# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 4 The Effect of User Behaviour on HWLNets Performance
### User Mobility  
User mobility models are arranged in the following categories according to movement memory or restriction:  
* random models;
* dependency models;
* geographic models.  
The behaviour of users indoors is very complex and variable, with many different models trying to describe user movement at certain speeds [40]. One model tries to mimic user movement by defining rules a specific path which a user would follow while moving. These models are established based on an emphasis that each user will behave in a certain way in a specially designed environment, which in practice is impossible. Another type of model is random waypoint (RWP) [41] that simulates the movement of users in winding lines, touching some randomly allocated markers (so called waypoints) as they go. The variable of speed in this model is constant, and the user always moves forward. Distances in this model are short, so the speed of users could be calculated as constant over a short period of time [42]. This model is used for determining optimal locations for APs [43] in HWLNets as it gives increased mobility prediction but longer task execution time and high energy consumption. The latest research in the field of mobility-aware computational offloading is presented in [44], which is applicable to hybrid networks.  
Task offloading uses SDN-based models [44] to handle network requirements such as load balancing and handover but does not consider the user's geographic location or mobility. This can be solved by combining SDN and Markov chain to predict future network conditions. The complexity of computation in Markovian models makes prediction difficult to obtain with a high degree of accuracy. The Leavy Walk [44] model uses randomness as a function to predict user's location but cannot provide an optimal solution. Self-similar Least Action Walk (SLAW) [44] uses walk behaviour to predict the user's next location. This model has turned out to be very good in predicting user patterns if the delay can be tolerated; however, in a delay-sensitive environment, its efficiency is yet to be tested.  
Another approach is the Headlight model (HLM) [44], which uses statistical analysis of user mobility records to forecast future user locations. HLM picks up zones adjacent to the direction of movement, such as the headlight of a moving vehicle. Unfortunately, HLM has no global view of network resources, and predictions are inadequate.  
Machine learning models [44] for solving user mobility problems use Deep Neural Networks (DNN) or Reinforcement Learning (RL). These algorithms have high time complexity due to the increased number of users present and the fact that they use linear mapping with some entry parameters and features. Input data for learning models are not linear, thus adding complexity to predict user movement with these models.  
Evolutionary algorithms such as Genetic algorithm (GA), particle swarm optimization (PSO), and Ant Colony Optimization (ACO) can also be used to describe the behaviour of users because these models work very well in multi-objective scenarios [44]. GA and PSO can be used to optimize the placement of nodes based on criteria such as signal strength, coverage, and connectivity. ACO, a metaheuristic algorithm inspired by the behaviour of ant colonies searching for food, can be used to mark the user's movement through a problem space.  
The above-mentioned models have straight movement-based meaning prediction of user behaviour and are limited by movement in line, whereas in real-world scenarios, users can move in loops or elliptical curves. Solving user mobility can be achieved by new models that will consider dynamic mobility patterns. Current mobility models used in HWLNets underline that moving users are served with WiFi rather than LiFi to keep certain thresholds in QoS [45].