## 2 Related Work
They show analytical methods that use probability distribution functions to find the probability of wrongly identifying a transmission arriving from a node with location information, reducing the localization error and thus providing more accurate positioning information to the target node. These researchers state that their algorithm works well even when only one beacon node is present. The algorithm performs calculations with low time complexity, which is suitable when memory and energy constraints are present. This work is very similar to our own, with the difference that the authors use probability distribution functions to determine viable neighbor signals, and their experiments utilize smaller scenarios (200 \(\times\) 300 m\({}^{2}\)) and a greater numbers of nodes (250-500) in order to achieve acceptable accuracy levels. [PERSON] _et al_. [18] developed a two-phase distributed algorithm for determining the positions of nodes in an _ad hoc_ wireless sensor network. The algorithm assumes that some of the users know their positions (_i.e._, they act as anchors). The anchors broadcast their positions to their neighbors, allowing the latter to estimate their positions with a certain degree of confidence, which improves iteratively with each new transmission from the anchors. This work provides collaborative positioning in outdoor environments to all participants of an _ad hoc_ network, but, unlike our model, the beacon nodes' position is transmitted to all nodes in the network and then improved iteratively, which could significantly increase the energy consumption of the process and overload the communication link. [PERSON] _et al_. [19] propose a distributed, infrastructure-free positioning algorithm that uses distance between mobile nodes to build a relative coordinate system. In this algorithm the nodes' positions are computed in two dimensions, thereby providing location information accurate enough to support basic positioning capabilities within the environment. The nodes know the position of their neighbors using the relative coordinate system, but they have no way to translate such a position to a geographic coordinate system, unless nodes with GPS capabilities are included in the network. This is an interesting proposal that enables nodes in an _ad hoc_ network to collaboratively determine the positions of each neighbor in relation to their own; however, it does not take into account the physical scenario of the devices, and thus is unable to provide the actual geographical position of the nodes. [PERSON] _et al_. [20] apply the problem of mapping an estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. To this end, they propose a spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. This work uses machine learning techniques to create an optimal ranking function, which learns from large amounts of implicit feedback, evaluating the performance of their system in a variety of real-world situations. This novel work takes advantage of social networks' positioning information to locate target devices; however, given the nature of the algorithms used, it requires an extensive location database that could be unavailable in disaster relief scenarios. [PERSON] [21] presents a study of the effect of some radio propagation models, such as free space and two-ray ground, on the communication capability of devices in simulated urban mobile _ad hoc_ networks. The study shows that commonly used propagation models do not necessarily provide results similar to real-world conditions. Thus, choosing an incorrect propagation model could translate into misleading positioning measurements, particularly when using Wi-Fi signal strength. Similarly, [PERSON] _et al_. [22] provide a detailed explanation of mobility models used on _ad hoc_ network research, as well as an evaluation of their performance, and [PERSON] _et al_.