Indices of strength and location for the North Pacific Subtropical and Subpolar Gyres

The adjustment of the North Pacific Subtropical and Subpolar Gyres towards changes in wind stress leads to different time-scale variabilities, which plays a significant role in climate changes. Based on the Sim- ple Ocean Data Assimilation （SODA） and Global Ocean Data Assimilation System （GODAS） datasets, the variations of the Subtropical and Subpolar Gyres are diagnosed using ＂three-dimension Ocean Circulation Diagnostic Method＂, and established three types of index series describe the strength, meridional and depth center of the Subtropical and Subpolar Gyres. The above indices present the seasonal, interannual and in- terdecadal variabilities of the Subtropical and Subpolar Gyres, which proves well. Both the Gyres are the strongest in winter, but the Subtropical Gyre is the weakest in summer and the Subpolar Gyre is the weakest in autumn. The Subtropical Gyre moves northward from February to March, southward in October, and to the southernmost in around January, while the Subpolar Gyre moves northward in spring, southward in summer, northward again in autumn and reaching the extreme point in winter to the south. The common feature of the interannual and interdecadal variabilities is that the two gyres were weaker and to the north before 1976-1977, while they were stronger and to the south after 1976-1977. The Subpolar Gyre has made a paramount contribution to the variability on interdecadal scales. As is indicated with the Subpolar Gyre strength indices, there was an important shift from weak to strong around 1976-1977, and the correlation coefficient with the North Pacific Decadal Oscillation （PDO） indices was 0.45, which was far better than that between the Subtropical Gyre strength indices and the PDO. Tests show that influenced by small and mesoscale eddies, the magnitude of large-scale gyres strength is strongly dependent on data resolution. But seasonal interannual and interdecadal large-scale variabilities of the two gyres presented with indices is less affected by model resolution.

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

基于RSSI变化序列的MAC地址与目标匹配方法

获得真实人物与所持通信设备MAC (Media Access Control)地址的联系对公共和网络安全有重大价值。针对现有方法的不足提出了更好的解决方法。方法布置多个Wi-Fi探针获取通信设备的MAC和实时RSSI (Received Signal Strength Indication)状态变化,构建RSSI状态变化序列;利用目标跟踪获取目标相对多个Wi-Fi探针的距离,构建距离状态变化序列;对2类状态变化序列进行匹配,选取最优匹配对作为目标与所携通信设备MAC地址匹配的结果。实验表明,该方法相比其他方法通用性更强,准确率更高。

Coverage of communication-based sensor nodes deployed location and energy efficient clustering algorithm in WSN

An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-algorithms.One is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability（DLD-ECCP）.DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes.Through simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.

Variance-based fingerprint distance adjustment algorithm for indoor localization

The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication （RSSI） and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm （VFDA）. Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

Experimental study of two saturated natural soils and their saturated remoulded soils under three consolidated undrained stress paths

In this paper,an experimental investigation is conducted to study the mechanical behavior of saturated natural loess,saturated natural filling in ground fissure and their corresponding saturated remoulded soils under three consolidated undrained triaxial stress tests,namely,conventional triaxial compression test(CTC),triaxial compression test(TC)and reduced triaxial compression test(RTC).The test results show that stress-strain relation,i.e.strain-softening or strain-hardening,is remarkably influenced by the structure,void ratio,stress path and confining pressure.Natural structure,high void ratio,TC stress path,RTC stress path and low confining pressures are favorable factors leading to strain-softening.Excess pore pressure during shearing is significantly affected by stress path.The tested soils are different from loose sand on character of strain-softening and are different from common clay on excess pore water pressure behavior.The critical states in p′-q space in CTC,TC and RTC tests almost lie on one line,which indicates that the critical state is independent of the above stress paths.As for remoulded loess or remoulded filling,the critical state line(CSL)and isotropic consolidation line(ICL)in e-log p′space are almost straight,while for natural loess or natural filling,in e-log p′space there is a turning point on the CSL,which is similar to the ICL.

Improvement of location methods based on RFID

The most common location algorithms based on received signal strength （RSS） are location identification based on dynamic active radio frequency identification （LANDMARC） and virtual reference elimination （VIRE）. However, both the original algorithms suffer from some drawbacks. In this paper, several aspects of the two original algorithms have been modified to reduce the positioning errors. Firstly, Lagrange interpolation has been used instead of linear interpolation. Secondly, adaptive threshold has been introduced in the new algorithm. Thirdly, insert virtual reference tags to improve the location accuracy of the boundary of the sensing area. Finally, combine LANDMARC with VIRE to absorb both advantages. Compared with the original algorithms, on average, simulated results show that the modified algorithms can improve the location performance efficiently and achieve the goal of accurate positioning in indoor environment.

Connectivity of wireless sensor networks for plant growth in greenhouse

Wireless sensor networks have been applied in farmland and greenhouse.However,poor connectivity always results in a lot of nodes isolation in the network in a scenario.For this reason,the network connectivity is worth considering to improve its quality,especially when the collected data cannot be sent to the data center because of the obstacles such as the growth of crop plants and weeds.Therefore,how to reduce the effect of crop growth on network connectivity,and enable the reliable transmission of field information,are the key problems to be resolved.To solve these problems,the method which adds long distance routing nodes to the WSN to reduce the deterioration of WSN connectivity during the growth of plants was proposed.To verify this method,the network connectivity of the deployed WSN was represented by the rank of connection matrix based on the graph theory.Consequently,the rank with value of 1 indicates a fully connected network.Moreover,the smaller value of rank means the better connectedness.In addition,the network simulator NS2 simulation results showed that the addition of long-distance backup routing nodes can improve the network connectivity.Furthermore,in experiments,using ZigBee-based wireless sensor network,a remote monitoring system in greenhouse was established,which can obtain environmental information for crops,e.g.temperature,humidity,light intensity and other environmental parameters as well as the wireless link quality especially.Experimental results showed adding of long-distance backup routing nodes can guarantee network connectivity in the region where received signal strength indication(RSSI)was poor,i.e.RSSI value was less than−100 dBm,and the energy was low.In conclusion,this method was essential to improve the connectivity of WSN,and the optimized method still needs further research.

A Novel Adaptive Cooperative Location Algorithm for Wireless Sensor Networks

To overcome the disadvantages of the location algorithm based on received signal strength indication(RSSI) in the existing wireless sensor networks(WSNs),a novel adaptive cooperative location algorithm is proposed.To tolerate some minor errors in the information of node position,a reference anchor node is employed.On the other hand,Dixon method is used to remove the outliers of RSSI,the standard deviation threshold of RSSI and the learning model are put forward to reduce the ranging error of RSSI and improve the positioning precision effectively.Simulations are run to evaluate the performance of the algorithm.The results show that the proposed algorithm offers more precise location and better stability and robustness.