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This paper presents DiVA, a novel hybrid range-free and range-based acoustic source localization scheme that uses an ad-hoc network of microphone sensor nodes to produce an accurate estimate of the source's location in the presence of various real-world challenges. DiVA uses range-free pairwise comparisons of sound detection timestamps between local Voronoi neighbors to identify the node closest to the acoustic source, which then estimates the source's location using a constrained range-based method. Through simulation and experimental evaluations, DiVA is shown to be accurate and highly robust, making it practical for real-world applications.

In this paper, we propose an Error Tolerant Dual-Hydrophone Localization method, i.e., ET-DHL, in underwater sensor networks (USNs). To reduce the impact of the node uncertainty, measurement uncertainty, poor link quality and long latency for underwater localization problem, ET-DHL adds bit-level probability to process the binary sequence. Different from our previous work PCM, each dual-hydrophone node in ET-DHL leverages different probabilities based on TDOA to distinguish which half-plane the source lies in, and ET-DHL takes the sum of expectations of all small regions by normalizing all weighted probability to localize the source. The proposed design is evaluated through extensive simulations, evaluation results demonstrate that ET-DHL can effectively locate the acoustic source with good robustness for node location error, node angle error and measurement uncertainty.

In this paper, we propose a novel dual-hydrophone localization method, termed DHL, in underwater sensor net-works. The objective is to reduce the impact of the serious disturbance of noise, poor link quality, long latency, limited band-width and low data rate. For underwater localization problems, DHL uses dual-hydrophone nodes to convert the localization problem into half-plane intersection issues. In particular, DHL adopts the sign of TDOA (Time difference of arrival) from the acoustic source to the pairwise hydrophones as the binary measurement information, in order to narrow the feasible region of the acoustic source. To test the performance of DHL, we evaluate it with extensive simulations. The simulation results demonstrate that DHL achieves higher robustness than the TDOA-based method when location and angle errors of nodes exist. Consequently, DHL is a promising scheme for underwater localization in adverse conditions.

This paper presents DiVA, a new acoustic source localization scheme that uses an ad-hoc network of microphone sensor nodes to produce an accurate estimate of the source's location. DiVA uses pairwise comparisons of sound detection timestamps between local Voronoi neighbors to identify the node closest to the acoustic source and then estimates the source's location. The scheme improves on the state of the art by effectively dealing with anchor nodes' position error, time stamp measurement error and time synchronization error in real world conditions. Through simulation and experimental evaluations, DiVA is shown to be more robust than existing solutions under different error conditions.

This paper proposes a new acoustic source localization scheme, called Probabilistic Cutting Method (PCM), with randomly deployed smartphones which equipped with known location and direction dual-microphones. Instead of using the value of TDOA (Time Difference Of Arrival), we just use binary information (0/1) and probability to convert the localization problem into plane cutting issues. We can easily come up with the Basic Cutting Method (BCM), but it may appear empty set when error (location error, angle error or error anchors) occurs. PCM can effectively avoid the problem along with lower positioning error. When comparing PCM with TDOA and BCM in different aspects, simulation evaluation results indicate that PCM algorithm achieves highly robustness and accuracy.

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m 2 in a campus building and a laboratory covering 78m 2 . Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.