An Improved Underwater Acoustic Network Localization Algorithm

Underwater sensor network can achieve the unmanned environmental monitoring and military monitoring missions.Underwater acoustic sensor node cannot rely on the GPS to position itself,and the traditional indirect positioning methods used in Ad Hoc networks are not fully applicable to the localization of underwater acoustic sensor networks.In this paper,we introduce an improved underwater acoustic network localization algorithm.The algorithm processes the raw data before localization calculation to enhance the tolerance of random noise.We reduce the redundancy of the calculation results by using a more accurate basic algorithm and an adjusted calculation strategy.The improved algorithm is more suitable for the underwater acoustic sensor network positioning.

A Survey on Distributed Network Localization from a Graph Laplacian Perspective

Network localization serves as a fundamental component for enabling various position based operations in multi-agent systems,facilitating tasks like target searching and formation control by providing accurate position information for all nodes in the network.Network localization focuses on the challenge of determining the positions of nodes within a network,relying on the known positions of anchor nodes and internode relative measurements.Over the past few decades,distributed network localization has garnered significant attention from researchers.This paper aims to provide a review of main results and advancements in the field of distributed network localization,with a particular focus on the perspective of graph Laplacian.Owning to its favorable characteristics,graph Laplacian unifies various network localization,even when dealing with diverse types of internode relative measurements,into a unified protocol framework,which can be constructed by a linear method and ensure the global convergence.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

Waveguide Bragg Grating for Fault Localization in PON

Femtosecond laser direct inscription is a technique especially useful for prototyping purposes due to its distinctive advantages such as high fabrication accuracy,true 3D processing flexibility,and no need for mold or photomask.In this paper,we demonstrate the design and fabrication of a planar lightwave circuit(PLC)power splitter encoded with waveguide Bragg gratings(WBG)using a femtosecond laser inscription technique for passive optical network(PON)fault localization application.Both the reflected wavelengths and intervals of WBGs can be conveniently tuned.In the experiment,we succeeded in directly inscribing WBGs in 1×4 PLC splitter chips with a wavelength interval of about 4 nm and an adjustable reflectivity of up to 70% in the C-band.The proposed method is suitable for the prototyping of a PLC splitter encoded with WBG for PON fault localization applications.

A Survey of Ranging Algorithms and Localization Schemes in Underwater Acoustic Sensor Network

Underwater Acoustic Sensor Network(UASN) has attracted significant attention because of its great influence on ocean exploration and monitoring. On account of the unique characteristics of underwater environment, localization, as one of the fundamental tasks in UASNs, is a more challenging work than in terrestrial sensor networks. A survey of the ranging algorithms and the network architectures varied with different applications in UASNs is provided in this paper. Algorithms used to estimate the coordinates of the UASNs nodes are classified into two categories: rangebased and range-free. In addition, we analyze the architectures of UASNs based on different applications, and compare their performances from the aspects of communication cost, accuracy, coverage and so on. Open research issues which would affect the accuracy of localization are also discussed, including MAC protocols, sound speed and time synchronization.

Phase Retrieval via Sensor Network Localization

The problem of phase retrieval is revisited and studied from a fresh perspective.In particular,we establish a connection between the phase retrieval problem and the sensor network localization problem,which allows us to utilize the vast theoretical and algorithmic literature on the latter to tackle the former.Leveraging this connection,we develop a two-stage algorithm for phase retrieval that can provably recover the desired signal.In both sparse and dense settings,our proposed algorithm improves upon prior approaches simultaneously in the number of required measurements for recovery and the reconstruction time.We present numerical results to corroborate our theory and to demonstrate the efficiency of the proposed algorithm.As a side result,we propose a new form of phase retrieval problem and connect it to the complex rigidity theory proposed by Gortler and Thurston(in:Connelly R,Ivic Weiss A,Whiteley W(eds)Rigidity and symmetry,Springer,New York,pp 131–154,2014).

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

Computer Network Control System in Automatic Automobile Detection

An automobile test controlling and managing system with computer network is introduced. The architecture of the local network, hardware structure, software structure, design of the test process, and error tolerating redundant design in work position substituting are presented. At last, the pivotal questions solved are discussed. With an advanced structure, this system is multi functional and flexible. Furthermore, advanced computer technology is adopted to improve its technical degree.

A Transport Protocol and Its Formal Description for Local Networks

This paper discusses a transport protocol and its formal description techniques for local network. The transport layer function, the transport services and a transport protocol design in a local network architecture model are presented. A transport protocol specification using the finite state automata (FSA) is given. The correctness of the protocol is verified by using the reachability tree technique with respect to the protocol properties of completeness, deadlock and livelock freeness, termination and reachability.

Domain adaptive methods for device diversity in indoor localization

To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.

Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

Analysis of coseismic effect on temperature in the Three Gorges well network

Through the Three Gorges well network, we examine different coseismic changes in water temperature caused by local earthquakes since 2008, and offer a mechanistic explanation.The relations between the coseismic changes in water temperature and the parameters of distant and local earthquakes are deduced.

Study on Optimal Topology for Computer Local Double Loop Networks

A dist ributed optimal local double loop (DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter d and average hop distance a for this class of networks are [3N- 2]≤d≤[3N ] and (5N/9 (N-1))-(3N -1.8)<a<(5N/9(N-1)) (3N -0.9),respectively (N is the number of nodes in the network ). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed and analyzed. The correctness of the algorithm has also been verified by simulating.

Detectability and reliability analysis of the local seismic network in Pakistan

The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.

A Structured Methodology for Local Network Design Engineering

This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.

RELAY ALGORITHM BASED ON NETWORK CODING IN WIRELESS LOCAL NETWORK

The network coding is a new technology in the field of information in 21st century. It could enhance the network throughput and save the energy consumption, and is mainly based on the single transmission rate. However, with the development of wireless network and equipment, wireless local network MAC protocols have already supported the multi-rate transmission. This paper investigates the optimal relay selection problem based on network coding. Firstly, the problem is formulated as an optimization problem. Moreover, a relay algorithm based on network coding is proposed and the transmission time gain of our algorithm over the traditional relay algorithm is analyzed. Lastly, we compare total transmission time and the energy consumption of our proposed algorithm, Network Coding with Relay Assistance (NCRA), Transmission Request (TR), and the Direct Transmission (DT) without relay algorithm by adopting IEEE 802.11b. The simulation results demonstrate that our algorithm that improves the coding opportunity by the cooperation of the relay nodes leads to the transmission time decrease of up to 17% over the traditional relay algorithms.

Estimation of azimuth and slowness of teleseismic signals recorded by a local seismic network

A new method that is applicable to local seismic networks to estimate the azimuth and slowness of teleseismic signals is introduced in the paper. The method is based on the correlation between the arrival times and station positions. The analyzed results indicate that the azimuth and slowness of teleseismic signals can be accurately estimated by the method. Average errors for azimuth and slowness measurements obtained by this method using data of Xian Digital Telemetry Seismic Network are 2.0?and 0.34 s/(?, respectively. The conclusions drawn from this study indicate that this method may be very useful to interpret teleseismic records of local seismic networks.

Application of Wireless Local Area Network Technology in Mobile Robot for Finned Tube Inspection

This paper introduces a novel robot for outer surface inspection of boiler tubes. The paper describes the hardware system, wireless communication strategy, communication procedure and system software of the robot. The WLAN technology is used in the robot. It solves the problem of shielding generated by iron boiler and 11Mbps bandwidth made it possible for video and control stream real-time transmit within the same channel. Though TCP/IP protocol is robust, serial server is a transparent channel but cannot detect error and retransmit the data. In order to improve the reliability of serial communication, a new communication protocol is proposed. Key words boiler tubes - mobile robotics - wireless local area network Project Supported by the National High-Tech Program (Grant No. 2002AA420080)