Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor network...Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.展开更多
针对汽车装配过程特点与管控要点,构建了基于Prism/MVVM(model-viewview model)架构的汽车装配过程管控一体化系统.然后,对系统实现的关键技术进行研究,基于企业服务总线(enterprise service bus,ESB)技术以松散耦合方式实现异构管理系...针对汽车装配过程特点与管控要点,构建了基于Prism/MVVM(model-viewview model)架构的汽车装配过程管控一体化系统.然后,对系统实现的关键技术进行研究,基于企业服务总线(enterprise service bus,ESB)技术以松散耦合方式实现异构管理系统间集成,依据不同作业环境采取自动采集与移动式作业采集感知汽车装配过程,设计可配置图形模型的车辆自动识别(automatic vehicle identification,AVI)跟踪模式以满足不同类别和不同层级监管人员需求,基于采集器、触发器、调度器与控制器实现汽车装配过程中自动路由控制(route control,RC)调度.设计系统集成灵活、模块可插拔、界面与业务可分离设计开发、具有良好的扩展性与维护性.最后,企业案例验证了系统有效性.展开更多
基金supported by the Natural Science Foundation of China (No. 60704046, 60725312)the National High-Tech Research Development Plan(863 plan) of China (No. 2007AA041201)the Natural Science Foundation of Liaoning Province (No. 20092083)
文摘Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.
