Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome these limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Half Plane Intersection method to Sequence-Based Localization, i.e., HPI-SBL, in distributed smartphone networks. The localization space can be divided into distinct regions, and each region can be uniquely identified by the node sequence that represents the ranking of distances from the reference nodes to the region. The key idea behind HPI-SBL is to turn the localization problem into half-plane intersection by processing the node sequence. The proposed design is evaluated through extensive simulations and physical experiments in an indoor test-bed with 30 smartphone nodes. Evaluation results show that HPI-SBL can effectively locate the acoustic source with good robustness.

Thunder localization provides an important solution to lightning location systems. This paper designs a smartphone- based thunder localization system, ThunderLoc. The key idea is to turn the localization problem into search problem in Hamming space by collecting the dual-microphone data of smartphones via crowdsensing mechanism. We utilized the TDOA of dual- microphone integrated in smartphone. After the quantization with a bit for the TDOA measurement from the smartphone nodes, thunder localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. Evaluation results demonstrate that ThunderLoc can effectively localize the virtual thunder with good robustness.

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.

Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Hamming Distance-based Method for Acoustic Source Localization, i.e., HammingLoc, in distributed smartphone networks. The key idea behind HammingLoc is to turn the localization problem into search problem in Hamming space. Time Differences of Arrival (TDOAs) of signals pertaining the same smartphone are estimated through the simple Generalized Cross-Correlation method. After the quantization with a bit for the TDOA measurement from the smartphone nodes, source localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 30 smartphone nodes). Evaluation results demonstrate that HammingLoc can effectively localize the acoustic source with good robustness

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.

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.

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 560m2in a campus building and a laboratory covering 78m2. 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.

Sensor node localization is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Convex Optimization-based Method for Event-Driven Localization, i.e., COMEDL, in large-scale wireless sensor networks. The key idea behind COMEDL is to estimate the two-dimensional location coordinates of sensor nodes by processing multiple one-dimensional node sequences that can be easily obtained via loosely guided localization event distributions. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 44 MICAz sensor nodes). Evaluation results demonstrate that COMEDL can effectively localize nodes with improved accuracy and flexibility.