The Experts below are selected from a list of 30225 Experts worldwide ranked by ideXlab platform

Jie Zhang - One of the best experts on this subject based on the ideXlab platform.

  • load balancing based multi controller coordinated Deployment Strategy in software defined optical networks
    Optical Fiber Technology, 2018
    Co-Authors: Yongli Zhao, Chuan Liu, Hua Wang, Qi Shao, Jie Zhang
    Abstract:

    Abstract In software defined optical networks (SDON), a single controller can hardly manage all the optical devices especially in the large-scale networks, while multiple controllers deployed in the network can solve it. Recently, more and more attention has been paid to the multi-controller architecture, and the Deployment of controllers is an important issue in large software defined optical networks. However, the existing architectures focus on static mapping of controllers and switches and do not consider load balance among different controllers. It is worth further studying the cooperation of multiple controllers. This paper presents a multi-controller coordinated Deployment Strategy (Switch Migration Based Controller Placement, SMBCP) based on load balancing. Simulation results show that SMBCP can significantly reduce the load difference among different controllers in SDON and reduce the migration cost of switches, thus improving the load balance of the controller.

  • PIMRC - A network Deployment Strategy for home area networks in smart grid
    2015 IEEE 26th Annual International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2015
    Co-Authors: Dehua Li, Jialai Weng, Jie Zhang
    Abstract:

    Reliability is one of the most concerned issues in wireless communications for smart grid, especially in the home area network(HAN) where the smart meter data are collected. In this paper, we model the HAN as a wireless mesh network that deploys cooperative transmissions for improving link reliability. We then define a new reliability performance metric that depends not only on the channel model but also on the locations of smart meters. Based on the reliability performance metric, we propose a smart meter Deployment Strategy to maximize the communication reliability. The performance of the proposed HAN Deployment Strategy is applied to a real home environment. The simulation results show that the proposed Deployment Strategy is effective in guaranteeing reliability for smart-grid communications.

  • a network Deployment Strategy for home area networks in smart grid
    Personal Indoor and Mobile Radio Communications, 2015
    Co-Authors: Dehua Li, Jialai Weng, Jie Zhang
    Abstract:

    Reliability is one of the most concerned issues in wireless communications for smart grid, especially in the home area network(HAN) where the smart meter data are collected. In this paper, we model the HAN as a wireless mesh network that deploys cooperative transmissions for improving link reliability. We then define a new reliability performance metric that depends not only on the channel model but also on the locations of smart meters. Based on the reliability performance metric, we propose a smart meter Deployment Strategy to maximize the communication reliability. The performance of the proposed HAN Deployment Strategy is applied to a real home environment. The simulation results show that the proposed Deployment Strategy is effective in guaranteeing reliability for smart-grid communications.

  • mobile small cell Deployment Strategy for hot spot in existing heterogeneous networks
    Global Communications Conference, 2015
    Co-Authors: E Nan, Xiaoli Chu, Jie Zhang
    Abstract:

    As the mobile data demand keeps growing, an existing heterogeneous network (HetNet) composed of macrocells and small cells may still face the problem of not being able to provide sufficient capacity for unexpected but reoccurring hot spots. In this paper, we propose a mobile small-cell Deployment Strategy that avoids replanning the overall network while fulfilling the hot spot demand by optimizing the Deployment of additional mobile small cells on top of the existing HetNet. We formulate the problem as a joint optimization over the number and locations of mobile small cells and the user associations of all cells in order to maximize the minimum user throughput. In order to solve it, we first propose a Fixed Number Deployment Algorithm (FNDA) to solve the problem with a fixed number of new small cells. Afterwards, we extend FNDA into a Deployment Over Existing Network Algorithm (DOENA) to solve the joint optimization problem. The simulation results show that DOENA offers a higher minimum user throughput while requiring less mobile small cells to be deployed than the Deployment optimization based on maximizing sum user throughput.

  • GLOBECOM Workshops - Mobile Small-Cell Deployment Strategy for Hot Spot in Existing Heterogeneous Networks
    2015 IEEE Globecom Workshops (GC Wkshps), 2015
    Co-Authors: Nan E, Xiaoli Chu, Jie Zhang
    Abstract:

    As the mobile data demand keeps growing, an existing heterogeneous network (HetNet) composed of macrocells and small cells may still face the problem of not being able to provide sufficient capacity for unexpected but reoccurring hot spots. In this paper, we propose a mobile small-cell Deployment Strategy that avoids replanning the overall network while fulfilling the hot spot demand by optimizing the Deployment of additional mobile small cells on top of the existing HetNet. We formulate the problem as a joint optimization over the number and locations of mobile small cells and the user associations of all cells in order to maximize the minimum user throughput. In order to solve it, we first propose a Fixed Number Deployment Algorithm (FNDA) to solve the problem with a fixed number of new small cells. Afterwards, we extend FNDA into a Deployment Over Existing Network Algorithm (DOENA) to solve the joint optimization problem. The simulation results show that DOENA offers a higher minimum user throughput while requiring less mobile small cells to be deployed than the Deployment optimization based on maximizing sum user throughput.

B Benhabib - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive static sensor network Deployment Strategy for detecting mobile targets
    International Symposium on Safety Security and Rescue Robotics, 2016
    Co-Authors: Zendai Kashino, Julio Vilela, Justin Y Kim, Goldie Nejat, B Benhabib
    Abstract:

    The mobile-target search problem has been, typically, addressed in the literature through the sole use of mobile agents. Recently, however, it has been shown that the use of static-sensor networks could significantly contribute to the likelihood of detecting a mobile target and in a shorter time. In this paper, thus, we propose a novel adaptive and optimal static-sensor network Deployment Strategy to detect un-trackable targets in unstructured environments. The Strategy utilizes a probabilistic target-motion model representative of the demographic group to which the target belongs and realtime location history information to construct a target-location probability distribution function over the search region. The novelty of our Strategy lies in the utilization of a time-varying target-location probability distribution in order to deploy sensors in a manner that is both maximally adaptive and optimal for every Deployment. Network Deployment for a wilderness search and rescue problem is also presented in detail as an example case. Furthermore, numerous factors that may influence the performance of our Deployment Strategy are discussed, including a network coverage comparative study.

  • SSRR - An adaptive static-sensor network Deployment Strategy for detecting mobile targets
    2016 IEEE International Symposium on Safety Security and Rescue Robotics (SSRR), 2016
    Co-Authors: Zendai Kashino, Julio Vilela, Justin Y Kim, Goldie Nejat, B Benhabib
    Abstract:

    The mobile-target search problem has been, typically, addressed in the literature through the sole use of mobile agents. Recently, however, it has been shown that the use of static-sensor networks could significantly contribute to the likelihood of detecting a mobile target and in a shorter time. In this paper, thus, we propose a novel adaptive and optimal static-sensor network Deployment Strategy to detect un-trackable targets in unstructured environments. The Strategy utilizes a probabilistic target-motion model representative of the demographic group to which the target belongs and realtime location history information to construct a target-location probability distribution function over the search region. The novelty of our Strategy lies in the utilization of a time-varying target-location probability distribution in order to deploy sensors in a manner that is both maximally adaptive and optimal for every Deployment. Network Deployment for a wilderness search and rescue problem is also presented in detail as an example case. Furthermore, numerous factors that may influence the performance of our Deployment Strategy are discussed, including a network coverage comparative study.

Yijhong Tsai - One of the best experts on this subject based on the ideXlab platform.

  • node Deployment Strategy for wsn based node sequence localization considering specific paths
    International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2013
    Co-Authors: Chunchieh Hsiao, Yijhong Tsai, Wendian Zheng
    Abstract:

    In wireless sensor network (WSN) applications, information of detection location for sensing changes in the environment is very important. Sequence-based Localization (SBL) is a well-known localization mechanism for WSN that can be deployed quickly and utilized right after Deployment. In our previous study, we have already designed and developed a Deployment Strategy for the sensor nodes that can effectively reduce location error in SBL. In this paper we further consider the condition when the target is used to moving in specific paths in the sensing environment. We can then deploy sensor nodes to optimize the location error along the paths.

  • Node Deployment Strategy for WSN-based node-sequence localization
    2011 Seventh International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2011
    Co-Authors: Chunchieh Hsiao, Yijhong Tsai
    Abstract:

    In this paper we focus on the Deployment Strategy of sensor nodes for Wireless Sensor Network (WSN) for Sequence-based Localization (SBL) algorithm in order to effectively reduce location error. To investigate the effects of locations of deployed sensor nodes on the location error, we first develop a node Deployment analysis toolkit for node-sequence localization. Based on the observation via the toolkit we then develop our node Deployment methodology. When deploying sensor nodes, we discover that in order to reduce location error (1) the standard deviation of the polygon area cut by the perpendicular bisectors of the sensor nodes should be kept as small as possible, (2) certain amount of space should be maintained between the sensor nodes and (3) optimization with the angle between the perpendicular bisectors should be utilized. We use the ns2 network simulator for evaluation, and the ns2 simulation results show that our proposed node Deployment methodology can indeed reduce location error of the SBL algorithm.

Chunchieh Hsiao - One of the best experts on this subject based on the ideXlab platform.

  • node Deployment Strategy for wsn based node sequence localization considering specific paths
    International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2013
    Co-Authors: Chunchieh Hsiao, Yijhong Tsai, Wendian Zheng
    Abstract:

    In wireless sensor network (WSN) applications, information of detection location for sensing changes in the environment is very important. Sequence-based Localization (SBL) is a well-known localization mechanism for WSN that can be deployed quickly and utilized right after Deployment. In our previous study, we have already designed and developed a Deployment Strategy for the sensor nodes that can effectively reduce location error in SBL. In this paper we further consider the condition when the target is used to moving in specific paths in the sensing environment. We can then deploy sensor nodes to optimize the location error along the paths.

  • Node Deployment Strategy for WSN-based node-sequence localization
    2011 Seventh International Conference on Intelligent Sensors Sensor Networks and Information Processing, 2011
    Co-Authors: Chunchieh Hsiao, Yijhong Tsai
    Abstract:

    In this paper we focus on the Deployment Strategy of sensor nodes for Wireless Sensor Network (WSN) for Sequence-based Localization (SBL) algorithm in order to effectively reduce location error. To investigate the effects of locations of deployed sensor nodes on the location error, we first develop a node Deployment analysis toolkit for node-sequence localization. Based on the observation via the toolkit we then develop our node Deployment methodology. When deploying sensor nodes, we discover that in order to reduce location error (1) the standard deviation of the polygon area cut by the perpendicular bisectors of the sensor nodes should be kept as small as possible, (2) certain amount of space should be maintained between the sensor nodes and (3) optimization with the angle between the perpendicular bisectors should be utilized. We use the ns2 network simulator for evaluation, and the ns2 simulation results show that our proposed node Deployment methodology can indeed reduce location error of the SBL algorithm.

Zendai Kashino - One of the best experts on this subject based on the ideXlab platform.

  • an adaptive static sensor network Deployment Strategy for detecting mobile targets
    International Symposium on Safety Security and Rescue Robotics, 2016
    Co-Authors: Zendai Kashino, Julio Vilela, Justin Y Kim, Goldie Nejat, B Benhabib
    Abstract:

    The mobile-target search problem has been, typically, addressed in the literature through the sole use of mobile agents. Recently, however, it has been shown that the use of static-sensor networks could significantly contribute to the likelihood of detecting a mobile target and in a shorter time. In this paper, thus, we propose a novel adaptive and optimal static-sensor network Deployment Strategy to detect un-trackable targets in unstructured environments. The Strategy utilizes a probabilistic target-motion model representative of the demographic group to which the target belongs and realtime location history information to construct a target-location probability distribution function over the search region. The novelty of our Strategy lies in the utilization of a time-varying target-location probability distribution in order to deploy sensors in a manner that is both maximally adaptive and optimal for every Deployment. Network Deployment for a wilderness search and rescue problem is also presented in detail as an example case. Furthermore, numerous factors that may influence the performance of our Deployment Strategy are discussed, including a network coverage comparative study.

  • SSRR - An adaptive static-sensor network Deployment Strategy for detecting mobile targets
    2016 IEEE International Symposium on Safety Security and Rescue Robotics (SSRR), 2016
    Co-Authors: Zendai Kashino, Julio Vilela, Justin Y Kim, Goldie Nejat, B Benhabib
    Abstract:

    The mobile-target search problem has been, typically, addressed in the literature through the sole use of mobile agents. Recently, however, it has been shown that the use of static-sensor networks could significantly contribute to the likelihood of detecting a mobile target and in a shorter time. In this paper, thus, we propose a novel adaptive and optimal static-sensor network Deployment Strategy to detect un-trackable targets in unstructured environments. The Strategy utilizes a probabilistic target-motion model representative of the demographic group to which the target belongs and realtime location history information to construct a target-location probability distribution function over the search region. The novelty of our Strategy lies in the utilization of a time-varying target-location probability distribution in order to deploy sensors in a manner that is both maximally adaptive and optimal for every Deployment. Network Deployment for a wilderness search and rescue problem is also presented in detail as an example case. Furthermore, numerous factors that may influence the performance of our Deployment Strategy are discussed, including a network coverage comparative study.