Due to the popularity of mobile computing devices, 802.11-based wireless networks have become common place. A critical but unspecified component of 802.11 is transmission rate adaptation. Rate adaptation is difficult to do because it requires accurately estimating channel conditions while experiencing distinctly different wireless phenomena such as multi-path fast fading, nodal mobility, environmental mobility and hidden terminal interference. Rate adaptation algorithms use various methods to estimate channel conditions but these methods can be inaccurate in complex wireless environments causing incorrect adaptations. In this paper, we investigate the impact of inaccurate channel estimation methods by studying two rate adaptation algorithms, CHARM and SampleRate. We emulate high fidelity versions of both algorithms, using QualNet and evaluate their performance under environmental mobility and hidden terminal interference conditions. We found that the performance of both algorithms degrade dramatically due to inaccurate estimations caused by stale channel information. We propose two optimization techniques, REGS SINR and IntRec, to counteract these inaccuracies and implement them for CHARM and SampleRate. The proposed optimization techniques increase the performance dramatically for CHARM, up to 340% for environmental mobility and more than 290% for hidden terminal interference. We saw similar improvements of 220% and 88% for SampleRate.

Although there is growing interest in the use of physical testbeds to evaluate the performance of applications and protocols for sensor platforms, such studies also encounter significant challenges that include the lack of scalability and repeatability, as well as the inability to represent a diverse set of operational scenarios. On the other hand, simulators can typically address the preceding problems but of-ten lack the high degree of fidelity available to the analysts with physical testbeds. In this paper, we present the design and implementation of SenQ - an accurate and scalable evaluation framework for sensor networks that effectively addresses the preceding challenges. In particular, SenQ integrates sensor network operating systems with a very high-fidelity simulation of wireless networks such that sensor network applications and protocols can be executed, without modifications, in a repeatable manner under a diverse set of scalable environments. SenQ extends beyond the existing suite of simulators and emulators in four key aspects: first, it supports emulation of sensor network applications and protocols in an efficient and exible manner; second, it provides an efficient set of models of diverse sensing phenomena; third, it provides accurate models of both battery power and clock drift effect which have been shown to have a significant impact on sensor network studies; and finally it provides an efficient kernel that allows it to run experiments that provide substantial scalability in both the spatial and temporal contexts.