# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 2 Related Work  
As stated before, P2P networks may provide a solution to the exponential growth of connected devices. Indeed, by 2030, we expect to have hundreds of millions of devices in indoor environments [9]. As such, P2P networks reduce--and in some cases avoid--bottlenecks at the servers, since each node connected to the system provides additional resources. P2P networks have been extensively studied before in works such as [10]. However, our work proposes a totally different mathematical analysis based on transitory CTMCs, while in these previous works, they use irreducible CTMCs. The reason for this is to model static P2P networks, where nodes have to maintain a certain degree of alignment to allow for the use of LiFi communications. Also, since nodes are not allowed to enter or leave the system in the considered applications, we model managed networks where the administrator controls the download procedure. As stated earlier, this is the first work that develops such models for P2P networks with LiFi capabilities. Although there are a large number of works related to performance analysis in LiFi networks, most of them focus on the study and modeling of sender-receiver orientations [11; 12], as well as their radiation patterns, distances, and angles of incidences [13] to estimate metrics such as signal-to-noise ratio (SNR), signal-to-interference-and-noise ratio (SINR), and bit error ratio (BER). Therefore, another important contribution of our work is to present relevant data about the performance of a LiFi network through the estimation of its download times under certain environments and configurations. By taking a look at alternatives to improve network performance, we find the use of decentralized networks such as those implemented in _decentralized federated learning (DFL)_ models through _machine learning (ML)_ as presented by [PERSON] et al. [14], which looks for the exchange of parameters between neighbors promoting _device-to-device communication (D2D)_ to reduce communication costs in IoT environments and thereby improve data compression and collaboration efficiency. In the same way, [PERSON] [15] presents the idea of a decentralized system through a decentralized deep reinforcement learning (DRL) for each user to dynamically learn the selection of bands that maximizes its download rate, improving resource utilization and predominance of _quality of service_ (_QoS_) in the system and modeling the behavior of each _access point_ (_AP_) as a Markov chain. The previous work shows the efforts being made in the field of decentralization structures in networks for the optimization of resources due to the way that nodes behave in the network and that they can be modeled by Markovian processes. However, important efforts also predominate in the types of technologies that are used, and in many cases, complement to give way to new technological alternatives. Despite the fact that most research considers total mobility in P2P networks, in the context of static managed networks, there are some interesting works, such as those presented by [PERSON] [16], [PERSON] [17], and [PERSON] [18], where semistatic P2P networks are studied using mathematical models and simulations, in which some of the nodes are maintained with some mobility over time and the rest remains static, obtaining network performance metrics such as success rate, messages per requests, successful delivery ratio, and latency, among others (unlike these works, we do not measure the impact of network mobility; on the contrary, we consider nodes with null mobility, according to the principles of operation of LiFi). Interestingly, there is another range of work related to P2P networks in which a description is made of the advantages of using decentralized structures instead of classical ones through specific techniques and protocols such as Juxtapose (JXTA) [19; 20], Context-Aware Recommendations (CARS) [21] or Coordinated Multi-RobotExploration Aquila Optimizer (CME-AO) [22] in the branches of genetic computing, networking, and automation.