## 4 Experimental Design
### Experiment Implementation
#### 4.2.3 Communication Behavior
However, the model follows the lines of peer-to-peer (P2P) networks file sharing, which are highly dependent on the user's intent, but still have been proven to be relatively safe for most of the user's operations (specifically file sharing). Ensuring the security and privacy of the shared information is a challenge that will be addressed as part of our future work. ## 7 Conclusions and Future Work

This paper presents a context-aware positioning model for outdoor environments, particularly to support disaster relief efforts. The model enables devices to estimate their position based on information gathered from their context, either directly from a positioning system or through collaboration with neighboring devices. This capability is required due to the fact that in disaster relief efforts, at least in developing countries, there is a large variety of mobile computing devices with no capability of self-positioning, but that potentially can be useful to support the response activities. The proposed model also considers the typical features of these working environments; _i.e._, unavailability of infrastructure-based electrical energy and digital communication, heterogeneity of devices, and instability of the communication link. The model succeeds in providing a degree of positioning to mobile applications running on devices otherwise unable to position themselves, by sharing positioning information with their neighbors. However, due to the nature of the collaboration (_i.e._, the reference points' positions have a variable error), accuracy is significantly lower than using a positioning strategy directly, showing errors ranging from less than 1 m to over 12 m greater than when using GPS. Thus, the estimations need to be performed at shorter time intervals in order to correct eventual inaccurate measurements. Likewise, by using the set of recommendations assembled by the model, a device is able to access any of the positioning strategies available in its surroundings. This eliminates the need to start the whole positioning process over when there has been no significant change in context, since the device can use any of the recommended strategies, ideally in order of priority. The interval at which the model senses the environment depends on the speed of the device, and on the position accuracy requirement of the consumer application. In terms of energy, despite advances in battery technology, maintaining an active connection (e.g., GPS or Wi-Fi) consumes significant amounts of power. Thus, the model must make additional decisions to ensure extended operation to the devices, e.g., sensing the context of a device to determine whether it is moving or not, in order to avoid requesting an unnecessary positioning estimation, or determining whether a device has enough energy to go through the positioning process without jeopardizing the device's basic functions. Although this approach greatly reduces the energy consumption, sensing the context is still energy demanding. This means that an optimal context-sensing interval must be found in order to ensure less energy usage. Future work involves using a random forest classifier in place of the randomized decision tree that is currently used by the model. The random forest strategy [41] performs very well compared to many other classifiers, including discriminative analysis, support vector machines, and neural networks, and it is robust against over-fitting. We believe this could help improve the recommendations of positioning methods given by the model. Moreover, we plan to conduct an evaluation of the energy consumption of the proposed model, not only to node level, but also considering the whole interconnected system. Several settings of the application scenario should be simulated and analyzed. The obtained results will help us improve the positioning priorities considered during the strategy choosing process performed by the model. Security and privacy issues related to the shared information are also some of the challenges to address during the next step.