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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.

Civilian unmanned aerial vehicles (UAVs) have been increasingly used in problematic ways. For instance, more and more UAVs disrupt flights and peep into privacy. This problem is likely to expand given the rapid proliferation of UAVs for commerce, monitoring, recreation, and other applications. In this paper, we propose a UAV presence detection system which identifies signal signatures by using the UAV's RF communication. We explore UAV's physical characteristics, including the mobility due to fast moving, spatiality due to UAV's 3D nature, and vibration due to its wing rotation. We consider whether the received UAV signals are uniquely differentiated from other wireless devices. We thoroughly analyze the angle of arrival (AoA) in 3D space, and flexibly apply super-resolution AoA estimation method to calculate the elevation in 3D place. We conduct spectrum on fine- grained channel state information data, and successfully detect the frequency of UAV vibration due to the rotation of UAV's propellers, and finally improve the accuracy through a clustering algorithm. Our system is prototyped and evaluated using commodity WiFi devices in real-world environment. Our system shows a good performance, which achieves 86.6% of accuracy, 87.3% of precision and 85.8% of recall.

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.

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.

Localization based on received signal strength indication (RSSI) is a low cost and low complexity technology, and it is widely applied in distance-based localization of wireless sensor networks. The error of existing localization technologies is significant. This paper presents the N-times trilateral centroid weighted localization algorithm, which can reduce the error considerably. Considering the instability of RSSI, we use the weighted average of many RSSIs as current RSSI. To improve the accuracy, we select a number of (no less than three) reliable beacon nodes to increase the localization times. Then we calculate the distances between reliable beacon nodes and the mobile node using an empirical formula. The mobile node is located N times using the trilateral centroid algorithm. Finally, we take the weighted average of the filtered reference coordinates as the mobile node's coordinates. We conduct experiments with the STM32W108 chip, which supports IEEE 802.15.4. The results show that the proposed algorithm performs better than the trilateral centroid algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

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.

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 \(100\%\).

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.

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.

In this paper, we pay attention to the problem of node search in Wireless Sensor Networks (WSNs). The main problem of basic flooding is that the cost is too high. In order to solve above problem, we propose a variant of Flooding called Level Based Flooding (LBF). In LBF, the whole network is divided into several layers according to the distance (hops) between the nodes and the sink node. The sink node knows the level information of each node. The search packet is broadcast in the network according to levels of nodes and its TTL is set to the level of the target node. When the target node receives the packet, it sends its data back to the sink node in random walk within level hops.We show by extensive simulations that the energy consumption of LBF is much better than that of basic flooding.

Analyze the characteristics of network security courses and the use of task-driven teaching practical significance. Discuss the detailes to carry out all aspects of curriculum, including classroom teaching, task-driven experimental procedures, evaluation system of the re-establishment and task-driven implementation of teaching effectiveness.

Disparity estimation plays an important role in multiview video coding to eliminate inter-view redundancy with the cost of high computational complexity. In this paper, a fast disparity estimation has been proposed for low complexity multiview video coding utilizing the disparity field's correlation and epipolar geometry. The spatial-temporal correlation of disparity field is exploited to select the search center and rough searching range firstly. Then, the searching range is further confined according to the information of epipolar geometry. The experimental results show that 58% reduction in coding time has been achieved compared with the original fast-search algorithm in MVC reference software with negligible degradation in rate-distortion performance.

Human-computer interaction for in-vehicle information and navigation systems is a challenging problem. In this paper, we propose a robust system for speaker localization and speech enhancement for an in-vehicle speech dialog system by using microphone arrays. The performance of traditional localization methods drastically decline in a moderately reverberant environment. In this paper, we introduce Gauss-Hermite filter to integrate the current observation, effectively steer particles towards regions with high likelihood. The sound source localization (SSL) method based on the model of the precedence effect is integrated into the framework of speaker localization based on particle filters. The simulation indicates that the proposed algorithm is able to accurately track speaker in a moderately reverberant car environment.