Naigao Jin

UPMAC: A Localized Load-Adaptive MAC Protocol for Underwater Acoustic Networks

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput and higher energy cost of transmission than reception. Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput. Receiver-based protocols, like receiver-initiated packet train and cluster-based on-demand time sharing, can significantly reduce collision and retransmission. But they are either time-consuming or energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol (UPMAC), whose objective is to be adaptive to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption by using the technique of piggyback. UPMAC uses receiver-based approach in high load mode, leading to a low data collision rate in both one-hop and multihop situation. Extensive simulations show that UPMAC can achieve better performance in both general and Sea Swarm topologies.

A novel dual-hydrophone localization method in underwater sensor networks

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.

INBS: An Improved Naive Bayes Simple learning approach for accurate indoor localization

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 560m2 in 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.

A probability-based acoustic source localization scheme using dual-microphone smartphones

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.

DiVA: Distributed Voronoi-based acoustic source localization with wireless sensor networks

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 sources 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 sources 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.

Error tolerant dual-hydrophone localization in underwater sensor networks

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.

RTDA: A Novel Reusable Truthful Double Auction Mechanism for Wireless Spectrum Management

In the secondary spectrum market, more and more primary users (PUs) release their idle spectrum to secondary users (SUs). While some of the existing auction mechanisms are truthful, few of them emphasize achieving a high usage rate. Even the SUs get the channel they require, the spectrum resource is still wasted in the spare time. In this paper, we propose a Reusable Truthful Double Auction (RTDA) mechanism for spectrum management, which considers temporal reuse and improve the usage rate significantly. Mathematical inference and game theory is used to prove that RTDA is economic-robust. The simulation results show that RTDA significantly improves the spectrum usage rate. In certain scenario, the usage rate can reach up to \(100\%\).

Acoustic Source Localization with Distributed Smartphone Arrays

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