# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 4 IoT Applications
### Meeting the Requirements of IoT Applications  
We present an analytical study to evaluate the viability of IEEE 802.11 ah as the basis of different IoT applications by confronting the application requirements and the IEEE 802.11 ah capabilities. We collect  
Figure 3: Indoor A-MPDU Throughput vs. coverage range in IEEE 802.11: (**a**) highlights the throughput using 1 SS for 802.11n and 802.11 ac; (**b**) shows the throughput for 802.11 ah in 1, 2, 4, 8, 16 MHz CBW with 1 SS, also exposes the throughput on 1 MHz CBW and MCS10 with 1 SS; (**c**) depicts the throughput using 4 and 8 SS for 802.11n and 802.11 ac, respectively; (**d**) highlights the throughput for 802.11 ah using 4 SS. a selection of typical IoT applications, dividing them into smart applications and multimedia and smart/e-Health applications. The smart applications are further divided in two categories according to their time-related requirements: permanent connectivity and event-based applications (highlighted in Table 4). Multimedia and smart/e-Health applications (signified in Table 5) are classified by type, namely audio, video, data and biometrics. Tables 4 and 5 show the minimum number (i.e., worst case) of stations (STAs) each IEEE 802.11 ah AP can support while meeting the requirements of different IoT applications. In all of the aforementioned IoT applications, we expose the expected number of devices that an IEEE 802.11 ah standard AP can support over different distances (i.e., less than 1 km, 500 m and 250 m). In order to do that, we consider the typical data size and aggregated data rate requirements for different applications found in the literature (e.g., [15; 16]). In each case, we also assume the fastest MCS (among the set of mandatory MCS) that can be reached at those distances, according to the minimum receiver sensitivity set in the IEEE 802.11 ah specification (cf. Tables 23-31--Receiver minimum input level sensitivity in [2]). This explains why larger cells admit less users (larger distances require more robust and, therefore, slower modulations). Our evaluation scenarios are conformed by multiple IEEE 802.11 ah transmitters or STAs and one receiver (AP). In order to set a reliable lower bound, we assume the most demanding case; that is, all STAs are active and willing to transmit at the same time (i.e., saturation conditions). We start the evaluation with one STA and then we keep adding new STAs until the provided layer-2 throughput ceases to meet the requirements of the application, i.e., the obtained throughput per station is below the data rate required by the application. The throughput as a function of the number of contending STAs is computed according to the well-known [PERSON]'s analytical model [17] and considering IEEE 802.11 ah basic access parameters. Note that the specific use of IEEE 802.11 ah mechanisms, such as RAW, will improve the efficiency in the radio channel access, thus allowing an increase in device density and in the number of STAs served by one AP. Also note that we are not considering any multiplexing gain when, for most applications, it is unlikely that all associated STAs are active simultaneously. As a rule of thumb, the total number of associated devices supported could be obtained by dividing the number of devices reported in Tables 4 and 5 by the expected duty cycle of the application, measured during the hours of maximal activity. In many applications where the duty cycle is very small (e.g., few transmissions per hour or per day), the limit in the number of supported devices is actually determined by the AID field (i.e., near 8200 devices per AP) and not by the achieved throughput. For the sake of example, let us assume that the distribution automation application requires each connected device to transmit 600 Bytes (4 frames with a payload of 150 Bytes each, cf. Table 4) every 5 s. The resulting duty cycle considering the slowest bit rate (i.e., 150 kbps at MCS10) is \(<\)1.3%.