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.

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.

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.

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.