The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
Ruchuan Wang - One of the best experts on this subject based on the ideXlab platform.
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a type of energy efficient Data Gathering Method based on single sink moving along fixed points
Peer-to-peer Networking and Applications, 2018Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:A type of Data Gathering Method based on one mobile Sink moving along the fixed traverse points (DGFP) is proposed in this paper. An optimal trajectory for the mobile Sink is built with the help of sensing and coverage models of the sensor node. Moreover, a sleep scheduling strategy is executed to further reduce energy consumption on idle listening. Sensors could go into light sleeping or deep sleeping mode when the Sink is far away from their communication ranges. Simulation results show that, DGFP could not only enhance network coverage, but also balance energy consumption compared with VNP, RP-UG and MobiCluster Methods.
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an energy efficient Data Gathering Method based on compressive sensing for pervasive sensor networks
Pervasive and Mobile Computing, 2017Co-Authors: Lijuan Sun, Fu Xiao, Ruchuan WangAbstract:Abstract This paper proposes an energy-efficient Data Gathering Method called CN-MSTP (Combining Minimum Spanning Tree with Interest Nodes) for pervasive wireless sensor networks, basing on Compressive sensing (CS) and Data aggregation. The proposed CN-MSTP protocol selects different nodes at random as projection nodes, and sets each projection node as a root to construct a minimum spanning tree by combining with interest nodes. Projection node aggregates sensor reading from sensor nodes using compressive sensing. We extend our Method by letting the sink node participate in the process of building a minimum tree and introduce eCN-MSTP. We compare our Methods with the other Methods. Simulation results indicate that our two Methods outperform the other Methods in overall energy consumption saving and load balance and hence prolong the lifetime of the network.
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a type of low latency Data Gathering Method with multi sink for sensor networks
Sensors, 2016Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:To balance energy consumption and reduce latency on Data transmission in Wireless Sensor Networks (WSNs), a type of low-latency Data Gathering Method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less Data Gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on Data collection is higher than one Sink based Data Gathering Methods.
Fu Xiao - One of the best experts on this subject based on the ideXlab platform.
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an energy efficient Data Gathering Method based on compressive sensing for pervasive sensor networks
Pervasive and Mobile Computing, 2017Co-Authors: Lijuan Sun, Fu Xiao, Ruchuan WangAbstract:Abstract This paper proposes an energy-efficient Data Gathering Method called CN-MSTP (Combining Minimum Spanning Tree with Interest Nodes) for pervasive wireless sensor networks, basing on Compressive sensing (CS) and Data aggregation. The proposed CN-MSTP protocol selects different nodes at random as projection nodes, and sets each projection node as a root to construct a minimum spanning tree by combining with interest nodes. Projection node aggregates sensor reading from sensor nodes using compressive sensing. We extend our Method by letting the sink node participate in the process of building a minimum tree and introduce eCN-MSTP. We compare our Methods with the other Methods. Simulation results indicate that our two Methods outperform the other Methods in overall energy consumption saving and load balance and hence prolong the lifetime of the network.
Mengye Qiang - One of the best experts on this subject based on the ideXlab platform.
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a type of energy efficient Data Gathering Method based on single sink moving along fixed points
Peer-to-peer Networking and Applications, 2018Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:A type of Data Gathering Method based on one mobile Sink moving along the fixed traverse points (DGFP) is proposed in this paper. An optimal trajectory for the mobile Sink is built with the help of sensing and coverage models of the sensor node. Moreover, a sleep scheduling strategy is executed to further reduce energy consumption on idle listening. Sensors could go into light sleeping or deep sleeping mode when the Sink is far away from their communication ranges. Simulation results show that, DGFP could not only enhance network coverage, but also balance energy consumption compared with VNP, RP-UG and MobiCluster Methods.
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a type of low latency Data Gathering Method with multi sink for sensor networks
Sensors, 2016Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:To balance energy consumption and reduce latency on Data transmission in Wireless Sensor Networks (WSNs), a type of low-latency Data Gathering Method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less Data Gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on Data collection is higher than one Sink based Data Gathering Methods.
Chao Sha - One of the best experts on this subject based on the ideXlab platform.
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a type of energy efficient Data Gathering Method based on single sink moving along fixed points
Peer-to-peer Networking and Applications, 2018Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:A type of Data Gathering Method based on one mobile Sink moving along the fixed traverse points (DGFP) is proposed in this paper. An optimal trajectory for the mobile Sink is built with the help of sensing and coverage models of the sensor node. Moreover, a sleep scheduling strategy is executed to further reduce energy consumption on idle listening. Sensors could go into light sleeping or deep sleeping mode when the Sink is far away from their communication ranges. Simulation results show that, DGFP could not only enhance network coverage, but also balance energy consumption compared with VNP, RP-UG and MobiCluster Methods.
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a type of low latency Data Gathering Method with multi sink for sensor networks
Sensors, 2016Co-Authors: Chao Sha, Jianmei Qiu, Mengye Qiang, Ruchuan WangAbstract:To balance energy consumption and reduce latency on Data transmission in Wireless Sensor Networks (WSNs), a type of low-latency Data Gathering Method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less Data Gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on Data collection is higher than one Sink based Data Gathering Methods.
Shojiro Nishio - One of the best experts on this subject based on the ideXlab platform.
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Data aggregation and forwarding route control for efficient Data Gathering in dense mobile wireless sensor networks
2015Co-Authors: Kazuya Matsuo, Keisuke Goto, Akimitsu Kanzaki, Takahiro Hara, Shojiro NishioAbstract:This chapter presents a Data Gathering Method considering geographical distribution of Data values for reducing traffic in dense mobile wireless sensor networks. First, we present our previous Method (DGUMA) which is a Data Gathering Method that efficiently gathers sensor Data using mobile agents in dense mobile wireless sensor networks. Second, we introduce an extended Method of DGUMA, named DGUMA/DA (DGUMA with Data Aggregation), that exploits geographical distribution of Data values in order to further reduce traffic. Finally, we analyze DGUMA/DA and confirm the effectiveness of the Method through some simulation experiments.
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Data Gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks
The Missouri Review, 2013Co-Authors: Keisuke Goto, Takahiro Hara, Yuya Sasaki, Shojiro NishioAbstract:Recently, there has been increasing interest in Mobile Wireless Sensor Networks MWSNs that are constructed by mobile sensor nodes held by ordinary people, and it has led to a new concept called urban sensing. In such MWSNs, mobile sensor nodes densely exist, and thus, there are basically many sensor nodes that can sense a geographical point in the entire sensing area. To reduce the communication cost for Gathering sensor Data, it is desirable to gather the sensor Data from the minimum number of mobile sensor nodes which are necessary to guarantee the sensing coverage or the quality of services. In this paper, to achieve this, we propose a Data Gathering Method using mobile agents in dense MWSNs. The proposed Method guarantees the sensing coverage of the entire area using mobile agents that autonomously perform sensing operations, transmit sensor Data, and move between sensor nodes. By Gathering only sensor Data generated by sensor nodes where mobile agents are running, our proposed Method can achieve efficient Gathering of sensor Data.
