimum for industrial control and monitoring, building automation and low-power WSNs. Some other network parameters for the experiments are summarized in Table 6.  
We want to test the experiment with a methodology in which, according to the PDR, RSSI and Energy performance metrics (which are simple, easy to detect and inexpensive), the network can manifest an anomalous behavior (symptoms) where it can tell that you are being attacked by a jammer or potential jammer. With the experiment, we are showing that our detection method does work in a Campus that has characteristics similar to an IWSN, with industrial protocols such as Zigbee and Lora, into a considerable area. We want to validate our algorithm and interference detection mechanism with the two technologies, not only with simulations but in a real environment. The following section will show the results obtained in the simulations and the experiment for each type of metric within the cooperative and collaborative network schemes. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Experiment area & 500 m\({}^{2}\) \\ Number of sensor nodes & 12 \\ Sink nodes & 1 \\ Topology & Grid \\ Node RAM & 128 KB \\ Normal voltage range & 1.8 V to 3.8 V \\ Bit architecture & 16 \\ \hline \hline \end{tabular}
\end{table}