Most rural areas in developing countries are isolated due to the lack of appropriate low-cost communication technologies. Previous experiences have shown that IEEE 802.11 can be used for the deployment of large static mesh networks with only minor changes to the MAC layer that enable WiFi transceivers to work properly even for very long distances (up to 100 km in point to point links, and almost 40 km in point to multipoint setups). However, the impact of distance on performance of such long links has not been deeply analyzed. In addition, previous analytical models of IEEE 802.11 DCF cannot be applied because they implicitly assume that the propagation time can be neglected. This paper formally studies the impact of the distance on the behavior of IEEE 802.11 DCF and presents an analytical model of IEEE 802.11 DCF that accounts for distances correctly. The model is validated with simulations and within a controlled experimental framework, based on wireless channel emulation. Finally, we propose adjustments for ACKT imeout, CT ST imeout, SlotT ime, and CW min parameters that improve significantly the performance of DCF over long distances.
Indoor Location (IL) using Received Signal Strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (WiFi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used, and the actual performance of the proposed fingerprinting algorithms, are still controversial. In addition the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: (1) A non parametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models; (2) New accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our WiFi network, showing relevant information relating to the different fingerprinting schemes that can be used.
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