New Path Centrality Based on Operator Calculus Approach for Wireless Sensor Network Deployment

Syarif, Abdusy; Abouaïssa, Abdelhafid; Idoumghar, Lhassane; Lorenz, Pascal; Schott, René; Staples, G. Stacey

doi:10.1109/tetc.2016.2585045

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…There are differences in muscle activity among different subjects. In order to uniformly compare the effects of different parameter eigenvalues on fatigue characterization, it is necessary to normalize the surface EMG signal [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Wang

2022

Scanning

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In order to use the surface EMG signal to automatically detect the muscle fatigue state, a research method of the muscle exercise fatigue intelligent scanning detection system based on surface EMG was proposed, and the sEMG signal features of 10 subjects before and after fatigue were extracted. A time-varying parameter autoregressive model is established. By introducing the Legendre basis function, the parameter identification of the linear nonstationary process is transformed into the parameter identification of the linear time-invariant system. Combined with the correlation index, the optimal Legendre base function dimension of the time-varying system parameter estimation can be obtained, then the best model fitting effect can be obtained, and the time-invariant parameters are solved by the least square method. Using the rate of change of the first time-varying parameter (ARC1) of the autoregressive model before and after fatigue as an index to detect muscle fatigue sensitivity, a two-tailed t test was used to compare the mean power frequency (MPF) and the median frequency (MF) with the rate of change. The results showed that the change rates of ARC1, MPF, and MF before and after fatigue were 34.33 % ± 2.5 % , 68 % + 2.03 % , and 22.80 % + 2.19 % , which were 41% and 25%, respectively. The rate of change of ACR1 was significantly higher than that of MPF and MF ( P < 0.05 ). When detecting muscle fatigue by sEMG signal, it has the advantages of short time and high sensitivity. It can be used for online real-time analysis of muscle fatigue, providing a potential analysis tool for limb muscle strain, rehabilitation, and ergonomics assessment.

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Section: Methodsmentioning

confidence: 99%

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Wang

2022

Scanning

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“…With the development and progress of communication technology, embedded computing technology and sensor technology, sensor networks with perception, computing, and communication capabilities increasingly show broad application prospects. The wireless sensor network comprehensively utilizes sensor technology, embedded computing technology, distributed information processing technology, and communication technology to monitor, perceive, and collect various environmental information in the network distribution area in real time, process this information, and transmit it to the users who need this information, so as to make scientific and reasonable decisions [1]. When the wireless sensor network (WSNS) is affected by external events (such as forest fires, geological disasters, and air pollution) or the sensor node itself has software or hardware failures (such as software defects, insufficient battery power, and electromagnetic interference), the measurement data of sensor nodes will be abnormal [2].…”

Section: Introductionmentioning

confidence: 99%

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Li¹,

Sharma

2022

Journal of Sensors

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In order to solve the abnormal behaviors in wireless sensor networks, such as attacks, intrusions, node failures, and data anomalies, a data anomaly detection method for sensor networks based on DNN algorithm and cluster analysis is proposed. The deep neural network is introduced into the wireless sensor network, and each wireless sensor data is described by the neuron to construct the neural network element model. The traditional neural network model is improved, and the neural network model of the wireless sensor is used to realize the fusion and extraction of the data collected by the wireless sensor network. The clustering technology is used to complete the abnormal data judgment of the nodes, and at the same time, the spatial correlation of the sensing data between neighboring nodes is used to filter the noise data, extract the abnormal event information, and assist the system decision-making. The results show that when the number of neighbor nodes increases, the number of nodes with similar physical locations in the optimal neighbor node set gradually increases, and the accuracy rate is also continuously improved. However, if too many nodes are selected, the algorithm will use the data of nodes with a longer physical distance to vote, which will lead to an increase in the error rate. Therefore, when determining the number of neighbor nodes, 25%-30% of the total number of nodes in the positioning scenario can be selected. Therefore, the algorithm can effectively detect and distinguish environmental noise and abnormal events in the network.

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“…Wireless sensor networks [13] another important feature of the optimization way.…”

Section: Related Workmentioning

confidence: 99%

Avant-Grade framework Routing Optimization Based on Established Network Lifetime and Energy Management in WSN

Kumar¹,

Jayapandian²,

Balasubramanie³

2021

Preprint

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Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. The existing system of LTDC has more drawbacks in network routing optimization lifetime and energy level improvement needed. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled Avant-Grade framework Routing Optimization [AGFRO]. AGFRO is designed to minimize energy consumed in each node by reducing the amount of time in which a sensor node is in an idle listening state and reducing the average communication distance over the network. The performance of the proposed system has been critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known AGFRO protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN.

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New Path Centrality Based on Operator Calculus Approach for Wireless Sensor Network Deployment

Cited by 19 publications

References 24 publications

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Avant-Grade framework Routing Optimization Based on Established Network Lifetime and Energy Management in WSN

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