## 5 Discussion
### Economic Cost  
We next estimate the economic cost of each UAN class in an example scenario, from the point of view of the entity responsible for UAN deployment and management (e.g., a municipality). We consider a 1 km\({}^{2}\) urban area with a total of 1000 sensor nodes. This sensor node density matches the characteristics of a 30,000- inhabitant city called Sant Vicenc dels Horts, whereby the first ever smart city pilot was deployed in Spain, to our best knowledge. In this city, the number of streetlights and garbage containers per km\({}^{2}\) is approximately equal to 800 and 200, respectively. These numbers provide an order of magnitude on the sensor node density that can be expected in the type of municipalities (in the range between 20,000 and 50,000 inhabitants) that contribute the highest fraction to the total population of the country [45]. Note, however, that sensor node density and municipality characteristics may vary across cities. On the other hand, for the economic cost calculations, we assume the pricing data shown in Table 3, based on current market costs. Device acquisition costs include microcontroller, network interface(s), transducers, and robust encapsulation costs for outdoor deployment. Installation costs comprise roadworks costs for all types of devices, whereas, in addition, the installation of backhaul nodes and gateways requires works for connecting these devices to the power grid. Note that gateways need a more robust physical support than backhaul nodes, due to the greater weight and form factor of the former, thus increasing their installation cost. The cost increase is greater when the gateway has to be deployed on the street (for LR-WPAN UANs) than in a vehicle (for DTN UANs). Figure 6 illustrates the cumulative economic cost of each UAN class as a function of time, considering the initial deployment cost, as well as averaged yearly operation, maintenance and consumption (OMC) costs. The deployment cost includes acquisition and installation of sensor nodes, backhaul nodes and gateways (when necessary), whereas OMC costs include device operator subscription fees, infrastructure mains power consumption, and maintenance including sensor node battery replacement. For the latter, two economic cost values are considered: a lower bound on the battery replacement cost, which corresponds to the lifetime of an ideal 2.2 Ah AA-category battery under a regime of sending 1 notification/h, and an example scenario where such batteries have to be replaced every 5 years. AA-category batteries offer a good trade-off between cost and capacity, being 2.2 Ah a typical capacity value for this type of batteries. Note that battery characteristics will determine actual battery replacement frequency and cost. The rate of 1 notification/h has been assumed as a canonical value, intended to capture the order of magnitude of the average notification rate in Smart City applications (see Table 1). LR-WPAN UAN requires the greatest initial investment, since in addition to the sensor nodes, and in contrast with the rest of UAN solutions, backhaul nodes and gateways have to be expressly deployed. The initial investment in DTN UANs is greater than that of SO and WLAN UANs mainly because DTN UAN nodes are slightly more expensive due to the use of radio-triggered wake-up receivers. OMC cost lower bounds (_i.e._, the slope in the corresponding curves in Figure 6) are similar across technologies, since they strongly depend on the number of sensor nodes. In LR-WPAN UANs, the additional maintenance cost of infrastructure (_i.e._, backhaul nodes and gateways) is compensated by the low power consumption of sensor nodes, which leads to a low battery replacement frequency. Note that LR-WPAN UAN infrastructure would be more expensive in relative terms in scenarios of lower sensor node density. Finally, in MNO UANs the high power consumption leads to an exacerbated battery replacement cost, which can be mitigated by exploiting greater capacity batteries (see the example of using 19 Ah batteries in Figure 6).