Jian shu - Academia.edu (original) (raw)

Papers by Jian shu

Frontiers in physics, Mar 15, 2024

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused o... more Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages metapaths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

IEEE Access

Link prediction is a hot issue in the research of network evolution. The existing methods employ ... more Link prediction is a hot issue in the research of network evolution. The existing methods employ a stacked structure, which feeds captured topology information into a time series model. However, the structure introduces network noise that affects the accurate extraction of temporal features and reduces the prediction accuracy. Motivated by feature fusion methods in the Computer Vision (CV), we introduce a link prediction model based on attentional feature fusion (AFF-LP), which automatically extracts network features through Deep Learning. The proposed model leverages the self-attention mechanism to extract the topological and temporal features respectively. Moreover, the network features are fused based on a graph-level representation. With the help of a vector mapping model, the feature vectors are mapped to the future network topology. Three real opportunistic network datasets, ITC, MIT and Infocom06, are used for experiments. The experimental results show that the proposed model is more accurate and stable compared to other baseline methods.

IEEE Access, 2018

The target of link prediction is used to estimate the possibility of future links among nodes thr... more The target of link prediction is used to estimate the possibility of future links among nodes through known network structures and nodes information. According to the time-varying characteristics of the opportunistic network, the historical information of node pairs has a significant influence on the future connection state. We propose a novel link prediction approach which is based on the recurrent neural network link prediction (RNN-LP) framework. With the help of time series method, we define the vector that is made up of the node information and historical connection information of the node pairs, in which a sequence vector is constructed. Benefiting from RNN in sequence modeling, the time domain characteristics were extracted in the process of the dynamic evolution of the opportunistic network. Hence, the future link prediction becomes significantly better. By utilizing iMote traces Cambridge and MIT reality datasets, experimental results are obtained to reveal that RNN-LP method gives better accuracy and stability than the prediction techniques of the common neighbor, Adamic-Adar, resource allocation, local path, and Katz.

International Journal of Distributed Sensor Networks, 2017

Link prediction for opportunistic sensor network has been attracting more and more attention. How... more Link prediction for opportunistic sensor network has been attracting more and more attention. However, the inherent dynamic nature of opportunistic sensor network makes it a challenging issue to ensure quality of service in opportunistic sensor network. In this article, a novel deep learning framework is proposed to predict links for opportunistic sensor network. The framework stacks the conditional restricted Boltzmann machine which models time series by appending connections from the past time steps. A similarity index based on time parameters is proposed to describe similarities between nodes. Through tuning learning rate layer-adaptively, reconstruction error of restricted Boltzmann machine goes stable rapidly so that the convergence time is shortened. The framework is verified by real data from INFOCOM set and MIT set. The results show that the framework can predict links of opportunistic sensor network effectively.

IEEE Access, 2019

The goal of link prediction is to estimate the possibility of future links among nodes using know... more The goal of link prediction is to estimate the possibility of future links among nodes using known network information and the attributes of the nodes. According to the time-varying characteristics and the node's mobility of opportunistic networks, this paper proposes a novel link prediction method based on the Bayesian recurrent neural network (BRNN-LP) framework. The time series data of a dynamic opportunistic networks is sliced into snapshots in which there exist the correlation information and spatial location information. A vector of a snapshot is constructed based on such information, which represents the link information. Then, the vectors of multiple network snapshots constitute a spatiotemporal vector sequence. Benefiting from the BRNN's ability of extracting the features of time series data, the correlation between spatiotemporal vector sequence and node connection states is learned, and the law of the link evolution is captured to predict future links. The results on the MIT Reality dataset show that compared with methods such as the similarity-based indices, the support vector classifier, linear discriminant analysis and recurrent neural network, the proposed prediction method is more accurate and stable. INDEX TERMS link prediction; opportunistic networks; Bayesian recurrent neural network

Journal of Sensors, 2017

Wireless networks have experienced explosive growth during the last few years. Nowadays, there ar... more Wireless networks have experienced explosive growth during the last few years. Nowadays, there are a large variety of networks spanning from the well-known cellular networks to noninfrastructure wireless networks such as mobile ad hoc networks and sensor networks. Communication security is essential to the success of wireless sensor network applications, especially for those mission-critical applications working in unattended and even hostile environments. However, providing satisfactory security protection in wireless sensor networks has ever been a challenging task due to various network and resource constraints and malicious attacks. In this special issue, we concentrate mainly on security and privacy as well as the emerging applications of wireless sensor network. It aims to bring together researchers and practitioners from wireless and sensor networking, security, cryptography, and distributed computing communities, with the goal of promoting discussions and collaborations. We are interested in novel research on all aspects of security in wireless sensor networks and tradeoff between security and performance such as QoS, dependability, and scalability. The special issue covers industrial issues/applications and academic research into security and privacy for wireless sensor networks. This special issue includes a collection of 25 papers selected from 97 submissions to 21 countries or districts

IEEE Access, 2021

The link quality of wireless sensor networks is the basis for selecting communication links in ro... more The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols. Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability. The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient. According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality. The link quality grade is taken as a metric of the link quality estimation. Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor. A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed. In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM. Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean. INDEX TERMS Wireless sensor networks, link quality estimation, weighted extreme learning machine, particle swarm optimization algorithm.

Computer Science and Information Systems, 2021

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation ... more Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the propose...

Computer Science and Information Systems, 2017

Connectivity is an important indicator of network performance. But the opportunistic sensor netwo... more Connectivity is an important indicator of network performance. But the opportunistic sensor networks (OSNs) have temporal evolution characteristics, which are hard to modelled with traditional graphs. After analyzing the characteristics of OSNs, this paper constructs OSNs connectivity model based on time graph theory. The overall connectivity degree of the network is defined, and is used to estimate actual network connectivity. We also propose a computing method that uses the adjacency matrix of each snapshot. The simulation results show that network connectivity degree can reflect the overall connectivity of OSNs, which provide a basis for improving the OSNs performance.

Computer Science and Information Systems, 2015

Connectivity is one of the most important parameters in network monitoring. The connectivity mode... more Connectivity is one of the most important parameters in network monitoring. The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be established by traditional graph models due to the fact that its connectivity is timing correlative and evolutionary, which makes it extremely difficult to monitor an OSN. In order to solve the monitoring problem, this paper builds an evolving graph model based on the theory of evolving graph as a description of an OSN. It defines a series of parameters to measure the connectivity of the OSN and establishes an monitoring model. Meanwhile, this paper gives the key algorithms in building the model, the Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ) algorithm. The rationality of the monitoring model has been proven by a prototype system and the simulation results. Extensive simulation results show that the proposed connectivity monitoring model can indicate real circumstances of OSN? connectivity, and it is appli...

Journal of Networks, 2009

There are many RSSI-based localization algorithms for wireless sensor networks (WSN). This paper ... more There are many RSSI-based localization algorithms for wireless sensor networks (WSN). This paper proposes a RSSI-based refinement of the optimization mechanism (BR 2 OM) based on multi-dimensional scaling (MDS). The basic idea of the algorithm is that establishing sub-areas for localization to ensure the processing of localization can be completed successfully. The sub-area is divided by cluster which is a tree structure. At the same time, the mechanism filters the Received Signal Strength Indicator (RSSI) based on the link quality indication (LQI) to reduce the error number of RSSI. The RSSI is corrected and refined by using cosine rule and the limitation of communication range. The relationship of relative position among blind nodes is obtained by adopting classical MDS algorithm. The relative position is finally converted into the global coordinate. The experiment result shows that the algorithm has many advantages, such as high accuracy, simple condition, and moderate cost. The algorithm satisfies the requirement of large-scale network applications and also is valuable to the positioning refined problems under circumstance of the non-line-of-sight.

Journal of Networks, 2012

In wireless sensor networks, location information is essential to its monitoring activities. In v... more In wireless sensor networks, location information is essential to its monitoring activities. In view of the "boundary effect" and lack of accuracy in APIT-3D, this paper proposes an improved three-dimensional localization scheme based on APIT named IAPIT-3D. In the scheme, the probability of misjudgment is reduced by adding the conditions of judgment. On one hand, the signal strength that all of the neighbor nodes receive should be compared with the one of the unknown node. On the other hand, two variables are set for comparison to determine the position of the node relative to the triangular pyramid. Furthermore, narrowing down the targeted area through "Perpendicular Median Surface Cutting" reduces the localization error. The results of simulation show that compared with APIT-3D, IAPIT-3D can improve the accuracy of localization.

Journal of Computers, 2010

Data fusion is one of the key techniques in Wireless sensor network (WSN). Data fusion can reduce... more Data fusion is one of the key techniques in Wireless sensor network (WSN). Data fusion can reduce quantity of data transmission in the network, extend network lifetime by reducing energy consumption, and improve efficiency of bandwidth utilization. At present, researches on data fusion in WSN mainly fall into the following aspects: aggregation tree construction and data processing. This paper proposes an aggregation tree construction method based on grid named ATCBG which makes some improvements over GROUP. The results of simulation experiment show that the average energy consumption of ATCBG is evidently lower than GROUP, and the lifetime of the network is much longer than GROUP before the emergence of node death.

Journal of Computers, 2009

In wireless sensor networks, three-dimensional localization is important for applications. It bec... more In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.

IEEE Access, 2019

Link quality is an important factor for nodes selecting communication links in wireless sensor ne... more Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks. INDEX TERMS Wireless sensor networks, link quality prediction, XGBoost, improved fuzzy C-means algorithm.

ArXiv, 2020

It is greatly significant in evaluating nodes Influence ranking in complex networks. Over the yea... more It is greatly significant in evaluating nodes Influence ranking in complex networks. Over the years, many researchers present different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighborhood information to define the strength of a node, which is defined as the summation of Grueblers Equation of nodes one-hop triangle neighborhood to the number of all the edges in the subgraph. Furthermore, we socially consider it as the local trust of a node. To verify the validity of Tr-centrality, we apply it to four real-world networks with different densities and shapes and Tr-centrality has proven to yield better results.

Computer Science and Information Systems, 2013

In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and... more In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and the stability is decreasing in wireless sensor networks, a novel algorithm is proposed based on consistency test and sliding-windowed variance weighted. The internal noise is considered to be the main factor of the problem in this paper. And we can use consistency test method to diagnose whether the mean of sensor data is offset. So the abnormal data is amended or removed. Then, the result of fused data can be calculated by using sliding window variance weighted algorithm according to normal and amended data. Simulation results show that the misdiagnosis rate of the abnormal data can be reduced to 3% by using improved consistency test with the threshold set to [0.05, 0.15], so the abnormal sensor data can be diagnosed more accurately and the stability can be increased. The accuracy of the fused data can be improved effectively when the window length is set to 2. Under the condition that...

2006 Fifth International Conference on Grid and Cooperative Computing Workshops, 2006

Wireless sensor networks have been proposed for many location based applications, so the localiza... more Wireless sensor networks have been proposed for many location based applications, so the localization problem in wireless sensor networks has got considerable attentions these days. More and more researchers pay attention to how to use the location information to implement specific application and how to locate nodes accurately. In this paper, DV-hop localization algorithm is introduced, and the influence of beacon nodes on localization average error of DVhop are explored. The paper focuses on how the placement and quantity of beacon nodes affect on localization average error. And simulations show the results.

2009 Second International Symposium on Electronic Commerce and Security, 2009

Journal of Software, 2010

This paper proposes a hybrid scheduling approach for real-time system on homogeneous multi-core a... more This paper proposes a hybrid scheduling approach for real-time system on homogeneous multi-core architecture. To make the best of the available parallelism in these systems, first an application is partitioned into some parallel tasks as much as possible. Then the parallel tasks are dispatched to different cores, so as to execute in parallel. In each core, real-time tasks can run concurrently with nonreal-time tasks. The hybrid scheduling approach uses a twolevel scheduling scheme. At the top level, a sporadic server is assigned to each scheduling policy. Each sporadic server is used to schedule the dispatched tasks according to its scheduling policy. At the bottom level, a rate-monotonic OS scheduler is adopted to maintain and schedule the top level sporadic servers. The schedulability test is also considered in this paper. The experimental results show that the hybrid scheme is an efficient scheduling scheme.

Frontiers in physics, Mar 15, 2024

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused o... more Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages metapaths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

IEEE Access

Link prediction is a hot issue in the research of network evolution. The existing methods employ ... more Link prediction is a hot issue in the research of network evolution. The existing methods employ a stacked structure, which feeds captured topology information into a time series model. However, the structure introduces network noise that affects the accurate extraction of temporal features and reduces the prediction accuracy. Motivated by feature fusion methods in the Computer Vision (CV), we introduce a link prediction model based on attentional feature fusion (AFF-LP), which automatically extracts network features through Deep Learning. The proposed model leverages the self-attention mechanism to extract the topological and temporal features respectively. Moreover, the network features are fused based on a graph-level representation. With the help of a vector mapping model, the feature vectors are mapped to the future network topology. Three real opportunistic network datasets, ITC, MIT and Infocom06, are used for experiments. The experimental results show that the proposed model is more accurate and stable compared to other baseline methods.

IEEE Access, 2018

The target of link prediction is used to estimate the possibility of future links among nodes thr... more The target of link prediction is used to estimate the possibility of future links among nodes through known network structures and nodes information. According to the time-varying characteristics of the opportunistic network, the historical information of node pairs has a significant influence on the future connection state. We propose a novel link prediction approach which is based on the recurrent neural network link prediction (RNN-LP) framework. With the help of time series method, we define the vector that is made up of the node information and historical connection information of the node pairs, in which a sequence vector is constructed. Benefiting from RNN in sequence modeling, the time domain characteristics were extracted in the process of the dynamic evolution of the opportunistic network. Hence, the future link prediction becomes significantly better. By utilizing iMote traces Cambridge and MIT reality datasets, experimental results are obtained to reveal that RNN-LP method gives better accuracy and stability than the prediction techniques of the common neighbor, Adamic-Adar, resource allocation, local path, and Katz.

International Journal of Distributed Sensor Networks, 2017

Link prediction for opportunistic sensor network has been attracting more and more attention. How... more Link prediction for opportunistic sensor network has been attracting more and more attention. However, the inherent dynamic nature of opportunistic sensor network makes it a challenging issue to ensure quality of service in opportunistic sensor network. In this article, a novel deep learning framework is proposed to predict links for opportunistic sensor network. The framework stacks the conditional restricted Boltzmann machine which models time series by appending connections from the past time steps. A similarity index based on time parameters is proposed to describe similarities between nodes. Through tuning learning rate layer-adaptively, reconstruction error of restricted Boltzmann machine goes stable rapidly so that the convergence time is shortened. The framework is verified by real data from INFOCOM set and MIT set. The results show that the framework can predict links of opportunistic sensor network effectively.

IEEE Access, 2019

The goal of link prediction is to estimate the possibility of future links among nodes using know... more The goal of link prediction is to estimate the possibility of future links among nodes using known network information and the attributes of the nodes. According to the time-varying characteristics and the node's mobility of opportunistic networks, this paper proposes a novel link prediction method based on the Bayesian recurrent neural network (BRNN-LP) framework. The time series data of a dynamic opportunistic networks is sliced into snapshots in which there exist the correlation information and spatial location information. A vector of a snapshot is constructed based on such information, which represents the link information. Then, the vectors of multiple network snapshots constitute a spatiotemporal vector sequence. Benefiting from the BRNN's ability of extracting the features of time series data, the correlation between spatiotemporal vector sequence and node connection states is learned, and the law of the link evolution is captured to predict future links. The results on the MIT Reality dataset show that compared with methods such as the similarity-based indices, the support vector classifier, linear discriminant analysis and recurrent neural network, the proposed prediction method is more accurate and stable. INDEX TERMS link prediction; opportunistic networks; Bayesian recurrent neural network

Journal of Sensors, 2017

Wireless networks have experienced explosive growth during the last few years. Nowadays, there ar... more Wireless networks have experienced explosive growth during the last few years. Nowadays, there are a large variety of networks spanning from the well-known cellular networks to noninfrastructure wireless networks such as mobile ad hoc networks and sensor networks. Communication security is essential to the success of wireless sensor network applications, especially for those mission-critical applications working in unattended and even hostile environments. However, providing satisfactory security protection in wireless sensor networks has ever been a challenging task due to various network and resource constraints and malicious attacks. In this special issue, we concentrate mainly on security and privacy as well as the emerging applications of wireless sensor network. It aims to bring together researchers and practitioners from wireless and sensor networking, security, cryptography, and distributed computing communities, with the goal of promoting discussions and collaborations. We are interested in novel research on all aspects of security in wireless sensor networks and tradeoff between security and performance such as QoS, dependability, and scalability. The special issue covers industrial issues/applications and academic research into security and privacy for wireless sensor networks. This special issue includes a collection of 25 papers selected from 97 submissions to 21 countries or districts

IEEE Access, 2021

The link quality of wireless sensor networks is the basis for selecting communication links in ro... more The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols. Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability. The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient. According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality. The link quality grade is taken as a metric of the link quality estimation. Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor. A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed. In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM. Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean. INDEX TERMS Wireless sensor networks, link quality estimation, weighted extreme learning machine, particle swarm optimization algorithm.

Computer Science and Information Systems, 2021

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation ... more Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the propose...

Computer Science and Information Systems, 2017

Connectivity is an important indicator of network performance. But the opportunistic sensor netwo... more Connectivity is an important indicator of network performance. But the opportunistic sensor networks (OSNs) have temporal evolution characteristics, which are hard to modelled with traditional graphs. After analyzing the characteristics of OSNs, this paper constructs OSNs connectivity model based on time graph theory. The overall connectivity degree of the network is defined, and is used to estimate actual network connectivity. We also propose a computing method that uses the adjacency matrix of each snapshot. The simulation results show that network connectivity degree can reflect the overall connectivity of OSNs, which provide a basis for improving the OSNs performance.

Computer Science and Information Systems, 2015

Connectivity is one of the most important parameters in network monitoring. The connectivity mode... more Connectivity is one of the most important parameters in network monitoring. The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be established by traditional graph models due to the fact that its connectivity is timing correlative and evolutionary, which makes it extremely difficult to monitor an OSN. In order to solve the monitoring problem, this paper builds an evolving graph model based on the theory of evolving graph as a description of an OSN. It defines a series of parameters to measure the connectivity of the OSN and establishes an monitoring model. Meanwhile, this paper gives the key algorithms in building the model, the Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ) algorithm. The rationality of the monitoring model has been proven by a prototype system and the simulation results. Extensive simulation results show that the proposed connectivity monitoring model can indicate real circumstances of OSN? connectivity, and it is appli...

Journal of Networks, 2009

There are many RSSI-based localization algorithms for wireless sensor networks (WSN). This paper ... more There are many RSSI-based localization algorithms for wireless sensor networks (WSN). This paper proposes a RSSI-based refinement of the optimization mechanism (BR 2 OM) based on multi-dimensional scaling (MDS). The basic idea of the algorithm is that establishing sub-areas for localization to ensure the processing of localization can be completed successfully. The sub-area is divided by cluster which is a tree structure. At the same time, the mechanism filters the Received Signal Strength Indicator (RSSI) based on the link quality indication (LQI) to reduce the error number of RSSI. The RSSI is corrected and refined by using cosine rule and the limitation of communication range. The relationship of relative position among blind nodes is obtained by adopting classical MDS algorithm. The relative position is finally converted into the global coordinate. The experiment result shows that the algorithm has many advantages, such as high accuracy, simple condition, and moderate cost. The algorithm satisfies the requirement of large-scale network applications and also is valuable to the positioning refined problems under circumstance of the non-line-of-sight.

Journal of Networks, 2012

In wireless sensor networks, location information is essential to its monitoring activities. In v... more In wireless sensor networks, location information is essential to its monitoring activities. In view of the "boundary effect" and lack of accuracy in APIT-3D, this paper proposes an improved three-dimensional localization scheme based on APIT named IAPIT-3D. In the scheme, the probability of misjudgment is reduced by adding the conditions of judgment. On one hand, the signal strength that all of the neighbor nodes receive should be compared with the one of the unknown node. On the other hand, two variables are set for comparison to determine the position of the node relative to the triangular pyramid. Furthermore, narrowing down the targeted area through "Perpendicular Median Surface Cutting" reduces the localization error. The results of simulation show that compared with APIT-3D, IAPIT-3D can improve the accuracy of localization.

Journal of Computers, 2010

Data fusion is one of the key techniques in Wireless sensor network (WSN). Data fusion can reduce... more Data fusion is one of the key techniques in Wireless sensor network (WSN). Data fusion can reduce quantity of data transmission in the network, extend network lifetime by reducing energy consumption, and improve efficiency of bandwidth utilization. At present, researches on data fusion in WSN mainly fall into the following aspects: aggregation tree construction and data processing. This paper proposes an aggregation tree construction method based on grid named ATCBG which makes some improvements over GROUP. The results of simulation experiment show that the average energy consumption of ATCBG is evidently lower than GROUP, and the lifetime of the network is much longer than GROUP before the emergence of node death.

Journal of Computers, 2009

In wireless sensor networks, three-dimensional localization is important for applications. It bec... more In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.

IEEE Access, 2019

Link quality is an important factor for nodes selecting communication links in wireless sensor ne... more Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks. INDEX TERMS Wireless sensor networks, link quality prediction, XGBoost, improved fuzzy C-means algorithm.

ArXiv, 2020

It is greatly significant in evaluating nodes Influence ranking in complex networks. Over the yea... more It is greatly significant in evaluating nodes Influence ranking in complex networks. Over the years, many researchers present different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighborhood information to define the strength of a node, which is defined as the summation of Grueblers Equation of nodes one-hop triangle neighborhood to the number of all the edges in the subgraph. Furthermore, we socially consider it as the local trust of a node. To verify the validity of Tr-centrality, we apply it to four real-world networks with different densities and shapes and Tr-centrality has proven to yield better results.

Computer Science and Information Systems, 2013

In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and... more In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and the stability is decreasing in wireless sensor networks, a novel algorithm is proposed based on consistency test and sliding-windowed variance weighted. The internal noise is considered to be the main factor of the problem in this paper. And we can use consistency test method to diagnose whether the mean of sensor data is offset. So the abnormal data is amended or removed. Then, the result of fused data can be calculated by using sliding window variance weighted algorithm according to normal and amended data. Simulation results show that the misdiagnosis rate of the abnormal data can be reduced to 3% by using improved consistency test with the threshold set to [0.05, 0.15], so the abnormal sensor data can be diagnosed more accurately and the stability can be increased. The accuracy of the fused data can be improved effectively when the window length is set to 2. Under the condition that...

2006 Fifth International Conference on Grid and Cooperative Computing Workshops, 2006

Wireless sensor networks have been proposed for many location based applications, so the localiza... more Wireless sensor networks have been proposed for many location based applications, so the localization problem in wireless sensor networks has got considerable attentions these days. More and more researchers pay attention to how to use the location information to implement specific application and how to locate nodes accurately. In this paper, DV-hop localization algorithm is introduced, and the influence of beacon nodes on localization average error of DVhop are explored. The paper focuses on how the placement and quantity of beacon nodes affect on localization average error. And simulations show the results.

2009 Second International Symposium on Electronic Commerce and Security, 2009

Journal of Software, 2010

This paper proposes a hybrid scheduling approach for real-time system on homogeneous multi-core a... more This paper proposes a hybrid scheduling approach for real-time system on homogeneous multi-core architecture. To make the best of the available parallelism in these systems, first an application is partitioned into some parallel tasks as much as possible. Then the parallel tasks are dispatched to different cores, so as to execute in parallel. In each core, real-time tasks can run concurrently with nonreal-time tasks. The hybrid scheduling approach uses a twolevel scheduling scheme. At the top level, a sporadic server is assigned to each scheduling policy. Each sporadic server is used to schedule the dispatched tasks according to its scheduling policy. At the bottom level, a rate-monotonic OS scheduler is adopted to maintain and schedule the top level sporadic servers. The schedulability test is also considered in this paper. The experimental results show that the hybrid scheme is an efficient scheduling scheme.