# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 6 HWLNets in IoT Applications
### Indoor Positioning  
Positioning can be defined by the type of system used. There are two basic categories: outdoor and indoor. The first is widely known as a form of service employed by most consumer devices for navigation, mapping, and tracking. This service relies on satellite radio navigation data sent by global positioning system (GPS) or other variations of it such as GLONASS or BeiDou. All these systems provide GPS data to a receiver with some degree of accuracy. Using L5 band, GPS can provide accurate reading within a 30-centimetre diameter [77]. For indoor positioning, these systems are far less accurate due to the physical properties of the signal passing through non-opaque objects such as reinforced concrete or multi-storey buildings. Even if the signal passes through with enough strength in indoor scenarios, accuracy is considered in centimetres unlike metres for outdoor scenarios. High indoor accuracy can only be accomplished using positioning systems that are in the same area as the users. One such method is to use short range technologies such as Bluetooth or ZigBee. Here, we focus on alternative means of positioning using LiFi-WiFi combination methods proposed in [78; 79].  
Indoor positioning uses algorithms, represented in Figure 12, and information obtained from user devices or APs to calculate the exact position of a user. Triangulation algorithm uses at least three neighbouring APs to measure the distance and angle between the user's device and AP. This method is similar to the cell tower triangulation techniques used by service provides and offers pinpoint accuracy but has a high degree of system complexity. Triangulation is usually two-dimensional but in case the LiFi user's equipment is susceptible to orientation, as seen in Section 4, we can implement an additional AP beacon for three-dimensional triangulation [80].  
Another algorithm used is proximity that connects a user's device in regard to AP coverage area, thus having a low computational complexity. For LiFi APs that are usually densely deployed, a proximity algorithm can have decent results.  
Figure 12: Illustration of positioning methods: (**a**) triangulation, (**b**) proximity, and (**c**) fingerprint.  
Lastly, location-dependent information algorithms such as fingerprint are suitable for indoor positioning. Using off-line radio maps and RSS, this algorithm calculates the Euclidean distance between a map and real-time data [81]. If the maps are accurate, this algorithm has a high degree of accuracy.  
Indoor positioning models can determine a user's position using information such as RSS, time of arrival, angle of arrival, or time difference of arrival. Using an RSS model of positioning, reduction of channel throughput gives an approximate distance between users and AP. This model's major drawback is susceptibility to errors due to multipath propagations [82].  
Using time of arrival to compute distance requires time sync between the user's equipment and AP [83]. This problem can be bypassed by having multiple transmitters in the AP or receivers on user's devices to calculate the time difference between the signals [84]. Nevertheless, time sync is needed between APs.  
Angled differences in a signal that is transmitted and the nominal angle of AP are used to obtain the position in RF systems by detecting phase differences between antennas [85]. For LiFi technology, this cannot be applied, thus a new approach has been researched. Image transformation calculates distance using trigonometry of the LiFi AP's position on a map and light image on the photodiode of the receiver [86].