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 i...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.展开更多
In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioni...In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioning over the past several years.However,due to weak signals,multipath or non-line-of-sight signal propagation,accurately and efficiently localizing targets in harsh indoor environments re-mains a challenging problem.To improve the perfor-mance in harsh environment with insufficient anchors,cooperative localization has emerged.In this paper,a novel cooperative localization algorithm,named area optimization and node selection based sum-product al-gorithm over a wireless network(AN-SPAWN),is de-scribed and analyzed.To alleviate the high compu-tational complexity and build optimized cooperative cluster,a node selection method is designed for the cooperative localization algorithm.Numerical experi-ment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algo-rithms in the harsh indoor environments.展开更多
Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of ...Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node’s self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node’s self-influence;in the local neighborhood,the node’s one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node’s local influence;the topological position of the node in the network and the shortest path between nodes are considered to measure the node’s global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible–infected–recovered(SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.展开更多
Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accele...Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.展开更多
Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In th...Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.展开更多
In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most...In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.展开更多
Objective: To assess the inhibitory effects of local injection of liposomal adriamycin (LADR) on the proliferation of lymph node metastases in rabbits bearing VX2 carcinoma in the mammary gland. Methods:Thirty female ...Objective: To assess the inhibitory effects of local injection of liposomal adriamycin (LADR) on the proliferation of lymph node metastases in rabbits bearing VX2 carcinoma in the mammary gland. Methods:Thirty female New Zealand white rabbits were divided into 3 groups, with 10 in each. VX2 tumor mass suspensions were injected into the breast tissues of rabbits. Treatment initiated once the axillary lymph node reached 5 mm in the maximum diameter. Group 1 received a sham treatment. Group 2 received a subcutaneous injection of LADR adjacent to tumor. Group 3 received an intravenous injection of free ADR (FADR) at the same dose and concentration to group 2. The breast tumors and axillary lymph nodes were resected after the treatment was repeated 3 times. The tumor and node sizes before and after treatment were measured. PCNA mRNA expressions in breast tumors and axillary nodes were determined using RT-PCR. Results: The mean growth ratios of lymph nodes after treatment were 3. 70, 1. 55, and 2. 89,respectively, in groups 1,2, and 3. The slowest node growth was observed in animals of group 2, with significant differences from group 1 (P<0. 001) and group 3 (P = 0. 002). The relative values of PCNA mRNA expression in lymph nodes were 0. 541, 0. 329,and 0. 450, respectively, in groups 1,2, and 3. Group 2 exhibited a significantly reduced PCNA mRNA expression in metastatic lymph node, as compared to group 1 (P<0. 001) and group 3 (P = 0. 004). Intravenous FADR injection effectively lowered the mRNA expressions of PCNA in breast tumors, which were not apparently altered after local LADR injection. Conclusion: Local injection of LADR holds a strong inhibitory effect on the proliferation of metastatic tumor cells in lymph nodes and appears to be an effective method for the treatment of lymphatic metastases of breast cancer.展开更多
Determining the application and version of nodes in the Internet of Things （IoT） is very important for warning about and managing vulnerabilities in the IoT. This article defines the attributes for determining the a...Determining the application and version of nodes in the Internet of Things （IoT） is very important for warning about and managing vulnerabilities in the IoT. This article defines the attributes for determining the application and version of nodes in the roT. By improving the structure of the Internet web crawler, which obtains raw data from nodes, we can obtain data from nodes in the IoT. We improve on the existing strategy, in which only determinations are stored, by also storing downloaded raw data locally in MongoDB. This stored raw data can be conveniently used to determine application type and node version when a new determination method emerges or when there is a new application type or node version. In such instances, the crawler does not have to scan the Internet again. We show through experimentation that our crawler can crawl the loT and obtain data necessary for determining the application type and node version.展开更多
A distibuted 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 a...A distibuted 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 [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.展开更多
为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键...为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键节点.首先,借助节点间的最短路径长度和数量,定义节点间的通信概率序列.其次,通过结合通信概率和相对熵(Communication Probability and Relative Entropy,CPR),将传统的二元邻接矩阵转化为网络归一化相关矩阵.再次,结合加权聚类系数和邻居节点的影响(Weighted Clustering Coefficients and Neighbor Influence,WCCN),得到改进的考虑邻居影响的局部加权密度.最后,为验证CPRWCCN算法的效果,在故意攻击和随机攻击下进行模拟实验,利用传播模型在4种实际网络上对CPR-WCCN与其他5种算法进行对比分析.实验结果表明:当网络遭受故意攻击,导致前15个关键节点失效时,网络的连通性、效率、最大连接子图以及自然连通性等关键指标较随机攻击出现了更显著的下降;相较于其他5种算法,CPR-WCCN算法表现出最优的整体性能,能够准确且高效地识别出网络中的关键节点.展开更多
提出了一种MCBN(Monte Carlo loca liza tion boxed using non-anchor)定位算法。该算法建立在蒙特卡罗定位算法基础之上,利用两跳范围内可信任度权值最小且坐标确定的静态非锚节点,辅助网络中两跳范围内的锚节点构建最小锚盒,同时利用...提出了一种MCBN(Monte Carlo loca liza tion boxed using non-anchor)定位算法。该算法建立在蒙特卡罗定位算法基础之上,利用两跳范围内可信任度权值最小且坐标确定的静态非锚节点,辅助网络中两跳范围内的锚节点构建最小锚盒,同时利用待定位节点上一时刻的位置信息和临时锚节点的特性增强样本过滤条件,进行快速抽样和样本过滤。仿真结果表明:MCBN同MCL和MCB算法相比,提高了节点定位精度,降低了节点能量损耗。展开更多
文摘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.
基金the Nation-alKey Research&Development Program of China un-der Grant No.2020YFC1511702 and Open Fund of IPOC(BUPT)No.IPOC2021ZT20.
文摘In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioning over the past several years.However,due to weak signals,multipath or non-line-of-sight signal propagation,accurately and efficiently localizing targets in harsh indoor environments re-mains a challenging problem.To improve the perfor-mance in harsh environment with insufficient anchors,cooperative localization has emerged.In this paper,a novel cooperative localization algorithm,named area optimization and node selection based sum-product al-gorithm over a wireless network(AN-SPAWN),is de-scribed and analyzed.To alleviate the high compu-tational complexity and build optimized cooperative cluster,a node selection method is designed for the cooperative localization algorithm.Numerical experi-ment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algo-rithms in the harsh indoor environments.
基金Project supported by the Natural Science Basic Research Program of Shaanxi Province of China (Grant No. 2022JQ675)the Youth Innovation Team of Shaanxi Universities。
文摘Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node’s self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node’s self-influence;in the local neighborhood,the node’s one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node’s local influence;the topological position of the node in the network and the shortest path between nodes are considered to measure the node’s global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible–infected–recovered(SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.
基金Project supported by the National Natural Foundation of China(Grant No.11871328)the Shanghai Science and Technology Development Funds Soft Science Research Project(Grant No.21692109800).
文摘Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.
文摘Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.
基金supported in part by the Key Program of National Natural Science Foundation of China(Grant No.60873244,60973110,61003307)the Beijing Municipal Natural Science Foundation(Grant No.4102059)
文摘In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.
基金Supported by the grants provided by the National Natural Science Foundation of China (No. 30600597)Natural Science Foundation of Shaanxi Province [No. 2005K09-G10(4)Science Technology Development Foundation of Xi'an (No. GG06167)
文摘Objective: To assess the inhibitory effects of local injection of liposomal adriamycin (LADR) on the proliferation of lymph node metastases in rabbits bearing VX2 carcinoma in the mammary gland. Methods:Thirty female New Zealand white rabbits were divided into 3 groups, with 10 in each. VX2 tumor mass suspensions were injected into the breast tissues of rabbits. Treatment initiated once the axillary lymph node reached 5 mm in the maximum diameter. Group 1 received a sham treatment. Group 2 received a subcutaneous injection of LADR adjacent to tumor. Group 3 received an intravenous injection of free ADR (FADR) at the same dose and concentration to group 2. The breast tumors and axillary lymph nodes were resected after the treatment was repeated 3 times. The tumor and node sizes before and after treatment were measured. PCNA mRNA expressions in breast tumors and axillary nodes were determined using RT-PCR. Results: The mean growth ratios of lymph nodes after treatment were 3. 70, 1. 55, and 2. 89,respectively, in groups 1,2, and 3. The slowest node growth was observed in animals of group 2, with significant differences from group 1 (P<0. 001) and group 3 (P = 0. 002). The relative values of PCNA mRNA expression in lymph nodes were 0. 541, 0. 329,and 0. 450, respectively, in groups 1,2, and 3. Group 2 exhibited a significantly reduced PCNA mRNA expression in metastatic lymph node, as compared to group 1 (P<0. 001) and group 3 (P = 0. 004). Intravenous FADR injection effectively lowered the mRNA expressions of PCNA in breast tumors, which were not apparently altered after local LADR injection. Conclusion: Local injection of LADR holds a strong inhibitory effect on the proliferation of metastatic tumor cells in lymph nodes and appears to be an effective method for the treatment of lymphatic metastases of breast cancer.
基金supported by the ZTE Corporation and University Joint Research Project under Grant No.CON1307100001the National High Technology Research and Development Program of China under Grant No.2013AA013602
文摘Determining the application and version of nodes in the Internet of Things （IoT） is very important for warning about and managing vulnerabilities in the IoT. This article defines the attributes for determining the application and version of nodes in the roT. By improving the structure of the Internet web crawler, which obtains raw data from nodes, we can obtain data from nodes in the IoT. We improve on the existing strategy, in which only determinations are stored, by also storing downloaded raw data locally in MongoDB. This stored raw data can be conveniently used to determine application type and node version when a new determination method emerges or when there is a new application type or node version. In such instances, the crawler does not have to scan the Internet again. We show through experimentation that our crawler can crawl the loT and obtain data necessary for determining the application type and node version.
文摘A distibuted 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 [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.
文摘为解决许多关键节点识别算法在评估网络节点重要性时,忽视节点与其邻居节点间的相互关系,导致对网络鲁棒性和脆弱性的评估结果不准确的问题,提出一种改良的局部加权密度度量方式CPR-WCCN,旨在以较低的计算成本准确识别复杂网络中的关键节点.首先,借助节点间的最短路径长度和数量,定义节点间的通信概率序列.其次,通过结合通信概率和相对熵(Communication Probability and Relative Entropy,CPR),将传统的二元邻接矩阵转化为网络归一化相关矩阵.再次,结合加权聚类系数和邻居节点的影响(Weighted Clustering Coefficients and Neighbor Influence,WCCN),得到改进的考虑邻居影响的局部加权密度.最后,为验证CPRWCCN算法的效果,在故意攻击和随机攻击下进行模拟实验,利用传播模型在4种实际网络上对CPR-WCCN与其他5种算法进行对比分析.实验结果表明:当网络遭受故意攻击,导致前15个关键节点失效时,网络的连通性、效率、最大连接子图以及自然连通性等关键指标较随机攻击出现了更显著的下降;相较于其他5种算法,CPR-WCCN算法表现出最优的整体性能,能够准确且高效地识别出网络中的关键节点.
文摘提出了一种MCBN(Monte Carlo loca liza tion boxed using non-anchor)定位算法。该算法建立在蒙特卡罗定位算法基础之上,利用两跳范围内可信任度权值最小且坐标确定的静态非锚节点,辅助网络中两跳范围内的锚节点构建最小锚盒,同时利用待定位节点上一时刻的位置信息和临时锚节点的特性增强样本过滤条件,进行快速抽样和样本过滤。仿真结果表明:MCBN同MCL和MCB算法相比,提高了节点定位精度,降低了节点能量损耗。
