## 3 Context-Aware Positioning Model  
This context-aware positioning model was designed to (a) allow users (_i.e.,_ stationary and mobile devices) to estimate their positions in outdoor environments, specifically in urban areas affected by disasters, either by accessing positioning strategies directly, or by sharing positioning information with neighboring users having known positions; and (b) allow a smooth transition between the use of different positioning strategies. Given the model is able to use several strategies for providing this positioning,the process of choosing a particular strategy for a specific node also considers its energy consumption and the impact that it has on the whole positioning process.  
A device making use of the model would utilize its peripherals to sense its environment and gather contextual information, which is used to determine a set of suitable positioning strategies from those available in the scenario. Devices with positioning capabilities could either directly access positioning strategies available in the scenario (_i.e._, self-positioning), such as GPS or radio frequency identification (RFID), or use their neighbors' positions as reference points (_i.e._, collaborative positioning).  
Similarly, devices that have no positioning capabilities or that are somehow impeded from performing self-positioning could also take advantage of collaboration to estimate their position, using the positioning information of neighbors with known positions as input. Thus, the model could provide this capability to devices with access to at least one positioning strategy present in a given environment, enabling users to perform positioning in situations where they would normally be unable to, as well as favoring positioning strategies that would expend less energy than using the default strategy (usually GPS).  
It is important to mention that the model does not aim to improve the accuracy of the devices' positioning estimations. The model addresses the problem of providing context-aware positioning, with reasonable energy consumption, to devices participating in disaster relief efforts, regardless of whether these devices have the peripherals required to support such activity.  
Although the model does not particularly address indoor environments, in several cases these scenarios are also supported, because the positioning is performed considering the nodes with high confidence about their position. However, the focus of the proposed model is on outdoor environments, and therefore the estimations in indoor environments have a low accuracy (but are useful to support several disaster relief activities).  
Given the nature of the process to be supported, the outdoor positioning is always mandatory and indoor positioning is (in the best case) desirable. Typically, indoor positioning in disaster relief efforts is used only to provide coarse-grained awareness information about the resources' location (_i.e._, first responders and equipment), given that the reference points required for such positioning strategies are usually unavailable or in a new location (e.g., in the case of collapsed buildings). Therefore, if the outdoor positioning mechanism used by the first responders is able to provide coarse-grained positioning  
Figure 1: Three stage context-aware positioning model.  
information when these people are under a roof, the use of a specific indoor positioning strategy is usually discarded. Provided that the proposed model has such a capability, improving its support for indoor positioning is only desirable. This model involves a three-stage process (Figure 1): (1) sensing the context, (2) managing the context information, and (3) performing the context-aware positioning. The following subsections describe the processes considered in these stages.