We explore the problem of detecting whether a device has moved within a room. Our approach relies on comparing summaries of received signal strength measurements over time, which we call descriptors. We consider descriptors based on the differences in the mean, standard deviation, and histogram comparison. In close to 1000 mobility events we conducted, our approach delivers perfect recall and near perfect precision for detecting mobility at a granularity of a few seconds. It is robust to the movement of dummy objects near the transmitter as well as people moving within the room. The detection is successful because true mobility causes fast fading, while environmental mobility causes shadow fading, which exhibit considerable difference in signal distributions. The ability to produce good detection accuracy throughout the experiments also demonstrates that our approach can be applied to varying room environments and radio technologies, thus enabling novel security, health care, and inventory control applications.

We show that signal strength variability can be reduced by employing multiple low-cost antennas at fixed locations. We further explore the impact of this reduction on wireless localization by analyzing a representative set of algorithms ranging from fingerprint matching, to statistical maximum likelihood estimation, to threshold bounding of signal fingerprints, and to multilateration. Using an indoor wireless testbed, we provide experimental evaluation of the localization performance under multiple antennas. We found that in nearly all cases the performance of localization algorithms improved when using multiple antennas. Specifically, the median and the 90th percentile error can be reduced up to 70 percent. Additionally, we found that multiple antennas improve the localization stability significantly, up to 100 percent improvement, when there are small-scale three-dimensional movements of a mobile device around a given location.

Recently, it has been noted that localization algorithms that use signal strength are susceptible to noncryptographic attacks, which consequently threatens their viability for sensor applications. In this work, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We study both point-based and area-based methods that employ received signal strength for localization, and propose several performance metrics that quantify the estimator's precision, bias, and error, including Hölder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of a set of representative algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. Additionally, we observed similar values for the average Hölder metric across most of the algorithms, thereby providing strong experimental evidence that nearly all the algorithms have similar average responses to signal strength attacks with the exception of the Bayesian Networks algorithm.

We show that signal strength variability can be reduced by employing multiple low-cost antennas at fixed locations. We further explore the impact of this reduction on wireless localization by analyzing a representative set of algorithms ranging from fingerprint matching, to statistical maximum likelihood estimation, and to multilateration. We provide experimental evaluation using an indoor wireless testbed of the localization performance under multiple antennas. We found that in nearly all cases the performance of localization algorithms improved when using multiple antennas. Specifically, the median and the 90th percentile error can be reduced up to 70%. Additionally, we found that multiple antennas improve the localization stability significantly, up to 100% improvement, when there are small scale 3-dimensional movements of a mobile device around a given location.

We present two parallel algorithms and their Unified Parallel C implementations for Bayesian indoor positioning systems. Our approaches are founded on Markov Chain Monte Carlo simulations. We evaluated two basic partitioning schemes: inter-chain partitioning which distributes entire Markov chains to different processors, and intra-chain which distributes a single chain across processors. Evaluations on a 16-node symmetric multiprocessor, a 4-node cluster comprising of quad processors, and a 16 single- processor-node cluster, suggest that for short chains intra- chain scales well on the first two platforms with speedups of up to 12. On the other hand, inter-chain gives speedups of 12 only for very long chains, sometimes of up to 60,000 iterations, on all three platforms. We used the LogGP model to analyze our algorithms and predict their performance. Model predictions for inter-chain are within 5% of the actual execution time, while for intra-chain they are 7%-25% less due to load imbalance not captured in the model.

In this work we show how to reduce the computational cost of using Bayesian networks for localization. We investigate a range of Monte Carlo sampling strategies, including Gibbs and Metropolis. We found that for our Gibbs samplers, most of the time is spent in slice sampling. Moreover, our results show that although uniform sampling over the entire domain suffers occasional rejections, it has a much lower overall computational cost than approaches that carefully avoid rejections. The key reason for this efficiency is the flatness of the full conditionals in our localization networks. Our sampling technique is also attractive because it does not require extensive tuning to achieve good performance, unlike the Metropolis samplers. We demonstrate that our whole domain sampling technique converges accurately with low latency. On commodity hardware our sampler localizes up to 10 points in less than half a second, which is over 10 times faster than a common general-purpose Bayesian sampler. Our sampler also scales well, localizing 51 objects with no location information in the training set in less than 6 seconds. Finally, we present an analytic model that describes the number of evaluations per variable using slice sampling. The model allows us to analytically determine how flat a distribution should be so that whole domain sampling is computationally more efficient when compared to other methods

No abstract available.