Improving the speed and accuracy of indoor localization:

Advances in technology have enabled a large number of computing devices to communicate wirelessly. In addition, radio waves, which are the primary means of transmitting data in wireless communication, can be used to localize devices in the 2D and 3D space. As a result there has been an increasing number of applications that rely on the availability of device location. Many systems have been developed to provide location estimates indoors, where Global Positioning System (GPS) devices do not work. However, localization indoors faces many challenges. First, a localization system should use as little extra hardware as possible, should work on any wireless device with very little or no modification, and localization latency should be small. Also, wireless signals indoors suffer from environmental effects like reflection, diffraction and scattering, making signal characterization with respect to location difficult. Moreover, many algorithms require detailed profiling of the environment, making the systems hard to deploy. This thesis addresses some of the aforementioned issues for localization systems that rely on radio properties like Received Signal Strength (RSS). The advantage of these systems is that they reuse the existing communication infrastructure, rather than necessitating the deployment of specialized hardware. Specifically, we improved the latency of a particular localization method that relies on Bayesian Networks (BNs). This method has the advantage of requiring a small size of training data, can localize many devices simultaneously, and some versions of BNs can localize without requiring the knowledge of the locations where signal strength properties are collected. We proposed Markov Chain Monte Carlo (MCMC) algorithms and evaluated their performance by introducing a metric which we call relative accuracy. We reduced latency by identifying MCMC methods that improve the relative accuracy to solutions returned by existing statistical packages in as little time as possible. In addition, we parallelized the MCMC process to improve latency when localizing devices whose number is on the order of hundreds. Finally, since wireless transmission is heavily affected by the physical environment indoors, we investigated the impact of using multiple antennas on the performance of various localization algorithms. We showed that deploying low-cost antennas at fixed locations can improve the accuracy and stability of localization algorithms indoors.Ph.D.Includes bibliographical references (p. 103-106)by Konstantinos Kleisouri

Reducing the Computational Cost of Bayesian Indoor Positioning Systems

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

Parallel Algorithms for Bayesian Indoor Positioning Systems

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 singleprocessor-node cluster, suggest that for short chains intrachain 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.

The robustness of localization algorithms to signal strength attacks: a comparative study

Abstract. In this paper, 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 propose several performance metrics that quantify the estimator’s precision 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 several point-based and areabased 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. We observed both strong experimental and theoretic evidence that all the algorithms have similar average responses to signal strength attacks.

Detecting Intra-Room Mobility with Signal Strength Descriptors

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

Demo Abstract RTBP: Real Time Bayesian Positioning System for Wireless and Sensor Networks

Localization of nodes in wireless and sensor networks is important because the location of sensors is a critical input to many higher-level networking tasks, such as tracking, monitoring and geometric-base