**2.1 Experimental setup**

*Wireless Sensor Networks - Design, Deployment and Applications*

advanced packet scheduling [15].

analysis is emphasized.

LoRa and Wi-Fi standards.

the number of interferers increases.

via the channel state information (CSI) extracted from the Wi-Fi signals that are transmitted by the IoT devices during their operation as they are reflected in different manners from users of diverse categories (defined by their age, body shape, and daily routine). Interoperability between Wi-Fi-based IoT and other widespread wireless standards is another significant issue in literature. For example, in agricultural automation, these devices have to operate together with Bluetooth and radiofrequency identification (RFID) instruments. Their coexistence in the Industrial, Scientific, and Medical (ISM) band can be facilitated via rigorous analysis and adaptive frequency hopping [12]. Another aspect of interoperability is addressed for the case of operation between IoT and traditional wireless devices within the 2.4 GHz band due to the different characters of their dataflows. The IoT appliances with their acute battery limitations require low-latency and energy-efficient communications which may be complicated by the bandwidth-intensive transmissions of computers and smartphones. A solution to this issue is implementing an adaptive admission control for the IoT flows, which considers the wireless channel's characteristics [13]. Alternatively, this kind of interoperability is addressed via a Wi-Fi physical layer modification which utilizes multi-antenna access point (AP) [14] or traffic differentiation between IoT and traditional communications through

Scientific efforts are made to solve the present and future issues (mainly in terms to their dependability [16, 17]) with their practical deployment in 5G and beyond networks. Many of them have been focused on multiple wireless standards' coexistence in the license-free spectrum [18], interference mitigation, and coverage extension in the urban environment from the point of view of the overall access networks [6, 19, 20] or controlled retransmission of messages to increase the QoS by avoiding collisions [21]. Such approaches will need to be supplemented by a characterization of spectrum usage, which facilitates utilization analysis and implementation of dynamic access to the shared frequency resource via cognitive radio (CR)-enabled devices. In the ISM bands which are already heavily congested by traditional communications, the necessity of such software-defined monitoring is even more present. Furthermore, spectrum utilization is very different in indoor and outdoor scenarios, which requires that they should be analyzed separately [22]. Based on these observations, the importance of IoT's interoperability in indoor environments is established, and thus, the necessity for interference

The experiments presented in this chapter examine and evaluate the spectrum occupancy and interference of dense indoor scenario for LoRa and Wi-Fi. Multiple interferers for each of these two standards are implemented using the hardware platform PlutoSDR by Analog Devices to develop the high deployment density scenarios expected in 5G, where substantial levels of mutual interference are almost inevitable. Their influence on the spectrum occupancy is shown through 3D interference maps built using an automated testbench, which collects the received signal strength measurements at each location in the examined area for six deployment scenarios in which the number of active interferers and the density of their placement are varied. Thus, this chapter presents the following contributions:

• 3D heatmaps for different deployment densities and locations of interferers in

• Exemplifies the limitation of IoT devices' density and interference avoidance as

• Exemplifies the effect of fading for localization of interference-free areas.

**226**

The setup for the experiments is shown in **Figure 1**, and it encompasses an area with dimensions of [6000 x 2000 x 800] mm, which is the scope of coverage of the automated positioning system (APS). The APS utilizes a mechanical automated positioning tool (APT, marked with a yellow rectangle in **Figure 2**), which moves along a route preliminary programmed via the computer operating the APS. This route is within the width, length, and height of the aforementioned dimensions, that is, restricted by the four columns, as shown in **Figure 2**. A laptop computer controlling the PlutoSDR receiver which collects the measurements is mounted on the APT (**Figure 1**). The APS covers a plane with dimensions of [6000 x 2000] mm at each of these four elevation levels—0, 250, 500, and 750 mm. They form the 3D axis along which the APT moves. The 3D interference map is produced from the measurements by cubic interpolation.

Four PlutoSDR transmitters which play the role of interferers for other potential IoT nodes in both the LoRa and Wi-Fi standards are shown in **Figures 1** and **2**. They are placed at the four corners of the area's periphery (**Figure 1**; they are also marked with red rectangles in **Figure 2**). Thus, six scenarios for the interferers' density and spectrum occupancy are formed for each of the two wireless standards. Their description is outlined in **Table 1**. In the first four scenarios, the interferers are placed in the periphery, 2 m from the edge of the table which is situated in the middle of the experimental setup. In each of these scenarios, the spectrum occupancy is assessed depending on the number of active interferers. For the other two, all four transmitters are active but they are moved closer to the table—by 1 m for the middle position (S5) and by 2 m, that is, the interferers are placed on the four edges of the table for closest positions (S6).

All interferers have a transmission power of −3 dBm (PlutoSDR has an output power of 7 dBm and a 10 dB attenuation setting is applied [23]). The transmitters as well as the receivers are implemented using the GNU Radio software package [24], while the periods of transmission, reception, and measurement are managed via a Python script. The operational parameters of the SDR nodes are described in **Table 2**.

**Figure 1.** *Experimental setup and APT with (a) PlutoSDR and (b) its host computer.*

#### **Figure 2.** *APS and APT.*


#### **Table 1.**

*Deployment scenarios.*


#### **Table 2.**

*Operational parameters.*

The APS collects the measurement samples by moving along the [6000 x 2000] mm plane for each of the four heights with a step of 1000 mm in the x-coordinate (i.e., the length) and 200 mm in the y-coordinate (width), as underlined in **Figure 3**. As a result, each plane contains 77 measuring points (marked with ×). At each point, the APS performs one measurement over a period of 7 s. After covering all points of the current plane, the APT is elevated by 250 mm and the process is repeated for each of the four heights (0, 250, 500, and 750 mm in the z-coordinate).

**229**

*Interference Mapping in 3D for High-Density Indoor IoT Deployments*

To construct the 3D interference maps, the measured signal batches at each measurement point need to be filtered out so that only the samples with the strongest amplitude remain. Thus, their mean which characterizes the signal strength at this point will be maximized. The filtering is performed on the basis of energy detection spectrum sensing in the following way. For each 256 samples in the signal batch, their mean is compared against a constant decision threshold that is predetermined based on the highest instantaneous amplitude shown in the time domain representation of the batch. The higher the threshold's value is, the fewer samples will be produced in the resulting signal after the filtration. A minimal number of samples is chosen (at least a few hundred, usually in CR studies, over a few thousand [25], 30,000 in this case). If the threshold is too high for at least that number of samples to be produced, it is lowered by 2.5%. This coefficient is determined empirically as a viable compromise between the resulting number of samples and the speed of the process (a smaller reduction decreases the speed but will lead to limiting the signal samples to those which will

The mean value of the filtered signal determines the power of the received interference power at each measurement point. In each of the six scenarios, a 3D interference map is constructed for both the Wi-Fi and LoRa standards via cubic interpolation of the received interference power means of the 77 measurement points at each of the four elevation levels (0, 250, 500, and 750 mm) in a separate plane. These planes describe the 2D interference distributions (illustrated with the color map) at each height, while together they represent the interference in 3D. The interference maps for LoRa and Wi-Fi for the six scenarios are illustrated in **Figures 4**–**13**. To examine more closely some sections of the maps' layers which are partly obstructed by higher planes (i.e., the *y*-coordinate interval of [0; 1000] mm), they are represented as 3D bar plots. Such graphics are included for Scenarios **S1** and **S6** for LoRa (**Figures 5** and **8**) and **S3** and **S5** for Wi-Fi (**Figures 11** and **13**)

*DOI: http://dx.doi.org/10.5772/intechopen.93581*

*A schematic of the measurement path for a single plane.*

**2.2 Data processing**

**Figure 3.**

amount to the highest mean).

**3. 3D interference maps**

*Interference Mapping in 3D for High-Density Indoor IoT Deployments DOI: http://dx.doi.org/10.5772/intechopen.93581*

**Figure 3.** *A schematic of the measurement path for a single plane.*
