# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Comparing IoT Technologies and IoT Enablers  
After discussing the positioning domain and the main system parameters relevant to positioning, here we present two comparative tables between 29 IoT solutions (see Tables 1 and 2), whose goal is to sum up the key points mentioned so far and to enable an easy comparison between the different technologies. Throughout the rest of this subsection, our goal is to make comparisons and drive the reader towards a better understanding of how a certain technology would fare as the backbone of a positioning system, in a GNSS-free case. Table 1 presents for each technology, from left to right, the network topology, network type, the impact of each measurement domain on the device battery life and cost, the achievable positioning accuracy, the most suitable domain and reported accuracy studies. The first column maps each technology's topology to either a star or mesh topology. The second column presents the network type, where each entry starts with the rate, power consumption and maximum operating range offered by the technology (see section ). The operating range has a correlation with the frequency bands in Table 2.  
The third column presents the impact on battery consumption and device cost for each measurement domain in discussion. While most of the technologies do not offer such capabilities, this classification assumes that it would be possible to couple the necessary measurement units to provide such information. Hence, the classification of low impact \((+)\), medium \((++)\) or high impact \((+++)\) are based on what the authors expect to be the addition burden in terms of device cost and battery burden. The power domain is seen to be the one with the smallest impact, due to the fact that it would be easily available to all of these technologies. The fourth column provides a qualitative indicator for the expected accuracy based on what the technology currently offers. When available, this information is based on the related studies. The fifth column states the most suitable measurement domain to use with each technology. The domain is attributed based on the technology's signal characteristics presented in Table 2 and its current capabilities. Table 2 describes each technologies' key physical aspects, such as frequency bands, channel bandwidth and modulation type. Positioning services often have a high demand for power consumption. Operating a positioning based infrastructure is often tied to the need of having a fully plugged-in (powered) infrastructure. However, there are several industrial applications that would benefit from a fully battery-operated network, especially where an electricity network might not yet be present, e.g., construction sites, or for facility of service extension and maintainability. In terms of positioning, we found that most IoT systems are yet to offer specific signaling to support accurate measurements for localization. Few of the existing IoT systems have already raised interest in the academic field in terms of their positioning capabilities, as shown in the last column of Table 1. Most of the existing studies focus on RSS-based approaches and several of them rely on low-cost probabilistic methods requiring an underlying path loss model. Few studies that focus on time-based and space-based approaches are mostly targeting the current and future cellular IoT signals, derived from LTE, such as LTE-M, which are retaining some of LTE's positioning characteristics such as positioning-specific signaling. In addition, future 5G networks are likely to rely on time-based and space-based positioning approaches. Our paper further contributes with additional results based on RSS and time-based approaches as shown in the next sections.